BEGIN:VCALENDAR
VERSION:2.0
X-WR-CALNAME:cns2026
X-WR-CALDESC:Event Calendar
METHOD:PUBLISH
CALSCALE:GREGORIAN
PRODID:-//Sched.com CNS*2026 Halifax//EN
X-WR-TIMEZONE:UTC
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260711T111500Z
DTEND:20260711T220000Z
SUMMARY:Registration
DESCRIPTION:\n
CATEGORIES:REGISTRATION/ORGANIZATION
LOCATION:Ballroom Salon\, Halifax\, NS\, Canada
SEQUENCE:0
UID:d0bb8d04a1e90b714dad8ba67cf4e97a
URL:http://cns2026.sched.com/event/d0bb8d04a1e90b714dad8ba67cf4e97a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260711T120000Z
DTEND:20260711T200000Z
SUMMARY:OCNS Board Meeting
DESCRIPTION:\n
CATEGORIES:REGISTRATION/ORGANIZATION
LOCATION:Room 508\, Halifax\, NS\, Canada
SEQUENCE:0
UID:e2c0d677f38c663644671dbd2d3e4c1d
URL:http://cns2026.sched.com/event/e2c0d677f38c663644671dbd2d3e4c1d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260711T120000Z
DTEND:20260711T200000Z
SUMMARY:Building mechanistic biophysical models using NEURON and NetPyNE: from molecules to circuit dynamics to LFP/EEG measures
DESCRIPTION:Understanding the brain requires studying its multiscale interactions\, from molecules to cells to circuits and networks. Although vast experimental datasets are being generated across scales and modalities\, integrating and interpreting this data remains a daunting challenge. This tutorial will highlight recent advances in mechanistic multiscale modeling and how it offers an unparalleled approach to integrate these data and provide insights into brain function and disease. Multiscale models facilitate the interpretation of experimental findings across different brain regions\, brain scales (molecular\, cellular\, circuit\, system)\, brain function (sensory perception\, motor behavior\, learning\, etc)\, recording/imaging modalities (intracellular voltage\, LFP\, EEG\, fMRI\, etc) and disease/disorders (e.g.\, schizophrenia\, epilepsy\, ischemia\, Parkinson's\, etc). As such\, it has a broad appeal to experimental\, clinical and computational neuroscientists\, as well as students and educators.\n\n This tutorial will introduce multiscale modeling using two NIH-funded tools: the NEURON 9.0 simulator (https://www.neuronsimulator.org)\, including the Reaction-Diffusion (RxD) module\, and the NetPyNE tool (https://netpyne.org). The tutorial will combine background\, examples and hands on exercises covering the implementation of models at four key scales:\n\n (1) intracellular dynamics (e.g. calcium buffering\, protein interactions)\,\n\n (2) single neuron electrophysiology (e.g. action potential propagation)\,\n\n(3) neurons in extracellular space (e.g. spreading depression)\, and\n \n(4) neuronal circuits\, including dynamics such as oscillations and simulation of recordings such as local field potentials (LFP) and electroencephalography (EEG).\n\n For circuit simulations\, we will use NetPyNE\, a high-level interface to NEURON supporting both programmatic and GUI specification that facilitates the development\, parallel simulation\, and analysis of biophysically detailed neuronal circuits. We conclude with an example combining all three tools that link intracellular/extracellular molecular dynamics with network spiking activity and LFP/EEG. The tutorial will incorporate the recent substantial developments and new features in both the NEURON and NetPyNE tools.\n\n Relevant Publications:\n \nAwile O\, Kumbhar P\, Cornu N\, Dura-Bernal S\, King JG\, Lupton O\, Magkanaris I\, McDougal RA\,\n Newton AJH\, Pereira F\, Savulescu A\, Carnevale NT\, Lytton WW\, Hines ML\, Schürmann F.\n Modernizing the NEURON Simulator for Sustainability\, Portability\, and Performance. Frontiers in\n Neuroinformatics 10.3389/fninf.2022.884046.\n\n McDougal RA\, Hines ML\, Lytton WW. (2013) Reaction-diffusion in the NEURON simulator.\n https://doi.org/10.3389/fninf.2013.00028\n\n Dura-Bernal S\, Suter B\, Gleeson P\, Cantarelli M\, Quintana A\, Rodriguez F\, Kedziora DJ\, Chadderdon\n GL\, Kerr CC\, Neymotin SA\, McDougal R\, Hines M\, Shepherd GMG\, Lytton WW. (2019) NetPyNE: a\n tool for data-driven multiscale modeling of brain circuits.&nbsp\;eLife 2019\;8:e44494. \n \nDura-Bernal S\, Herrera B\, Lupascu C\, Marsh BM\, Gandolfi D\, Marasco A\, Neymotin SA\, Romani A\,\n Solinas S\, Bazhenov M\, Hay E\, Migliore M\, Reinmann M\, Arkhipov A (2024) Large-scale\n mechanistic models of brain circuits with biophysically- and morphologically-detailed neurons.\n Journal of Neuroscience 2 October 2024\, 44 (40) e1236242024\; DOI:\n 10.1523/JNEUROSCI.1236-24.2024.\n\n
CATEGORIES:TUTORIAL
LOCATION:Room 501\, Halifax\, NS\, Canada
SEQUENCE:0
UID:b743360c7ec4db8eb4f5edf8b97229db
URL:http://cns2026.sched.com/event/b743360c7ec4db8eb4f5edf8b97229db
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260711T120000Z
DTEND:20260711T150000Z
SUMMARY:From Graphs to Foundation Models: Modern Approaches to Neural Population Analysis
DESCRIPTION:The study of neuronal populations through graph and network theory provides powerful insights into brain organizational principles [1]. This half-day tutorial will focus on computational tools and modern learning paradigms for analyzing neuronal activity as networks\, uncovering functional organization\, and learning transferable representations of neural systems across scales and modalities.\n\n The tutorial will feature two main components:\n \n1. Graph Analysis of Neuronal Populations\n \nParticipants will learn to construct and analyze networks from neuronal activity using graph-theoretic approaches. Topics include adjacency matrix construction\, modularity detection\, and metrics such as clustering coefficients\, centrality\, and small-worldness to characterize neuronal communication and information flow [2\,3\,4]. Practical examples will use experimental datasets\, including in vitro spike trains from CL1—demonstrating adaptive\, task-oriented behavior in living neuronal cultures [5\,6]— and example fMRI time series. Common pitfalls in interpreting functional connectivity and network- based analyses will also be discussed [7].\n\n 2. Foundation Models for Neural Data and Brain Networks\n \nThis component will provide a step-by-step introduction to modern machine learning models for neural data\, progressing from sequence-to-sequence formulations to Transformer architectures and contemporary foundation models. Emphasis will be placed on developing intuition and understanding these methods from first principles\, with concrete examples drawn from neural time series and network-structured data. A key example will be BrainSymphony [8]\, illustrating how foundation models can jointly capture neural dynamics and structure for downstream tasks such as classification\, prediction\, and biomarker discovery.\n\n Beyond fMRI\, the tutorial will discuss applications of foundation models across neuroscience\, including causal discovery [9]\, electrophysiology tasks such as spike detection [10] and stimulus– response prediction [11]\, and representation learning [8\,12]. Participants will gain hands-on exposure to Python-based workflows\, alongside discussion of scalability\, inductive biases\, and open challenges in applying foundation models to neural systems.\n\n Attendees will gain practical insight into combining graph-theoretic analysis with foundation models for neural population analysis\, appealing to researchers in brain connectivity\, neural computation\, and representation learning.\n\nReferences\n\n [1] Bullmore\, E.\, & Sporns\, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience\, 10(3)\, 186-198.\n\n [2] Rubinov\, M.\, & Sporns\, O. (2010). Complex network measures of brain connectivity: uses and interpretations. NeuroImage\, 52(3)\, 1059-1069.\n\n [3] Khajehnejad\, M.\, & Habibollahi\, F.\, et al. (2023). On Complex Network Dynamics of an In-Vitro Neuronal System during Rest and Gameplay. In NeurIPS 2023 Workshop on Symmetry and Geometry in Neural Representations.\n\n [4] Khajehnejad\, M.\, Habibollahi\, F.\, Khajehnejad\, A.\, French\, C.\, Kagan\, B. J.\, & Razi\, A. (2024). Graph-Based Representation Learning of Neuronal Dynamics and Behavior. arXiv preprint arXiv:2410.00665.\n\n [5] Kagan\, B. J.\, et al. (2022). In vitro neurons learn and exhibit sentience when embodied in a simulated game-world. Neuron\, 110(23)\, 3952–3969.\n\n [6] Cortical Labs: https://corticallabs.com/\n\n [7] Bastos\, A. M.\, & Schoffelen\, J. M. (2015). A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Frontiers in Systems Neuroscience\, 9\, 175.\n\n [8] Khajehnejad\, M.\, Habibollahi\, F.\, & Razi\, A. (2025). BrainSymphony: A Transformer-Driven Fusion of fMRI Time Series and Structural Connectivity. arXiv preprint arXiv:2506.18314.\n\n [9] Nag\, S.\, & Uludag\, K. (2024). Transformer-aided dynamic causal model for scalable estimation of effective connectivity. Imaging Neuroscience\, 2\, 1-22.\n\n [10] Wei\, F.\, Mo\, J.\, Zhang\, K.\, Shen\, H.\, Nagarajan\, S.\, & Jiang\, F. (2024). Nested deep learning model towards a foundation model for brain signal data. arXiv preprint arXiv:2410.03191.\n\n [11] Wang\, E. Y.\, Fahey\, P. G.\, Ding\, Z.\, Papadopoulos\, S.\, Ponder\, K.\, Weis\, M. A.\, ... & Tolias\, A. S. (2025). Foundation model of neural activity predicts response to new stimulus types. Nature\, 640(8058)\, 470-477.\n\n [12] Dong\, Z.\, Li\, R.\, Wu\, Y.\, Nguyen\, T. T.\, Chong\, J.\, Ji\, F.\, ... & Zhou\, J. H. (2024). Brain-jepa: Brain dynamics foundation model with gradient positioning and spatiotemporal masking. Advances in Neural Information Processing Systems\, 37\, 86048-86073.\n\n
CATEGORIES:TUTORIAL
LOCATION:Room 502\, Halifax\, NS\, Canada
SEQUENCE:0
UID:7d2893e716534a86cc7708834a2a11ca
URL:http://cns2026.sched.com/event/7d2893e716534a86cc7708834a2a11ca
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260711T120000Z
DTEND:20260711T150000Z
SUMMARY:Multiscale modeling with MOOSE and Jardesigner
DESCRIPTION:MOOSE\, the Multiscale Object-Oriented Simulation Environment (https://mooseneuro.org) is a system for modelling multiple scales in neuroscience\, from biochemical pathways with reaction-diffusion systems to detailed biophysical models of single neurons and neuronal networks.\n\n This tutorial will provide participants with a brief overview of MOOSE and its ecosystem. We will start with a walkthrough of MOOSE installation\, then demonstrate how to write Python scripts to setup and simulate simple biochemical and biophysical models. Participants will also learn how to load models defined in standard formats like SBML and NeuroML into MOOSE\, and explore\, modify\, and simulate them using Python.\n\n Finally\, the participants will see a demonstration of Jardesigner\, a new browser-based graphical user interface for MOOSE that allows users to create multiscale models by putting together pre-built components\, simulate them\, and visualize the results with a few clicks.\n\n
CATEGORIES:TUTORIAL
LOCATION:Room 505\, Halifax\, NS\, Canada
SEQUENCE:0
UID:a8289454a568883bcb596ac03ec52660
URL:http://cns2026.sched.com/event/a8289454a568883bcb596ac03ec52660
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260711T131000Z
DTEND:20260711T134000Z
SUMMARY:Coffee break
DESCRIPTION:\n
CATEGORIES:BREAK
LOCATION:Ballroom Salon\, Halifax\, NS\, Canada
SEQUENCE:0
UID:afcd24a10d5622584cfd70791e7671b8
URL:http://cns2026.sched.com/event/afcd24a10d5622584cfd70791e7671b8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260711T160000Z
DTEND:20260711T200000Z
SUMMARY:From single-cell modeling to large-scale network dynamics with NEST Simulator
DESCRIPTION:For more details and materials related to this tutorial\, please see the tutorial website:&nbsp\;https://clinssen.github.io/NEST-workshop/\n\nNEST is an established open-source simulator for spiking neuronal networks that combines detailed biological modeling with high performance and scalability from laptops to HPC systems [1]\, and has supported hundreds of studies\, including a large-scale model of human cortex [2]. In two independent modules\, this tutorial highlights NEST's support for compartmental neuron models and advanced synaptic plasticity.\n\n Compartmental neuron models are a detailed way of describing biological neurons\, capturing their spatially extended morphology as systems of coupled ordinary differential equations. We introduce the recently introduced compartmental modeling feature in NEST\, starting with model construction in NESTML of biologically motivated multi-compartment neurons with active channels and synaptic inputs [4]\, and then create interacting networks composed of compartmental neuron populations. By explicitly constructing compartmental trees\, participants gain transparent and fine-grained control over model structure. We will build a simple ion channel model in NESTML\, and show how it can be compiled\, rewritten\, and extended\, providing a concrete template for user-defined model development. The tutorial demonstrates dendritic computations emerging from explicitly constructed compartmental neurons and networks\, and offers a practical entry point for developing custom compartmental models.\n\nAs an example of advanced plasticity rules in NEST\, we present supervised eligibility propagation\, an online\, biologically inspired learning rule that approximates backpropagation through time [3]. We show how this rule can be used to train functional spiking neural networks to learn a range of tasks\, from which we highlight the classification and generation of handwritten characters. The tutorial covers the full research workflow from model construction and simulation to data analysis. Participants can follow the material hands-on and interactively via the EBRAINS cloud services in the browser without local installation\, and are encouraged to bring an existing EBRAINS account or create one in advance.\n\n[1] https://nest-simulator.readthedocs.org/\n [2] https://github.com/INM-6/microcircuit-PD14-model\n [3] https://nest-simulator.readthedocs.io/en/latest/auto_examples/eprop_plasticity/index.html\n [4] https://nestml.readthedocs.org/
CATEGORIES:TUTORIAL
LOCATION:Room 505\, Halifax\, NS\, Canada
SEQUENCE:0
UID:7c0b06c3e6f7cde04020b95515026fba
URL:http://cns2026.sched.com/event/7c0b06c3e6f7cde04020b95515026fba
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260711T160000Z
DTEND:20260711T200000Z
SUMMARY:Let there be Neulite: A short introduction to a light-weight neuron simulator
DESCRIPTION:Neulite is a light-weight neuron simulator designed for biophysically detailed single-neuron and network models [1]. It specifically targets neuron models from the Allen Cell-Types Database and can execute models described in the Brain Modeling ToolKit (BMTK) with minimum modification.\n\n Neulite is built on the philosophy of a Minimum Viable Product (MVP). Unlike general-purpose simulators that aim for universal functionality\, Neulite maintains an exceptionally concise kernel of approximately 2\,000 lines of C code. This minimalist design allows for extreme performance optimization on specific hardware architectures and provides researchers with direct\, transparent access to the underlying computational logic. By eliminating the complexity of larger engines\, Neulite empowers users to easily modify simulation routines and implement custom features\, ensuring full control over their specific modeling workflows.\n\n This half-day tutorial is divided into two parts. In the first part\, we will provide a general introduction to Neulite\, set up the environment for BMTK and NEURON\, and replicate BMTK Tutorials 1 and 4 using Neulite. The second part features a walk-through of the simulation kernel\; we will examine the source code\, progressing from a passive single-neuron model to a population-scale model.\n\n Attendees are expected to bring a computer equipped with Python\, a C compiler\, and git.\n\n Website:&nbsp\;https://numericalbrain.org/en/neulite/\n\n Reference:\n\n [1] Rin Kuriyama*\, Kaaya Akira*\, Laura Green\, Beatriz Herrera\, Kael Dai\, Mari Iura\, Gilles Gouaillardet\, Asako Terasawa\, Taira Kobayashi\, Jun Igarashi\, Anton Arkhipov\, Tadashi Yamazaki (*: equally contributed). Microscopic-Level Mouse Whole Cortex Simulation Composed of 9 Million Biophysical Neurons and 26 Billion Synapses on the Supercomputer Fugaku. in The International Conference for High Performance Computing\, Networking\, Storage and Analysis (SC ʼ25)\, November 16‒21\, 2025\, St Louis\, MO\, USA. ACM\, New York\, NY\, USA\, 11 pages. doi: 10.1145/3712285.3759819\n\n
CATEGORIES:TUTORIAL
LOCATION:Room 502\, Halifax\, NS\, Canada
SEQUENCE:0
UID:991a55bce59381d8668ad7a3959eebe4
URL:http://cns2026.sched.com/event/991a55bce59381d8668ad7a3959eebe4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260711T180000Z
DTEND:20260711T183000Z
SUMMARY:Coffee break
DESCRIPTION:\n
CATEGORIES:BREAK
LOCATION:Ballroom Salon\, Halifax\, NS\, Canada
SEQUENCE:0
UID:9efe31cfc42a945f9e2617c9f91e4d91
URL:http://cns2026.sched.com/event/9efe31cfc42a945f9e2617c9f91e4d91
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260711T200000Z
DTEND:20260711T202000Z
SUMMARY:Welcome and Announcememts
DESCRIPTION:\n
CATEGORIES:ANNOUNCEMENTS
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:a722e050ca95c341ff2bac9d8ea1e09c
URL:http://cns2026.sched.com/event/a722e050ca95c341ff2bac9d8ea1e09c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260711T202000Z
DTEND:20260711T212000Z
SUMMARY:Keynote 1: Bratislav Misic
DESCRIPTION:\n
CATEGORIES:KEYNOTE
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:e74039b13d7d58f0b8850929de221234
URL:http://cns2026.sched.com/event/e74039b13d7d58f0b8850929de221234
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260711T212000Z
DTEND:20260711T230000Z
SUMMARY:Welcome Reception
DESCRIPTION:\n
CATEGORIES:RECEPTION/PARTY
LOCATION:TBA\, Halifax\, NS\, Canada
SEQUENCE:0
UID:aa4ca2d4fe1130885731440452d2b6e5
URL:http://cns2026.sched.com/event/aa4ca2d4fe1130885731440452d2b6e5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260712T111500Z
DTEND:20260712T200000Z
SUMMARY:Registration
DESCRIPTION:\n
CATEGORIES:REGISTRATION/ORGANIZATION
LOCATION:TBA\, Halifax\, NS\, Canada
SEQUENCE:0
UID:81903398073c5c3b1760fa1b7d20c800
URL:http://cns2026.sched.com/event/81903398073c5c3b1760fa1b7d20c800
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260712T120000Z
DTEND:20260712T121000Z
SUMMARY:Announcements
DESCRIPTION:\n
CATEGORIES:ANNOUNCEMENTS
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:a721444e62b6cb82be795b80800e1fb1
URL:http://cns2026.sched.com/event/a721444e62b6cb82be795b80800e1fb1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260712T121000Z
DTEND:20260712T131000Z
SUMMARY:Keynote 2: Mac Shine\, "The Neurobiological Basis of Consciousness"
DESCRIPTION:Understanding the neural basis of consciousness requires mechanistic accounts that span multiple scales of brain organisation. Yet most existing theoretical frameworks operate at the macroscale\, offering systems-level predictions without prescribing the cellular and circuit-level mechanisms that implement them. Here I argue that the multiscale architecture of the thalamocortical system offers a principled solution to this problem. Drawing on theoretical and computational work from our group\, I show how the core/matrix organisation of the thalamus\, in combination with the nonlinear dendritic integration properties of L5B pyramidal neurons\, generates the conditions necessary for both the global state of consciousness and the specific contents of experience. A biophysical microcircuit model\, extended to a corticothalamic neural mass framework\, reproduces key empirical phenomena across multiple perturbation regimes -&nbsp\;including anaesthesia\, optogenetic manipulation\, and pharmacological intervention - and makes predictions at the macroscale that are consistent with leading theoretical accounts. \n \n
CATEGORIES:KEYNOTE
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:8d072803608ff46b279157afd1702d8a
URL:http://cns2026.sched.com/event/8d072803608ff46b279157afd1702d8a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260712T131000Z
DTEND:20260712T134000Z
SUMMARY:Coffee Break
DESCRIPTION:\n
CATEGORIES:BREAK
LOCATION:Ballroom Salon\, Halifax\, NS\, Canada
SEQUENCE:0
UID:11ba6001a423962abcc4a1a2ed25ea8e
URL:http://cns2026.sched.com/event/11ba6001a423962abcc4a1a2ed25ea8e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260712T134000Z
DTEND:20260712T141000Z
SUMMARY:FO1: The Synapse-Pairing Tradeoff: How Clustering\, Bursts\, and Dendritic Location Enable Robust Plasticity In-Vivo
DESCRIPTION:Dhuruva Priyan Gowri Mariyappan*1\,2\,3\, Nghi V Nguyen2\,3\,4\, Giuseppe Chindemi5\, András Ecker6\, Sabrina Tazerart2\,4\, James Isbister7\, Darshan Mandge7\, Diana E. Mitchell2\,4\, Michael W Reimann7\, Roberto Araya4\,2\, Eilif B Muller2\,3\,4\n1&nbsp\;Department of Computer Science and Operations Research\, Faculty of Arts and Science\, Université de Montréal\, Montréal\, Canada\n2&nbsp\;Centre de Recherche Azrieli du CHU Sainte-Justine\, Montréal\, Canada\n3&nbsp\;Mila Quebec AI Institute\, Montréal\, Canada\n4&nbsp\;Department of Neurosciences\, Faculty of Medicine\, Université de Montréal\, Montréal\, Canada\n5&nbsp\;ETH AI Center\, Zurich\, Switzerland\n6&nbsp\;Cytocast Hungary Kft\, Budapest\, Hungary\n7&nbsp\;Open Brain Institute\, Lausanne\, Switzerland\n*Email: gmdhuruva@gmail.com\n\n\nIntroduction\n​Cortical representations are thought to arise from stable network motifs formed by neuronal assemblies\, with synaptic plasticity between pyramidal cells (PCs) playing a central role in their formation\, competition\, and maintenance. While rules governing such synaptic changes have been well characterized in slice conditions\, their implications for learning in awake behaving animals remain an active area of research. Here we use biophysically detailed simulations with calibrated ion channels\, background synaptic activity\, and calcium-based plasticity rules to investigate mechanisms enabling reliable plasticity in-vivo. We find that spatially clustered activation and burst firing offer robust pathways for LTP under physiological conditions.\n\nMethods\nWe used biophysically detailed simulations of a large-scale in-silico cortical microcircuit of rat somatosensory cortex with a calcium-based plasticity model capturing LTP and Long-Term Depression (LTD) as a function of integrated postsynaptic calcium. We parameterized voltage-gated Na⁺\, K⁺\, Ca²⁺\, and Bk channels throughout the dendritic tree based on experimental data. To reproduce the high-conductance state of awake cortex\, we incorporated stochastic background activity using Dendritic Extra-Excitatory Synapses (DEES) at 1.1 synapses/μm. We investigated clustered plasticity in L2/3 PC and L5-TTPC basal and apical dendrites under both in-vitro and in-vivo-like extracellular calcium concentrations.\n\nResults\nSynchronous activation of ≥11 clustered synapses generates dendritic plateau potentials (≥100 ms) that induce LTP in distal basal dendrites (Fig. 1). We identify a synapse-pairing tradeoff\, where dendrites effectively trade the number of co-activated synapses for pairing repetitions: 16-synapse clusters achieve one-shot learning\, while 8-synapse clusters require 3+ pairings. This tradeoff exhibits spatial gradients: distal dendrites achieve LTP independent of backpropagating action potentials\, while proximal clusters require spike-timing coincidence. When multiple basal clusters coactivate\, summated depolarizations trigger somatic bursts\; both presynaptic and postsynaptic bursts drive robust LTP with all-or-none threshold dynamics.\nDiscussion\nThese findings establish multiple plasticity mechanisms within a single neuron—spatial clustering\, location-dependent learning modes\, and dual burst pathways—providing testable predictions for how cortical circuits implement flexible\, hierarchical learning. Distal dendrites enable unsupervised learning via cluster-based LTP independent of bAPs\, while proximal regions implement supervised learning requiring spike-timing coincidence. Apical dendrites receiving top-down signals can generate bursts or couple with somatic spikes via backpropagation-activated calcium (BAC) firing\, a substrate for top-down plasticity modulation. These mechanisms reveal how dendrites trade synapse number for pairing repetitions under noisy physiological conditions.\n\nFigure 1.&nbsp\;A\, In silico cortical microcircuit. B\, L5-TTPC with magnified cluster showing plasticity for 4 vs 8 co-active synapses. C\, Clustered pre-post pairing (0.5 Hz)\; net potentiation vs synapse number. D\, Spatial learning gradient E\, Synapse-pairing tradeoff heatmap. F\, Basal cluster coactivation triggers somatic burst.​\n\nReferences\n1.&nbsp\;Chindemi\, G.\, Abdellah\, M.\, Amsalem\, O.\, Benavides-Piccione\, R.\, Delattre\, V.\, Doron\, M.\, Ecker\, A.\, Jaquier\, A. T.\, King\, J.\, Kumbhar\, P.\, Monney\, C.\, Perin\, R.\, Rössert\, C.\, Tuncel\, A. M.\, Van Geit\, W.\, DeFelipe\, J.\, Graupner\, M.\, Segev\, I.\, Markram\, H.\, & Muller\, E. B. (2022). A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex.\n2.&nbsp\;Ecker\, A.\, Egas Santander\, D.\, Abdellah\, M.\, Alonso\, J. B.\, Bolaños-Puchet\, S.\, Chindemi\, G.\, Gowri Mariyappan\, D. P.\, Isbister\, J. B.\, Ki
CATEGORIES:ORAL SESSION FEATURED
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:b8acfc2b7dd0ca357e984f82a4e981a5
URL:http://cns2026.sched.com/event/b8acfc2b7dd0ca357e984f82a4e981a5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260712T141000Z
DTEND:20260712T143000Z
SUMMARY:O1: Biologically plausible credit assignment via neuronal frequency multiplexing
DESCRIPTION:\nLi Ji-An1\,2\,3\,&nbsp\;Marcus K. Benna*3\n\n1&nbsp\;Neurosciences Graduate Program\, University of California San Diego\, La Jolla\, CA\, USA\n2&nbsp\;Department of Psychology\, New York University\, New York\, NY\, USA\n3&nbsp\;Department of Neurobiology\, University of California San Diego\, La Jolla\, CA\, USA\n\n*Email: mbenna@ucsd.edu\n\nIntroduction\nBackpropagation has been highly successful for training artificial neural networks. However\, whether the biological brain implements any variant of backpropagation remains unknown. A key challenge concerns the capability of a single biological neuron to simultaneously encode and transmit feedforward predictions and feedback errors with minimal interference. Here\, we propose a neuronal frequency multiplexing framework to address this challenge.\n\nMethods\nEach model neuron has multiple compartments that multiplex signals in the frequency domain. One dendrite acts as a low-pass filter\, and extracts feedforward prediction signals from the low-frequency\, direct-current components of the inputs. Another dendrite acts as a high-pass filter\, and extracts feedback error signals from high-frequency\, oscillatory components of the inputs. The soma integrates both signals\, transmitting them to other neurons through its firing rate\, which consists of a slowly varying prediction component and an oscillatory error component.\n\nResults\nWe demonstrate that this frequency multiplexing algorithm using a simple\, local learning rule closely approximates backpropagation in fully connected networks trained on the MNIST dataset and in convolutional networks trained on the CIFAR-10 dataset\, achieving comparable performance and similar learning speed as a function of the number of training epochs.\n\nDiscussion\nOur framework implements backpropagation-like training of functionally feedforward neural networks using continuously running\, recurrently connected neuronal populations that simultaneously encode and propagate both prediction and error signals with minimal interference. This represents a new solution to the long-standing problem of biologically plausible credit assignment\, suggesting a potential computational role for oscillatory signals in coordinating synaptic plasticity.\n\n
CATEGORIES:ORAL SESSION TALK
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:fdc417d61db239cf0f91d0bf7a8ea230
URL:http://cns2026.sched.com/event/fdc417d61db239cf0f91d0bf7a8ea230
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260712T143000Z
DTEND:20260712T145000Z
SUMMARY:O2: A unifying account of rTMS and rTFUS neurostimulation effects based on calcium-dependent synaptic plasticity theory and an equivalent-energy principle
DESCRIPTION:John D. Griffiths*1\,2\,3\,4\, Kevin Kadak1\,3\, Yupeng Tian1\,5\,6\n\n1Krembil Centre for Neuroinformatics\, Centre for Addiction and Mental Health\, Toronto\, Canada\n2Department of Psychiatry\, University of Toronto\, Canada\n3Institute of Medical Sciences\, University of Toronto\, Canada\n4Institute of Biomedical Engineering\, University of Toronto\, Canada\n5Dept. Mathematics\, University of Toronto\, Canada \n6Fields Institute for Mathematical Sciences\, Toronto\, Canada\n\n\n*Email: john.griffiths@utoronto.ca\n\nIntroduction\nRepetitive transcranial magnetic stimulation (rTMS) and transcranial focused ultrasound stimulation (rTFUS) are noninvasive neuromodulation techniques with established and promising clinical applications\, respectively.Though their primary mechanisms of action differ (electromagnetic vs. acoustic)\,both exert clinically relevant effects through stimulation-induced synaptic plasticity. Despite rich neurophysiological understanding of plasticity\, a validated theoretical framework describing noninvasive neurostimulation-induced plasticity remains to be developed. We present a unified mean-field modelling framework for rTMS- and rTFUS-induced plasticity\, grounded in calcium-dependent plasticity theory embedded in a corticothalamic circuit[1\,2\,3].\n\nMethods\nWe extended the Fung-Robinson calcium-dependent plasticity model [1] by embedding it in a multi-population corticothalamic circuit generating alpha oscillations\, implemented in NFTsim. For rTMS validation\, a within-subject TMS-EEG experiment (N=21\; 5 visits) tested 5 iTBS protocols varying inter-burst frequency and pulses-per-burst\, measuring motor-evoked potentials (MEPs) and resting-state EEG alpha power. For rTFUS\, a novel equivalent-energy principle (Fig.1) scaled continuous FUS burst amplitudes to deliver equivalent energy to corresponding rTMS waveforms\, enabling direct model comparison across modalities. Predictions were compared against published rTFUS motor plasticity data across varying inter-burst frequencies and durations [4\,5].\n\nResults\nWeaker iTBS protocols (3Hz/3PPB\, 5Hz/2PPB) produced paradoxically stronger LTP-like MEP facilitation than standard iTBS (5Hz/3PPB)\, while the strongest protocol (7Hz/3PPB) robustly sign-flipped to LTD. MEP and resting-state alpha power showed a consistent inverse relationship across all protocols\, supporting EEG as a plasticity biomarker outside the motor system. The corticothalamic model reproduced correct MEP directionality in 5/5 protocols and rank-ordering in 4/5\, and captured alpha directionality in 4/5.Applying the equivalent-energy principle to rTFUS\, the same model replicated published cTB-FUS plasticity results and accounted for the LTD/LTP sign-flip in cTB-TMS (40s vs. 80s) but not cTB-FUS\, explained by waveform shape alone [4\,5].\n\nDiscussion\nThese findings support a 'less is more' principle: gentler stimulation paradoxically yields stronger plasticity effects\, with over-stimulation causing sign-reversal to LTD. The consistent MEP-alpha inverse relationship opens the possibility of using scalp EEG as a protocol-agnostic plasticity readout. The equivalent-energy principle provides a principled bridge between rTMS and rTFUS modelling\, enabling the same calcium-dependent corticothalamic framework to account for both modalities without re-parameterization. Together these results establish a foundation for in silico exploration of the largely unmapped rTMS/rTFUS protocol space\, with direct implications for optimizing clinical neuromodulation [2\,3].\n\nFigure 1.&nbsp\;Equivalent energy principle for aligning rTMS and rTFUS plasticity models. rTMS delivers discrete pulse bursts\; rTFUS delivers continuous bursts filtered by the skull interface to sub-1kHz sinusoids. The principle constrains parameters so both modalities deliver equal energy at the same carrier frequency\, enabling unified mean-field modelling of calcium-dependent plasticity.\n​\n\nReferences\nFung & Robinson (2014). Neural field theory of synaptic metaplasticity with applications to theta burst stimulation. J Theor Biol\, 340\, 164–176. https://doi.org/10.1016/j.jtbi.2013.09.021Kadak K\, et al. (2026\, submitted). Less is more: gentle protocols induce stronger facilitatory effects than standard iTBS through calcium-dependent metaplasticity.Tian Y\, et al. (2026\, submitted). Equivalent energy principle and calcium-dependent plasticity theory unify TMS and FUS effects.Zeng K\, et al. (2024). Motor cortex plasticity by theta burst transcranial ultrasound. Ann Neurol\, 91(2)\, 238–252. https://doi.org/10.1002/ana.26294Gamboa OL\, et al. (2010). Reversal of theta burst after-effect with prolonged stimulation. Exp Brain Res\, 204(2)\, 181–187. https://doi.org/10.1007/s00221-010-2293-4\nAcknolwedgments\nWe acknowledge funding from the Krembil Foundation\, Labbatt Foundation\, UofT EMHSeed\, and Fields Institute for Mathematical Sciences\, that supported this work.&nbsp\;
CATEGORIES:ORAL SESSION TALK
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:f19632fcbdf9a83c7669c2e99e53c23c
URL:http://cns2026.sched.com/event/f19632fcbdf9a83c7669c2e99e53c23c
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DTSTAMP:20260708T114850Z
DTSTART:20260712T145000Z
DTEND:20260712T151000Z
SUMMARY:O3: Compartmentalized learning through coupled electrochemical adaptation in cortical pyramidal neurons
DESCRIPTION:Beatriz Barros1\,2\, Raquel Figueiredo1\,2\,&nbsp\;Renato Duarte*1\, 2\n\n\n1Center for Neuroscience and Cell Biology (CNC-UC)\, University of Coimbra\, Portugal\n2Centre for Innovative Biomedicine and Biotechnology (CiBB)\, University of Coimbra\, Portugal\n*Email: renato.duarte@cnc.uc.pt\n\nIntroduction\nA single cortical neuron simultaneously expresses Hebbian STDP proximally and cooperative plasticity distally\, couples excitatory and inhibitory weight changes\, and maintains homeostatic stability across timescales spanning seconds to days. Computational models treat these phenomena separately\, yet the underlying molecular cascades are shared\, shaped by local dendritic morphology and chemical composition. This convergence means that compartment-specific learning rules\, local E/I balance\, and multi-timescale homeostasis are not independent\, but mechanistically coupled through intracellular dynamics. What emerges computationally from this coupling\, and how it reshapes our understanding of single-neuron learning\, remains an open question.\n\nMethods\nWe build on a three-compartment neuron model [2]\, augmented with active\, electrogenic dendritic processes (Fig. 1). Local calcium currents feed a slow dendritic integrator that drives calcium-dependent plasticity [3] at every synapse. Compartment-specific learning emerges naturally: bAP-dominated proximal calcium produces Hebbian STDP\, while VGCC/NMDAR-dominated distal calcium yields cooperative plasticity. Inhibitory synapses read the same calcium with inverted thresholds\, coupling E/I balance without explicit homeostatic targets. We extend this via stargazin phosphorylation [4]\, anchoring AMPAR trafficking\, Kv7.2-mediated intrinsic excitability\, and synaptic scaling in a three-tier cascade spanning seconds to days.\n\nResults\nA single plasticity rule\, operating on compartment-resolved calcium trace\, produces Hebbian STDP proximally (bAP-dominated) and cooperative\, timing-independent plasticity distally (VGCC/NMDAR-dominated)\, matching recent in vivo observations [1]. Shared calcium maintains coupled E/I balance locally\, without explicit homeostatic targets\, and allows accurate stimulus representation from the response to localized perturbations. The stargazin cascade reproduces multiphasic homeostatic dynamics with intrinsic plasticity preceding synaptic scaling. We show that the apparent diversity of cortical plasticity rules is an emergent phenomenon\, a consequence of intracellular dynamics and proceed to investigate its functional consequences.\n\nDiscussion\nCompartment-specific signaling produces qualitatively different learning rules from the same mechanism\, reframing credit assignment in cortical circuits [1] and emphasizing intracellular signaling as a primary locus of learning and memory. Stimulus associations\, selectivity\, and stability emerge from dendritic biophysics and are co-modulated with shared electrochemical substrates. Beyond the biophysical details\, the framework we present here raises broader questions: can local\, compartmentalized balance serve as a natural learning objective? And how does coupling different adaptation mechanisms shape information representation and memorization\, with intracellular dynamics acting as a stack-like memory?\n\nFigure 1. Augmented tripod neuron with compartment-specific electrogenic events and coupled E/I plasticity. (a) Circuit schematic with compartment-resolved receptors and interneuron targeting. (b) NMDA plateaus\, apical Ca²⁺ spikes\, and BAC firing with dendritic calcium transients (insets). (c) Shared calcium couples excitatory and inhibitory weight dynamics\, actively maintaining E/I balance.​References\n[1] Wright\, W. J.\, Hedrick\, N. G.\, & Komiyama\, T. (2025). Distinct synaptic plasticity rules operate across dendritic compartments in vivo during learning. Science\, 388(6744)\, 322-328.\n[2] Quaresima\, A.\, Fitz\, H.\, Duarte\, R.\, van den Broek\, D.\, Hagoort\, P.\, & Petersson\, K. M. (2023). The Tripod neuron: a minimal structural reduction of the dendritic tree. The Journal of Physiology\, 601(15)\, 3265-3295.\n[3] Graupner\, M. & Brunel\, N. (2012). Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern\, rate\, and dendritic location. PNAS\, 109(10)\, 3991-3996.\n[4] Rodrigues\, M. V. et al. (2024). Type I TARPs regulate Kv7.2 potassium channels and susceptibility to seizures. bioRxiv\, 2024.08.09.607194.\n\nAcknowledgments\nThis work was supported by national funds through FCT—Foundation for Science and Technology\, I.P.\, under the project HetSyn (2023.13758.PEX).\n\n
CATEGORIES:ORAL SESSION TALK
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:5e3e13a0f6aea7b70ab567a483796ecf
URL:http://cns2026.sched.com/event/5e3e13a0f6aea7b70ab567a483796ecf
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BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260712T151000Z
DTEND:20260712T153000Z
SUMMARY:O4: Rewarding Control: Feedback-Phase-Dependent 2Hz Medial Frontal Transcranial Alternating Current Stimulation Shifts the Expected Value of Control
DESCRIPTION:Authors: Robert Louis Treuting1\, Eric Rawls*1\,2\nAffiliations:&nbsp\;1Department of Psychology\, University of North Carolina Wilmington\, Wilmington\, NC\, USA\,&nbsp\;2Department of Psychiatry and Behavioral Sciences\, University of Minnesota Twin Cities\, Minneapolis\, MN\, USA\n*Email: rawlse@uncw.edu\n\nIntroduction\nMedial frontal delta activity is a plausible control signal because the Reward Positivity (RewP) tracks outcome evaluation and prediction errors\, and our prior simultaneous EEG-fMRI work localized key signed prediction-error contributions of the RewP to medial frontal cortex [1\,2]. Here we tested whether 2 Hz transcranial alternating current stimulation (tACS) targeting this generator changes motivated cognitive control. We predicted that stimulation would alter latent decision parameters in an Expected Value of Control Stroop rather than merely speeding responses [2\,3].\n\nMethods\nSeven healthy participants completed a reward×efficacy Stroop with congruent/incongruent targets across sham and active 2 Hz medial frontal tACS sessions. Accuracy was analyzed with binomial generalized linear mixed models and reaction time with Gamma models. We then fit a hierarchical Wiener diffusion model in brms to decompose behavior into drift rate (mu)\, boundary separation (bs)\, and nondecision time (ndt). In a second model restricted to active trials\, sine and cosine terms indexed the phase of stimulation at the prior trial’s feedback.\n\nResults\nActive stimulation improved accuracy (χ²(1)=8.76\, p=0.003) without a main reaction-time benefit\, arguing against nonspecific speeding. In the diffusion model\, stimulation shifted all three latent components: drift\, boundary\, and nondecision time. Posterior contrasts showed robust drift benefits throughout incongruent trials and selective benefits in congruent trials when reward or efficacy was low (Fig. 1).&nbsp\;In active trials\, prior-feedback phase predicted subsequent drift (feed_cos=0.39 [0.19\, 0.59]\; feed_sin=-0.26 [-0.48\, -0.05])\, consistent with causal modulation of a RewP-linked medial frontal mechanism for updating expected value of control from recent outcomes [1\,2].\n\nDiscussion\nThese results suggest that medial frontal delta stimulation does not simply energize behavior. Instead\, it changes how outcome information is converted into the next trial’s control state. Computationally\, active 2 Hz tACS improved evidence accumulation while also reshaping caution and peripheral processing.\n\nFigure 1.&nbsp\;Active minus sham posterior contrasts from the hierarchical Wiener diffusion model. Top panels show drift-rate changes across reward and efficacy\, separated by congruency. Bottom panels show boundary-separation and nondecision-time changes across reward. Points indicate posterior medians\; ribbons show 95% highest posterior density intervals.​\n\nReferences\n[1] Rawls\, E.\, Demro\, C.\, Teich\, C. D.\, Zhang\, J.\, Wang\, A.\, Heilbronner\, S. R.\, Mueller\, B. A.\, Sponheim\, S. R.\, & MacDonald\, A. W.\, III. (2026). The search for RewP: Dissociating cortical generators of electrophysiological signed and unsigned prediction errors using simultaneous EEG-fMRI [Manuscript in preparation].\n[2] Frömer\, R.\, Lin\, H.\, Dean Wolf\, C. K.\, Inzlicht\, M.\, & Shenhav\, A. (2021). Expectations of reward and efficacy guide cognitive control allocation. Nature Communications\, 12\, 1030.\n[3] Ratcliff\, R.\, & McKoon\, G. (2008). The diffusion decision model. Neural Computation\, 20(4)\, 873–922.\n\nAcknowledgments\nWe thank the Brain\, Data\, and Causality Lab and our University of Minnesota collaborators for the prior simultaneous EEG-fMRI work that motivated this study\, and for foundational discussion of RewP source modeling and expected value of control.\n\n
CATEGORIES:ORAL SESSION TALK
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:fb9a6e60fff2762b4c999d4b766b6267
URL:http://cns2026.sched.com/event/fb9a6e60fff2762b4c999d4b766b6267
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DTSTAMP:20260708T114850Z
DTSTART:20260712T153000Z
DTEND:20260712T170000Z
SUMMARY:Program Commitee Meeting
DESCRIPTION:\n
CATEGORIES:REGISTRATION/ORGANIZATION
LOCATION:TBA\, Halifax\, NS\, Canada
SEQUENCE:0
UID:3d2c7c5e3dda697e8cec23d72082047c
URL:http://cns2026.sched.com/event/3d2c7c5e3dda697e8cec23d72082047c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260712T170000Z
DTEND:20260712T173000Z
SUMMARY:FO2: Norepinephrine Restores Cortical Dynamics and Enables Machine Learning–Based Severity Mapping in a Multiscale Model of Parkinson’s Disease
DESCRIPTION:Jeeyune Jung*1\,3\, &nbsp\; Adam Newton1\,3\, &nbsp\;Donald Doherty1\,3\, &nbsp\;Hong-Yuan Chu2\,3\, &nbsp\;Samuel Neymotin4 \, William Lytton1\,3\n1.&nbsp\;Department of Physiology and Pharmacology\, SUNY Downstate Health Sciences University\, Brooklyn\, NY\, USA&nbsp\;\n2.&nbsp\;Department of Pharmacology and Physiology\, Georgetown University Medical Center\, Washington\, DC\, USA&nbsp\;\n3.&nbsp\;Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network\, Chevy Chase\, MD\, USA\n4&nbsp\;Center for Biomedical Imaging & Neuromodulation\, Nathan Kline Institute\,&nbsp\;Orangeburg\, NY\, USA\n*Email: jungj68@neurosim.downstate.edu\n\n\nIntroduction\nParkinson’s disease (PD) involves not only basal ganglia dopamine loss but also cortical dysfunction\, including excessive beta synchronization\, abnormal beta–gamma coupling\, altered bursting\, and impaired corticospinal recruitment. Early locus coeruleus degeneration may reduce cortical norepinephrine (NE)\, disrupting cell-type-specific gain control in pyramidal tract (PT) and intratelencephalic (IT) neurons. We tested whether experimentally constrained NE modulation restores cortical excitability and network dynamics in an advanced MitoPark motor cortex model and whether NE-sensitive cortical biomarkers support severity mapping and prediction of dopamine-therapy response.\n\n\n\n\nMethods\nWhole-cell patch-clamp recordings from Layer 5 PT and IT neurons quantified NE (10 µM)-induced changes in firing–current relationships. Conductance-based single-cell models were fit to baseline and NE responses and embedded in a biophysically detailed laminar M1 network model in NEURON/NetPyNE. Parkinsonian simulations incorporated reduced PT5B intrinsic excitability and reduced thalamocortical drive\, with disease-stage-dependent NE scaling from PK/PD modeling. From the resulting simulations\, we extracted cortical biomarkers including PT5B firing\, IT5B beta-synchronized bursting\, IT/PT imbalance\, beta power\, beta-burst duration\, beta–high gamma phase-amplitude coupling\, and avalanche slope\, which were then used for severity mapping.&nbsp\;\n\nResults\nNE exerted opposite intrinsic effects across Layer 5 pyramidal subtypes: PT firing increased\, whereas IT repetitive firing decreased. In the Parkinsonian network\, NE-dependent conductance changes partially rescued pathological dynamics: PT5B firing increased by ~60–70%\, IT5B bursting declined\, the IT/PT activity ratio shifted toward control-like values\, and pathological beta–high gamma phase-amplitude coupling decreased by ~40%. Across the biomarker set\, NE shifted cortical dynamics toward the control regime. In the machine-learning framework\, greater deviation from control tracked greater disease severity and predicted progressively shorter and weaker levodopa benefit as NE support declined.\nDiscussion\nThese findings identify NE-sensitive intrinsic gain control as a mechanistic bridge between single-cell excitability and pathological cortical state in PD. Loss of noradrenergic modulation may directly contribute to corticospinal under-recruitment\, hypersynchronous beta activity\, and broader cortical biomarker abnormalities\, whereas restoring NE-dependent PT/IT balance may complement dopamine-based therapy. This multiscale framework links cellular mechanisms\, network dysfunction\, severity mapping\, and predicted treatment response\, positioning noradrenergic modulation as a promising strategy for advanced PD.\n\nReferences\n1. Dura-Bernal\, S.\, et al. (2023). Multiscale model of primary motor cortex circuits predicts in vivo cell-type-specific\, behavioral state-dependent dynamics. Cell Reports.\n2. Chu\, H. Y.\, et al. (2024). Dysfunction of motor cortices in Parkinson’s disease. Cerebral Cortex.\n3. Doherty\, D. W.\, et al. (2025). Enhanced beta power emerges from simulated parkinsonian primary motor cortex. npj Parkinson’s Disease.\n\nAcknowledgments\nThis work was supported by the Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network. We thank colleagues for providing experimental data used to constrain the model. Computational resources were provided by SUNY Downstate Health Sciences University.\n\n\n\n\n
CATEGORIES:ORAL SESSION FEATURED
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:dd031633beed5ebbef5a7188943730a1
URL:http://cns2026.sched.com/event/dd031633beed5ebbef5a7188943730a1
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DTSTAMP:20260708T114850Z
DTSTART:20260712T173000Z
DTEND:20260712T175000Z
SUMMARY:O5: Impact of EAAT2 Dysfunction on AMPA/NMDA-Mediated Excitability in Neuronal Activity
DESCRIPTION:Hannah van Susteren1\,*\, Guillaume Girier2\,*\, Michel J.A.M. van Putten3\,4&nbsp\;\, Jaroslav Hlinka2\, Helmut Schmidt2\, Hil G.E. Meijer1\n\n\n1 Department of Applied Mathematics\, University of Twente\, Enschede\, the Netherlands\n2 Institute of Computer Science\, Czech Academy of Sciences\, Prague\, Czech Republic\n3 Department of Neurology and Clinical Neurophysiology\, University of Twente\, Enschede\, the Netherlands\n4 Medisch Spectrum Twente\, Enschede\, the Netherlands\n* These authors contributed equally to this work.\n\n\nEmail: h.vansusteren@utwente.nl\n\n\nIntroduction\nEpilepsy is among the most prevalent neurological disorders. The astrocytic excitatory amino acid transporter (EAAT2) plays a key role in regulating excitability\, by controlling extracellular glutamate levels and glutamate receptor activation [1\,2]. Reduced EAAT2 expression has been reported in several epilepsy patients [1\,3]. However\, the impact of neuron-astrocyte interactions on hyperexcitability on single cell level is underexplored. We developed a biophysical model of a presynaptic neuron and astrocyte to explore the relation between astrocytic EAAT2-mediated glutamate clearance\, presynaptic glutamate receptors and bursting activity.\n\nMethods\nWe build on our previous work [4\,5]\, where we consider a presynaptic neuron and an astrocyte in a finite extracellular space (ECS). This model describes sodium\, potassium\, chloride dynamics as well as calcium-dependent exocytosis and glutamate-glutamine (GG) recycling. For this study\, we add a potassium bath with diffusion to the ECS to induce neuronal bursting (Fig. 1A). Additionally\, we implement the presynaptic glutamate receptors AMPA and NMDA\, which are important in regulating hyperexcitability. Lastly\, we study the impact of the antiseizure drugs perampanel and memantine by simulating the effect of these AMPA and NMDA receptor antagonists.\n\nResults\nWe induce neuronal bursting by increasing extracellular potassium in the bath. We first examine how AMPA and NMDA permeabilities affect burst frequency (Fig. 1C)\, where frequency refers to spike frequency during the last burst or during tonic firing\, to fit the NMDA/AMPA ratio to experimental data [6]. Higher permeabilities increase neuronal firing and intracellular calcium\, triggering a feedback loop that enhances neuronal glutamate release. Reducing EAAT permeability raises burst frequency and induces tonic firing (Fig. 1B). Finally\, AMPA and NMDA antagonists\, perampanel and memantine [7]\, reduce firing despite elevated extracellular glutamate\, with perampanel showing a more significant reduction in firing frequency (Fig. 1D).&nbsp\;\n\nDiscussion\nOur results show that reduced EAAT expression\, as observed in several epilepsy patients\, results in increased extracellular glutamate and overstimulation of excitatory glutamate receptors. Furthermore\, we show that the AMPA and NMDA receptor permeabilities affect burst frequency. Receptor antagonists such as perampanel and memantine are able to reduce firing. In conclusion\, our detailed neuron–astrocyte model provides insight into the effects of reduced EAAT expression and receptor antagonists on hyperexcitability.\n\nFigure 1.&nbsp\;A: Three-compartment model illustrating the GG-cycle during EAAT2 knockout. B: The membrane potential\, spike frequency f and ECS glutamate at different EAAT2 permeabilities. C: Spike frequency within bursts as a function of NMDA and AMPA receptor permeability. &nbsp\;D: Neuronal activity at fixed EAAT2 permeability (PEAAT=0.15 * 103 &nbsp\;µm3/ms) under antagonist conditions.\n\nReferences\n[1] Green\, J. L.\, dos Santos\, W. F.\, & Fontana\, A. C. K. (2021). Biochemical Pharmacology\, 10.1016/j.bcp.2021.114786\n[2] Scimemi\, A.\, Tian\, H.\, & Diamond\, J. S. (2009). The Journal of Neuroscience\, 10.1523/JNEUROSCI.4845-09.2009\n[3] Barker-Haliski\, M.\, & White\, H. (2015). Cold Spring Harbor perspectives in medicine\, 10.1101/cshperspect.a022863\n[4] van Susteren\, H.\, Rose\, C. R.\, van Putten\, M. J.\, & Meijer\, H. G. (2025). bioRxiv\, 10.1101/2025.11.10.687543\n[5] Kalia\, M.\, et al. (2021). &nbsp\;PLOS Computational Biology\, 10.1371/journal.pcbi.1009019\n[6] Watt\, A. J.\, Sjöström\, P. J.\, Häusser\, M.\, Nelson\, S. B.\, & Turrigiano\, G. G. (2004). &nbsp\;Nature neuroscience\, 10.1038/nn1220\n[7] Chen\, T.-S.\, Huang\, T.-H.\, Lai\, M.-C.\, & Huang\, C.-W. (2023). &nbsp\;Biomedicines\, 10.3390/biomedicines11030783\n\nAcknowledgments\nHVS\, HGEM\, MJAMVP funded from the DFG\, FOR2795 ‘Synapses under stress’ to CRR (Prof. Dr. Christine R. Rose) (Ro2327/13-2 and 14-2).\nGG\, HS\, and JH were supported by the ERDF-Project Brain dynamics\, No. CZ.02.01.01/00/22\_008/0004643\, a Lumina-Quaeruntur fellowship (LQ100302301)\, and the long-term strategic development financing of the Institute of Computer Science (RVO:67985807).\n\n\n
CATEGORIES:ORAL SESSION TALK
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:b8cf9221fc0fb4476c2cf205e9e16595
URL:http://cns2026.sched.com/event/b8cf9221fc0fb4476c2cf205e9e16595
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DTSTAMP:20260708T114850Z
DTSTART:20260712T175000Z
DTEND:20260712T181000Z
SUMMARY:O6: Multiscale modeling of neural markers for adaptive deep brain stimulation in Parkinson’s Disease
DESCRIPTION:\nAlberto Mazzoni1\,2\,*\,&nbsp\;Federico Fattorini1\,2\, Nicolò Meneghetti1\,2\n\n\n1The Biorobotics Institute\, Scuola Superiore Sant’Anna\, Pisa\, Italy\n2Department of Excellence for Robotics and AI\, Scuola Superiore Sant’Anna\, Pisa\, Italy\n\n\n*Email: alberto.mazzoni@santannapisa.it\n\n\nIntroduction\nParkinson’s disease (PD) is a common neurodegenerative disorder causing severe impairments. Drug-resistant patients are treated with Deep Brain Stimulation (DBS) of the basal ganglia (BG)\, with current efforts focused on adaptive DBS. The most relevant biomarkers for adaptive DBS is the power in the beta ([12\, 30] Hz) and gamma ([30\, 100] Hz) range\, yet the mechanisms underlying these rhythms remain unclear. Computational models can provide mechanistic insights into pathophysiology and test new stimulation treatments. Using spiking and morphological models\, we show how beta and gamma resonances emerge from BG interactions\, how DBS reshapes these dynamics\, and how they are reflected in subthalamic nucleus local field potentials (LFPs).\n\nMethods\nWe implemented a spiking model of the basal ganglia with six neuronal populations: three within the striatum\, two within the external globus pallidus\, and the subthalamic nucleus (STN) (Fig. 1A) [1]. Dopamine depletion was simulated by modulating striatal inputs. We dissected the network mechanisms underlying beta and gamma resonances and we simulated STN DBS\, considering short-term synaptic plasticity (Fig. 1B) [2]. To simulate the signals recorded by DBS electrodes we developed a population of morphological STN neurons model and computed LFPs associated with network activity through volume conduction theory (Fig. 1C).\n\nResults\nWe show how beta oscillations arise from two independent loops in the BG model that strongly synchronize when dopamine is depleted [1]. STN DBS disrupts these oscillations\, although without synaptic plasticity it requires unrealistically low stimulation levels (Fig 1B left). The model also supports the hypothesis that gamma-range stimulation can be as effective as the clinical standard of ~130 Hz used in PD (Fig. 1B right) [2]. Gamma oscillations emerge through recurrent inhibition in pallidal and striatal populations. Different from cortical ones\, STN LFPs are largely noise-dominated due to weak correlations and symmetric neuronal morphology\, becoming informative only when strong beta synchronization is present.\n\nDiscussion\nWe characterized in a spiking model of BG beta and gamma rhythmogenesis and their alteration due to PD-related dopamine depletion and DBS. Moreover\, we investigated the origin of STN LFPs driving adaptive DBS\, by integrating spiking and morphological modeling. Overall\, we provide a multiscale framework for better understanding Parkinsonian dynamics and DBS mechanisms\, showing how network modeling can clarify treatment mechanisms and guide improved stimulation strategies.\n\nFigure 1.&nbsp\;A) Spiking network model of the basal ganglia. &nbsp\;B) Left: efficacy of STN DBS as a function of the fraction of stimulated neurons\, with and without short-term plasticity (STP). Right: effect of stimulation frequencies on beta spectral power. &nbsp\;C) Top: STN population and morphological neuron model receiving cortical and pallidal inputs. Bottom: simulated and recorded local field potentials (LFPs).​\n\nReferences\n\n1. Ortone\, A.\, Vergani\, A. A.\, Ahmadipour\, M.\, Mannella\, R.\, & Mazzoni\, A. (2023). Dopamine depletion leads to pathological synchronization of distinct basal ganglia loops in the beta band. PLOS Computational Biology\, 19(4)\, 1–31. https://doi.org/10.1371/journal.pcbi.1010645\n2. Ahmadipour\, M.\, Fattorini\, F.\, Meneghetti\, N.\, & Mazzoni\, A. (2026). In silico model of basal ganglia deep brain stimulation in Parkinson’s disease captures range of effective parameters for pathological beta power suppression. PLOS Computational Biology\, 22(2)\, e1013280. https://doi.org/10.1371/journal.pcbi.1013280\nAcknowledgments\nThis work was supported by the Italian Ministry of University and Research\, under the complementary actions to the NRRP ‘Fit4MedRob - Fit for Medical Robotics’ Grant (# PNC0000007).\n\n\n
CATEGORIES:ORAL SESSION TALK
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:0184e69fe91e1706b209062ccae46fe8
URL:http://cns2026.sched.com/event/0184e69fe91e1706b209062ccae46fe8
END:VEVENT
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DTSTAMP:20260708T114850Z
DTSTART:20260712T181000Z
DTEND:20260712T183000Z
SUMMARY:O7: Decreased alpha/theta temporal ExSEnt of left prefrontal cortex: a robust biomarker of dementia
DESCRIPTION:Sara Kamali*1\, Fabiano Baroni1\, Pablo Varona1\n\n1Department of Computer Engineering\, Autonomous University of Madrid\, Madrid\, Spain\n\n*Email: sara.kamali@uam.es\nIntroduction\nDementia involves progressive cognitive decline\, with Alzheimer’s disease (AD) and frontotemporal dementia (FTD) as major subtypes. Electroencephalography (EEG) provides a noninvasive and accessible measure of brain dynamics and able to capture pathological slowing\, often reflected by increased theta-to-alpha power ratio (TAR) [1]. Reduced signal complexity has also been reported in dementia. We tested whether Extrema-Segmented Entropy (ExSEnt)\, which separates temporal and amplitude irregularity\, improves discrimination and interpretability beyond conventional electroencephalography biomarkers [2].\n\nMethods\nWe analyzed resting-state EEG from 88 subjects\, including 36 with AD\, 23 with FTD\, and 29 controls [3]. After preprocessing and independent component analysis\, we classified brain sources into four clusters: right and left prefrontal cortices (R/LPFC) and right and left visual associations (R/LVA). Then\, we computed a set of classical spectral and complexity features. We also measured the ExSEnt metrics\, which quantifies the entropy of extrema-based segment durations\, amplitudes\, and their pair. To find the stable features for dementia detection\, we performed stability selection with elastic-net logistic regression and nested leave-one-subject-out validation [4].\n\nResults\nExSEnt improved discrimination mainly in the LPFC\, where balanced accuracy increased from 71.5% to 82.6%. In that region\, ExSEnt features showed high selection stability and replaced conventional measures as dominant predictors in 3 out of 4 brain areas. The most informative variables were temporal and joint temporal-amplitude entropy measures in alpha and theta bands\, with negative coefficients indicating reduced irregularity and reduced dynamical variability in dementia. Other regions showed weaker or inconsistent gains\, suggesting a localized effect rather than a global one.&nbsp\;\n\nDiscussion\nExSEnt provides a compact and interpretable measure for EEG-based dementia classification. The results suggest that dementia is associated not only with spectral slowing but also with reduced diversity of extrema timing and temporal-amplitude variability in LPFC dynamics. Because ExSEnt is explainable by design\, when combined with stability selection and sparse linear classification\, it yields robust biomarkers with direct physiological interpretation. These findings support ExSEnt as a promising candidate for explainable dementia screening and motivate future validation against cognitive severity measures.\n\nReferences\n[1] Partanen\, J. V.\, Soininen\, H.\, & Riekkinen\, P. J. (1986). Does an ACTH derivative (Org 2766) prevent deterioration of EEG in Alzheimer's disease?.&nbsp\;Electroencephalography and clinical neurophysiology\,&nbsp\;63(6)\, 547-551.\n[2] Kamali\, S.\, Baroni\, F.\, & Varona\, P. (2025). ExSEnt: Extrema-Segmented Entropy Analysis of Time Series.&nbsp\;arXiv preprint arXiv:2509.07751.\n[3] Miltiadous\, A.\, Tzimourta\, K. D.\, Afrantou\, T.\, Ioannidis\, P.\, Grigoriadis\, N.\, Tsalikakis\, D. G.\, ... & Tzallas\, A. T. (2023). A dataset of scalp EEG recordings of Alzheimer’s disease\, frontotemporal dementia and healthy subjects from routine EEG.&nbsp\;Data\,&nbsp\;8(6)\, 95.\n[4] Meinshausen\, N.\, & Bühlmann\, P. (2010). Stability selection.&nbsp\;Journal of the Royal Statistical Society Series B: Statistical Methodology\,&nbsp\;72(4)\, 417-473.\nAcknowledgments\nFunded by PID2024-155923NB-I00 and CPP2023-010818.
CATEGORIES:ORAL SESSION TALK
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:7434d472178d9fe0b132bc956d0896fa
URL:http://cns2026.sched.com/event/7434d472178d9fe0b132bc956d0896fa
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SUMMARY:O8: Frequency-dependent modulation of cortical traveling waves during general anesthesia
DESCRIPTION:Duan Li*\, Anthony G. Hudetz\n\nCenter for Consciousness Science\, Department of Anesthesiology\, University of Michigan\, Ann Arbor\, MI \n\n*Email: liduan@umich.edu\nIntroduction\nGeneral anesthetics profoundly reshape neuronal activity and functional interactions\, yet how they alter the spatiotemporal organization of the cortex is incompletely understood. Traveling waves provide a framework for examining coordinated neural activity across the cortex\, but their modulation by anesthesia remains underexplored. Recently we showed that cortical dynamics under anesthesia exhibit spontaneous transitions among discrete states\, including a paradoxical state characterized by low delta power and high complexity in deep anesthesia[1]. To investigate how traveling waves vary across cortical states\, we recorded hemispheric ECoG at multiple anesthetic depths and analyzed wave dynamics as a function of cortical state and frequency.\n\nMethods\nECoG was recorded from the right hemisphere with chronically implanted 32-site flexible polymer arrays (4×8 grid) at four desflurane concentrations (6-0%) for 1 h each. Cortical states were identified using PCA of power spectrograms followed by density-based clustering across concentrations. Within each state\, traveling wave episodes were detected based on stabile spatial phase gradient patterns [2] between consecutive time points [3] in the delta\, theta\, and gamma bands. Wave occurrence and pattern richness were quantified\, the latter defined as the entropy of SVD eigenvalues of the episode similarity matrix. Plane waves were identified and further classified as feedforward (posterior-to-anterior) or feedback (anterior-to-posterior).\n\nResults\nSeven states were identified. S1-S5 broadly tracked anesthetic depth but were not tied to a specific level. S6 corresponded to burst suppression\, and S7 indicated a paradoxical state mostly during deep anesthesia. In S1\, theta waves were mainly feedback-directed\, whereas gamma were feedforward-directed. As anesthesia deepened\, delta waves became more frequent but showed reduced pattern diversity. In contrast\, the occurrence and diversity of theta and gamma patterns were largely preserved. Although gamma feedforward dominance persisted\, theta feedback dominance was suppressed during deep anesthesia. In S7\, reductions in delta complexity and theta organization were only partially reversed\, despite reduced delta power and wake-like complexity.\n\nDiscussion\nGeneral anesthesia differentially modulates cortical traveling waves across frequency bands. Loss of theta feedback dominance paired with preserved gamma feedforward propagation suggests disrupted top-down integration despite maintained bottom-up information flow. Reduced diversity of delta waves during deep anesthesia suggests a shift toward more stereotyped\, low-information dynamics. Importantly\, traveling waves do not fully recover in the paradoxical state suggesting that spectrally activated deep anesthetic states lack coordinated spatiotemporal organization necessary for conscious processing. These findings suggest that traveling wave dynamics provide complementary insight into brain state dynamics beyond spectral power or complexity.\n\nReferences\n1. Li\, D.\, & Hudetz\, A. G. (2025). Dynamic electrocortical states and paradoxical complexity during desflurane anesthesia. bioRxiv.&nbsp\;https://doi.org/10.1101/2025.10.13.682019\n2. Muller\, L.\, Piantoni\, G.\, Koller\, D.\, Cash\, S. S.\, Halgren\, E.\, & Sejnowski\, T. J. (2016). Rotating waves during human sleep spindles organize global patterns of activity that repeat precisely through the night. eLife\, 5\, e17267.&nbsp\;https://doi.org/10.7554/eLife.17267\n3. Das\, A.\, Zabeh\, E.\, Ermentrout\, B.\, & Jacobs\, J. (2024). Planar\, spiral\, and concentric traveling waves distinguish cognitive states in human memory. bioRxiv.&nbsp\;https://doi.org/10.1101/2024.01.26.577456\n\nAcknowledgments\nResearch was supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number R01-GM056398 and the Center for Consciousness Science\, Department of Anesthesiology\, University of Michigan Medical School\, Ann Arbor\, Michigan\, USA. The authors express their gratitude to Dr. Shiyong Wang for his assistance in performing the experiments.\n\n
CATEGORIES:ORAL SESSION TALK
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:cfddc0232eeda418329a9150d02ff5d1
URL:http://cns2026.sched.com/event/cfddc0232eeda418329a9150d02ff5d1
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DTSTART:20260712T185000Z
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SUMMARY:Coffee break
DESCRIPTION:\n
CATEGORIES:BREAK
LOCATION:Ballroom Salon\, Halifax\, NS\, Canada
SEQUENCE:0
UID:7c2c019cda154ee3a5d18af8021a13b1
URL:http://cns2026.sched.com/event/7c2c019cda154ee3a5d18af8021a13b1
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DTSTART:20260712T192000Z
DTEND:20260712T212000Z
SUMMARY:Poster Session 1
DESCRIPTION:\n
CATEGORIES:POSTER SESSION
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:6c3ec110b84b1f34e177088839196a5c
URL:http://cns2026.sched.com/event/6c3ec110b84b1f34e177088839196a5c
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SUMMARY:P001: Blind identification of sleep spindles through trispectral modulation analysis
DESCRIPTION:Introduction\nSleep spindles —oscillatory bursts of 12-16 Hz associated with non-REM (NREM) sleep— are a key marker of sleep-related neuroplasticity. The gold standard for sleep spindle detection is commonly taken to be visual inspection by a trained sleep specialist. No blind\, purely data-driven\, methods have yet been demonstrated that are capable of identifying spectral and temporal properties of spindles independently of human judgment. Because inter-rater agreement among experts is modest\, the lack of an objective criterion for identifying spindles leaves many open questions about their true prevalence and nature. Here we describe strictly data-driven identification and detection of spindles using information in the trispectrum.\n\nMethods\nA subdomain of the trispectrum was used to identify the presence and characteristics of modulated carrier oscillations and their envelopes by way of an interpretable representation (modulogram) [Kovach et al.\, 2026]. A decomposition of the trispectrum (HOSD) [Kovach and Howard\, 2019] allowed separation of modulated oscillations from each other and background noise\, and their characterization by a recovered feature waveform\, conveying average spectral and temporal characteristic of oscillatory bursts. These methods were applied towards identifying spindles in two open databases containing polysomnography samples and expert annotation of spindles: MASS [Lacourse et al.\, 2020] (N=100) and DREAMS [Devuyst et al.\, 2011] (N=8).\n\nResults\n\n\n1)&nbsp\;Tricoherence between 11Hz and 16 Hz (FDR Q ≪ 0.05) provided a highly robust and specific correlate of sleep spindles in every sample.\n2) Oscillatory bursts identified with HOSD agreed well with expert spindle annotation (median AUC &gt\; 0.85)\, albeit at a lower amplitude threshold resulting in many more detections.\n3)&nbsp\;A high proportion of these additional detections were validated as true positives by 4 blinded sleep specialists.\n4) N2 is distinguished by high-amplitude (&gt\; 12 dB) spindles while low amplitude oscillatory bursts in the spindle band are prevalent in all NREM stages.\n5) HOSD feature identification reveals descending frequency ( median −3.9 Hz/s\, IQR 1.6) as a characteristic of spindle waveforms (signed rank test\, P≪0.001).\n\n\n\nDiscussion\n\n\nHOSD applied to the trispectral modulogram provides a reliable means to spindle identification and detection that is (1) independent of human judgment\, (2) highly robust as gauged against available reference data sets (3) capable of revealing novel insights into properties of spindle waveforms and their association with sleep state.\n\nReferences\n\n\nDevuyst\, S.\, et al. (2011). Automatic sleep spindles detection—overview and development of a standard proposal assessment method. In 2011 Annual international conference of the IEEE engineering in medicine and biology society\, pp. 1713–1716. IEEE.\nKovach\, C. K.\, et al. Interpreting the trispectrum as the cross-spectrum of the wigner–ville distribution. IEEE Signal Processing Letters 33\, 221–225.\nKovach\, C. K. and M. A. Howard (2019).&nbsp\; Decomposition of higher-order spectra for blind multiple-input deconvolution\, pattern identification and separation. Signal Processing 165\, 357 – 379.\nLacourse\, K.\, et al. (2020). Massive online data annotation\, crowdsourcing to generate high quality sleep spindle annotations from eeg data. Scientific data 7 (1)\, 190.\n\nAcknowledgement\n\n\n\n\nNIH&nbsp\;(Grant Number:&nbsp\;3UH3NS113769\, R01NS117753 and R01DC004290)\n\nDOD&nbsp\;(Grant Number:&nbsp\;W81XWH-19-1-0637)
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:20794606136e59b806f298920a932271
URL:http://cns2026.sched.com/event/20794606136e59b806f298920a932271
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SUMMARY:P002: How Can Spiking Networks Remain Resilient Under Degeneration?
DESCRIPTION:Introduction\nProgressive loss of synapses and neurons can reshape circuit activity before complete network failure. Yet it remains unclear why some spiking networks preserve weakly active\, irregular dynamics under structural damage while others drift toward abnormal firing\, variability or synchrony [1\,2]. This problem is difficult because structural loss\, baseline dynamics and excitatory-inhibitory organization interact. We address two questions: which factors support resilience during degeneration\, and can activity changes be predicted from network structure?\n\n\nMethods\nWe simulated an empirical layer-4 cortical microcircuit [3] and matched synthetic networks: Erdős-Rényi\, small-world\, scale-free\, and two inhibition-promoting variants. All networks were calibrated to comparable baseline spiking activity. Degeneration was applied in two families\, synaptic and neuronal\, each with five pruning rules spanning random\, peripheral\, central and broadcaster-targeted damage. For each network\, we related firing rate\, variability and synchrony to global and subpopulation-resolved weighted structural descriptors.\n\n\nResults\nInhibition-promoting architectures\, including subpopulation-constrained and inhibitory-hub networks\, resisted degeneration at moderate inhibitory strength\, whereas generic synthetic networks drifted more strongly. Stronger inhibition could stabilize all network classes\, showing that architecture changes the inhibitory gain required for resilience rather than defining an absolute resilient category. Effective synaptic weight organized within-class activity trends\, while weight-aware E/I interaction features captured cross-architecture differences and predicted activity changes.\n\n\n\n\nDiscussion\nThese results suggest that circuits may be especially vulnerable when degeneration weakens inhibitory control or disrupts where inhibition is positioned. They also offer a possible interpretation of maladaptive sprouting: adding connections semi-randomly may restore connection number while blurring the fine inhibitory organization needed for stability. Conversely\, therapies that boost inhibition or improve its recruitment could help stabilize activity under structural loss.&nbsp\;\n\n\nReferences\n[1] van Vreeswijk C\, Sompolinsky H. Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science. 1996.\n\n[2] Brunel N. Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. Journal of Computational Neuroscience. 2000.\n[3] Landau ID\, Egger R\, Dercksen VJ\, Oberlaender M\, Sompolinsky H. The impact of structural heterogeneity on excitation-inhibition balance in cortical networks. Neuron. 2016.\n\nAcknowledgement\nThis work was supported by the PEPR Sant´e Num´erique program (France 2030)\, project “Brain Health Trajectories (BHT)”\, implemented by the Agence Nationale de la Recherche (ANR) under grant number ANR-22-PESN-0012-BHT.&nbsp\;\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:febdb9547d3eea1ee25711ab7471df72
URL:http://cns2026.sched.com/event/febdb9547d3eea1ee25711ab7471df72
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SUMMARY:P003: How long we live? Insights into neural ageing using fractional harmonic oscillator
DESCRIPTION:Introduction\n\nNeural signals exhibit systematic changes across the lifespan. In particular\, the 1/f slope of the neural power spectral density (PSD)\, a measure of power decay with frequency\, shows age-dependent decline from infancy to old age. Another PSD feature\, the peak power of gamma oscillations (20-66 Hz)\, elicited by visual stimulus\, varies non-monotonically with age\, while increasing from childhood to adolescence and decreasing later. While these neural features show promise as physiological markers of ageing\, researchers could not capture their age-related variation together using a single model. Moreover\, signal’s non-linearity\, often measured by Higuchi fractal dimension (HFD)\, exhibits inconsistent changes with ageing.\n\nMethods\n\nHere\, we model the neural signals via stochastic fractional harmonic oscillator (sFHO) (Fig.1 top)\, that captures the non-Markovian and fractal nature of the signals [1]. It intrinsically includes memory through its non-integer derivatives ‘alpha’. We use the observed monotonic decline of slope with age to get inferences about HFD from the model (Fig. 1A and B). The model explains age-related changes in power and centre frequency of stimulus-induced gamma oscillations (Fig. 1C) and resolves the conflicting findings of HFD. Moreover\, using just mathematics\, it predicts that in order to have a decline in EEG gamma power in old age\, the gamma power should increase from childhood to adolescence.\n\nResults\n\nTo understand the underlying neural mechanism\, I hypothesize a relation of excitation-inhibition (E-I) ratio with the non-integer derivative and age (Fig. 1D). I show how the E-I ratio could be increasing with age monotonically (fig. 1F) despite a non-monotonic change in the individual concentration of excitation and inhibition neurotransmitters (Fig. 1E). Taking a bold step forward\, I use this framework to estimate human life expectancy in existing electroencephalogram (EEG) and electrocorticogram (ECoG) datasets of healthy adults and epileptic patients as 76.9 and 69.7 years respectively that was consistent with population statistics.\n\nDiscussion\n\nThe present model captures the changes in PSD features from infancy to old age\, in place of focusing only either on childhood-related growth or degeneration in late stages of life\, thereby\, providing a unified framework. It could help in constraining neural mechanisms governing ageing and has huge potential for future individual lifespan estimation and disease-risk assessment.\n\n\n Figure 1. Age-related inferences: Top: Model equation. (A) HFD variation with α and λ. (B)\, (C) The PSDs and ΔPower (in dB) corresponding to triangles and diamonds respectively. (D) Illustration of E/I dependence on ageing and α. (E) The concentration of excitatory and inhibitory neurotransmitters varying non-monotonically with age. (F) Corresponding monotonic E/I ratio with age and α.​\n\nReferences\nAggarwal\, S. (2025). Decoding human lifespan from neural noise and explaining age-related changes in fractal dimension and gamma oscillations using fractional harmonic oscillator (p. 2025.09.13.675905). bioRxiv.&nbsp\;https://doi.org/10.1101/2025.09.13.675905\nAcknowledgement\n\nThe author expresses gratitude towards Prof. Banibrata Mukhopadhyay\, Department of Physics\, IISc\, Prof. Supratim Ray\, Centre for Neuroscience\, IISc and Dr. Surya Prakash for scientific discussions and guidance that enriches the quality of this work.\n&nbsp\;
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:d66f36df18b9ff597e3baf531b309030
URL:http://cns2026.sched.com/event/d66f36df18b9ff597e3baf531b309030
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SUMMARY:P004: Beyond Optimality: Neural Mechanisms of Heuristic Decision-Making
DESCRIPTION:Introduction\nHumans and animals deploy diverse strategies within their ecological environments. Much of neuroscience has focused on normative frameworks in which agents optimize decisions — Bayesian inference\, speed–accuracy tradeoffs\, gradient-based learning [1\,2]. Yet real agents frequently rely on heuristics specifically adapted to their ecology: cognitive shortcuts that save time and resources\, and can outperform optimal strategies in complex environments [3]. Despite their influence in psychology and behavioral economics as fast\, automatic "System 1" processes\, heuristics remain underrepresented in neuroscience. How neural systems implement and switch between heuristics and deliberative strategies remains a central challenge.\n\nMethods\nWe study heuristics in perceptual decision-making using a discrimination task where one “over-represented” stimulus evokes stronger population activity. Monkeys were shown to solve this task by relying on a simple heuristic: summing activity to detect the over-represented stimulus\, instead of optimally integrating activity weighted by task relevance [3]. We implement a model neural network trained with either gradient descent or Oja learning rule under over-represented and equally-represented conditions. We then assess\, as in [3]\, whether optimal readout or heuristic strategies were implemented using: (1) accuracy imbalance between stimuli under inactivation\; (2) choice probability correlating single-neuron activity with stimulus choice.\n\nResults\nWhile networks trained with gradient descent learn optimal strategies\, networks trained with the Oja rule reproduce empirical signatures of heuristics when the activity is uncentered. Using Oja\, networks learn to extract the first principal component of population activity as readout weights [4]. When activity is uncentered\, the mean dominates\, driving Oja toward a constant readout weight vector "summation" solution. When stimuli become equally-represented\, this solution fails and the network centers activity through slow integration of mean activity\, yielding a non-heuristic solution consistent with theoretical and experimental predictions. Our framework provides a mechanistic model of switching between heuristic and optimal strategies.\n\nDiscussion\nUnderstanding how the brain switches between fast heuristics (System 1) and deliberate cognition (System 2) has broad implications for psychology\, neuroscience\, and economics [5]. Our framework suggests heuristics are not mere shortcuts but ecologically rational strategies — tuned to environmental statistics and implemented through simple neural computations. This reframing has consequences for how we study decision-making across species. Beyond basic science\, these insights can inform neuroAI systems that integrate rapid heuristic strategies with precise reasoning\, offering a path toward more generalizable\, energy-efficient models.\n\nReferences\n\nKörding\, K. P.\, & Wolpert\, D. M. (2004). Bayesian integration in sensorimotor learning. Nature\, 427(6971)\, 244–247. https://doi.org/10.1038/nature02169\nRichards\, B. A.\, & Kording\, K. P. (2023). The study of plasticity has always been about gradients. The Journal of Physiology\, 601(15)\, 3141–3149. https://doi.org/10.1113/JP282747\nLaamerad\, P.\, Krause\, M. R.\, Guitton\, D.\, & Pack\, C. C. (2025). Inactivation of primate cortex reveals inductive biases in visual learning. Current Biology\, 35(19)\, 4699–4713.e6. https://doi.org/10.1016/j.cub.2025.08.027\nOja\, E. (1982). A simplified neuron model as a principal component analyzer. Journal of Mathematical Biology\, 15(3)\, 267–273. https://doi.org/10.1007/BF00275687\nKahneman\, D. (2011). Thinking\, fast and slow. Farrar\, Straus and Giroux.\n\n\n\nAcknowledgement\nThis work was supported by IVADO Projet Exploratoire (Explo24CO-3750823649).\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:c3010036df036a7db83446494cb68d77
URL:http://cns2026.sched.com/event/c3010036df036a7db83446494cb68d77
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SUMMARY:P005: A Brain-Wide Atlas of Intrinsic Neural Timescales in Mice
DESCRIPTION:Introduction\n\nIntrinsic neural timescales quantify how long neurons integrate information\, a fundamental metric of brain organization [1]. Yet accurate brain-wide mapping has been limited by two methodological challenges: binned autocorrelation methods underestimate timescales in low-firing neurons\, and single-exponential models obscure multi-component temporal structure. We addressed both by combining iSTTC (intrinsic Spike Time Tiling Coefficient) [2] with multi-exponential modeling [3]. Applied to 89\,047 neurons across 266 mouse brain regions (IBL 2025 dataset)\, this framework enables timescale estimation in low-firing neurons and captures multi-component temporal structure\, providing broad coverage across 86% of mouse brain regions. \n\nMethods\n\nWe analyzed spontaneous spiking activity from 580\,598 units across 427 sessions and 131 mice in the IBL 2025 Brainwide Map Release. Units meeting quality criteria (≥100 spikes\, declining autocorrelation\, R^2 ≥ 0.5) were retained (89\,047 units). Timescales were estimated using iSTTC\, and each autocorrelation fitted with 1-4 exponential components\, with optimal complexity selected by BIC. The amplitude-weighted effective timescale \u200bτ_eff captures the overall integration window of a neuron\, weighted by the contribution of each exponential component. To test the anatomical gradient\, we fitted a Bayesian hierarchical model with random intercepts for region\, mouse\, session\, and probe. \n\nResults\n\nMedian τ_eff spanned nearly two orders of magnitude across regions (37.9-3\,115 ms)\, following a rostro-caudal gradient: forebrain 213 ms (IQR = 120-304 ms)\, midbrain 765 ms (IQR = 482-968 ms)\, hindbrain 956 ms (IQR = 723-1183 ms). This gradient was observed in 100% of individual mice (median Spearman ρ = 0.76\, p &lt\; 10⁻³⁰). A Bayesian hierarchical model controlling for mouse\, session\, and probe confirmed brain region as the dominant variance source (24.1%)\, with substantial within-region heterogeneity remaining (median IQR = 588 ms). 73.9% of neurons required multi-component models: τ₂ co-varied sublinearly with τ₁ across regions (r = 0.766\, p = 9.68 × 10⁻⁴⁴\, slope = 0.545).\n\nDiscussion\n\nAnatomical position along the rostro-caudal axis is a strong organisational principle of intrinsic neural timescales\, holding robustly across 220 regions. The longer timescales of midbrain and hindbrain may reflect their roles in integrating homeostatic\, motor\, and state-related signals over extended periods. The majority of neurons (73.9%) are better described by multiple timescale components\; τ₁ may reflect intrinsic neuronal properties\, while τ₂\, extending to several seconds\, points to recurrent network interactions or neuromodulation. Within-region variance (median IQR=588 ms) likely reflects cell type\, laminar position\, and local connectivity\, motivating future integration with anatomical cell-type data.\n\nBrain-wide map of intrinsic neural timescales. (A) τ_eff by major brain division. (B) τ_eff across 12 brain subdivisions. (C) Median τ_eff per region\; error bars show 10th–90th percentiles. Regions grouped by division\, ordered alphabetically\; colors denote subdivision. (D) Fast (τ₁) vs slow (τ₂) timescales across regions.\n\nReferences\n\n1. Murray\, J. D.\, et al. (2014). A hierarchy of intrinsic timescales across the primate cortex. Nat Neurosci\, 17(12)\, 1661-1663. https://doi.org/10.1038/nn.3862\n\n2. Pochinok\, I.\, Hanganu-Opatz\, I. L.\, & Chini\, M. (2026). iSTTC: A robust method for accurate estimation of intrinsic neural timescales from single-unit recordings. PLOS Comput Biol\, 22\, e1013385. https://doi.org/10.1371/journal.pcbi.1013385\n\n3. Shi\, Y. L.\, et al. (2025). Brain-wide organization of intrinsic timescales at single-neuron resolution. bioRxiv. https://doi.org/10.1101/2025.08.30.673281\n\n4. Beiran\, M.\, & Ostojic\, S. (2019). Contrasting the effects of adaptation and synaptic filtering on the timescales of dynamics in recurrent networks. PLOS Comput Biol\, 15(11)\, e1007462. https://doi.org/10.1371/journal.pcbi.1007462\n\n\nAcknowledgement\nSupported by Neuromatch Impact Scholars Program. Data from International Brain Laboratory 2025 Brainwide Map Release. We thank the IBL consortium for open data access and Jason Manley for supervision. Analysis builds on iSTTC methodology (Pochinok et al. 2026) and multi-exponential fitting framework (Shi et al. 2025). \n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:3ccb9062635c24eb104fc3854b4cc478
URL:http://cns2026.sched.com/event/3ccb9062635c24eb104fc3854b4cc478
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SUMMARY:P006: Disentangling Sensory Drivers of Spatial Codes with Recurrent Audiovisual Models in VR
DESCRIPTION:Introduction\nSpatial navigation relies on integrating multimodal cues[1]\, yet both in vivo and in silico hippocampal research overwhelmingly focuses on vision [2\,3\,6]. While recent work showed mice entorhinal cortex contain both unimodal and multimodal cells[3]\, how different modalities are weighted and integrated remains poorly understood. We develop a modelling pipeline with an agent traversing a multimodal VR environment. A recurrent neural network was trained to perform a next-state prediction task[2]. We hypothesised that the network would develop place cell-like units that utilise both modalities\, and that integrating modalities would result in more robust spatial representations\, with each sense contributing additively to the cognitive map.\n\nMethods\nAn agent traversed a 2D arena with audiovisual cues in Unity3D[4]. Binaural audio broadcasted with head-related transfer functions[5] and visual frames were encoded via autoencoders into low-dimensional embeddings. We trained an RNN[2] to perform next-state prediction using its current sensory states and motion\, under three conditions: audiovisual\, visually-lesioned and auditorily-lesioned. Latent units were classified into place units using empirical metrics such as spatial information scores. These tunings were used to perform maximum-likelihood decoding of the agent’s position and orientation. The relative contributions of sensory inputs were effectively decomposed using a linearly weighted combination of their unimodal responses.\n\nResults\nThe audiovisual model produced 127 spatially tuned place cells\, significantly more than visually-driven (33 cells) and auditorily-driven (81 cells) ones. Furthermore\, the audiovisual model yielded the lowest trajectory decoding error (0.151 m) compared to visual-only (0.879 m) and auditory-only (0.293 m) ones\, with the highest spatial information content. Unimodal units that respond to a single modality were identified\, as well as multimodal units that remap when both are present. Finally\, by approximating multimodal ratemaps as linear combinations of unimodal maps\, we found that most place cells integrate modalities additively\, exhibiting intermediate visual weightings (μ=0.405) and relying more on auditory cues.\n\nDiscussion\nWhile derived in silico\, these results offer a framework for biological navigation. The model suggests the hippocampus may additively processes multisensory streams to reduce uncertainty rather than switching between senses. Notably\, auditory cues proved dominant in our VR setup\, likely because visual landmarks lose salience at a distance or vanish when facing walls. Consequently\, multimodal units anchor to the most reliable available cues—in this case\, sound. We further hypothesise these units will dynamically remap or reweigh sensory reliance if a primary modality degrades. Ultimately\, this model provides a normative theory for multisensory integration\, generating precise\, testable predictions for planned in vivo ferret recordings.\n\n(a) Virtual environment with visual cues and sound sources\; (b) Model architecture and pipeline\; (c) Spatial ratemap examples in audiovisual\, and lesioned (-) conditions\; (d) Distribution of spatial information contents\; (e) Number of place units identified\; (f) ML decoding of position\; (g) ML decoding of head direction\; (h) Distribution of visual weights (x) and resulting correlations (y)\; compar\n\nReferences\n\nJeffery\, K. J. (2007). Integration of the sensory inputs to place cells: what\, where\, why\, and how?. Hippocampus\n[1] Levenstein\, D.\, ...\, & Richards\, B. (2024). Sequential predictive learning is a unifying theory for hippocampal representation and replay. bioRxiv\n[2]&nbsp\;Nguyen\, D.\, ... \, & Gu\, Y. (2024). The medial entorhinal cortex encodes multisensory spatial information. Cell reports\n[3]&nbsp\;George\, T. M.\, ...\, & Barry\, C. (2024). RatInABox\, a toolkit for modelling locomotion and neuronal activity in continuous environments. Elife\n[4]&nbsp\;Cuevas-Rodríguez\, ... & Reyes-Lecuona\, A. (2019). 3D Tune-In Toolkit: An open-source library for real-time binaural spatialisation. PloS one\n[5] Banino\, A.\, ... & Kumaran\, D. (2018). Vector-based navigation using grid-like representations in artificial agents. Nature\n\n\nAcknowledgement\nWe thank Barry Lab\, Bizley La\, the Department of UCL Cell and Developmental Biology\, the Ear Institute for this work. \nThis&nbsp\;work was supported by the UKRI Biotechnology and Biological Sciences Research Council [grant\nnumber BB/T008709/1].
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:e5c4411b577ce3c7d854bdeea6ab27cb
URL:http://cns2026.sched.com/event/e5c4411b577ce3c7d854bdeea6ab27cb
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SUMMARY:P007: Spatial attention shapes the pupillary light response
DESCRIPTION:Introduction\nTraditionally\, the pupillary light response is viewed as a global reflex stabilizing retinal illumination by integrating luminance across the visual field [1]. However\, recent work suggests pupil responses are also modulated by spatial mechanisms linked to attention and eye movement planning. When global luminance is held constant\, directing attention to brighter regions produces stronger constriction\, indicating location-specific luminance weighting [2]. Moreover\, the pupil can begin adjusting to upcoming saccade target luminance before gaze shifts\, suggesting anticipatory modulation linked to presaccadic attention [3]. Together\, these findings suggest pupil dynamics reflect both global and gaze-dependent local luminance signals.\n\n\nMethods\n51 participants (ages 20-25) were recorded with an EyeLink-1000 eye-tracker while freely viewing 10 naturalistic movies. First\, luminance was extracted at the pixel level frame-by-frame using a photometric calibration. Analysis 1: gaze-contingent retinal luminance was mapped onto a 1°×1° spatial grid and regularized regression was used to estimate spatial luminance sensitivity of the pupil across the visual field. Analysis 2: intersaccadic intervals (ISI) between saccades were identified and linear mixed-effects models tested whether late-ISI pupil diameter predicted upcoming saccade goal luminance\, controlling for current fixation\, opposite-direction (control location)\, and global luminance.\n\n\nResults\n\nDiscussion\nThese findings advance understanding of how pupil dynamics encode spatial and temporal visual information during natural viewing. Central weighting (Analysis 1) likely reflects attentional allocation. The anticipatory effect at upcoming saccade targets (Analysis 2) suggests presaccadic attention modulates pupillary responses before gaze arrival\, consistent with oculomotor structures such as the superior colliculus and frontal eye fields influencing pupil control [4]. Limitations include use of instantaneous pupil measurements without accounting for pupillomotor delay (~200-300ms) in analysis 2. Ongoing work aims to incorporate temporal lags in analysis 2 and test generalizability across diverse tasks.\n\n\nReferences\nWatson\, A. B.\, & Yellott\, J. I. (2012). A unified formula for light-adapted pupil size. Journal of vision\, 12(10)\, 12. https://doi.org/10.1167/12.10.12Binda\, P.\, & Murray\, S. O. (2015). Spatial attention increases the pupillary response to light changes. Journal of vision\, 15(2)\, 1. https://doi.org/10.1167/15.2.1 Mathôt\, S.\, van der Linden\, L.\, Grainger\, J.\, & Vitu\, F. (2015). The pupillary light response reflects eye-movement preparation. Journal of experimental psychology. Human perception and performance\, 41(1)\, 28–35. https://doi.org/10.1037/a0038653 C. Wang\, & D.P. Munoz\, Neural basis of location-specific pupil luminance modulation\, Proc. Natl. Acad. Sci. 115 (41)\, 10446-10451\, https://doi.org/10.1073/pnas.1809668115 \n\n\nAcknowledgement\nThe research was undertaken thanks in part to funding from the Connected Minds Program\, supported by Canada First Research Excellence Fund\, Grant #CFREF-2022-00010\, and the Natural Sciences and Engineering Research Council of Canada (NSERC).\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:ea5ad03196787a59ae568f723e6205da
URL:http://cns2026.sched.com/event/ea5ad03196787a59ae568f723e6205da
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SUMMARY:P008: Movement representations for classification and perception: posture dominates over dynamics
DESCRIPTION:Introduction\nMotion perception is the remarkable ability of the visual system to recognize complex human movements effortlessly. Computational movement analysis seeks representations that mirror this efficiency while&nbsp\;remaining&nbsp\;interpretable.&nbsp\;The motor modularity hypothesis proposes that movements are composed&nbsp\;of&nbsp\;weighted&nbsp\;primitives [1]\, yet whether theory-driven decompositions outperform alternative&nbsp\;approaches&nbsp\;remains&nbsp\;untested. We compared Temporal Movement Primitives (TMPs)\,&nbsp\;Legendre polynomial coefficients\, and autoencoder embeddings&nbsp\;to&nbsp\;ask which movement features enable motion&nbsp\;classification&nbsp\;and&nbsp\;assess if the chosen&nbsp\;movement&nbsp\;features&nbsp\;align&nbsp\;with how human observers discriminate actions\, paralleling perceptual research on form versus motion cues&nbsp\;[2].\n\n\nMethods\nWe analyzed videos of 16 daily activities from the&nbsp\;MoVi&nbsp\;dataset [3] and extracted joint-angle trajectories using&nbsp\;MMPose\, with segmentation via visual&nbsp\;inspection. We&nbsp\;represented&nbsp\;these trajectories in three ways:&nbsp\;1-&nbsp\;TMP weights from Bayesian decomposition with Gaussian process priors for varying numbers of primitives (1- 20)\; 2- Legendre polynomial coefficients for varying maximum degrees (0–10)\;&nbsp\;3-&nbsp\;latent vectors from an encoder-decoder network.&nbsp\;To&nbsp\;determine&nbsp\;which&nbsp\;features drive discrimination\, we assessed&nbsp\;cross-validated&nbsp\;classification accuracy\,&nbsp\;optimal&nbsp\;primitive count and polynomial degree\, reconstruction&nbsp\;quality\, and&nbsp\;interpretability. To isolate dynamics from posture\, we repeated analyses after subtracting mean joint positions per trial.\n\n\nResults\nLegendre coefficients achieved 96%&nbsp\;cross-validated&nbsp\;classification accuracy across 16&nbsp\;classes\, outperforming TMP weights (91%) and autoencoder features (85%).&nbsp\;Optimal TMP count was&nbsp\;5&nbsp\;primitives\,&nbsp\;and&nbsp\;the&nbsp\;optimal&nbsp\;Legendre degree was&nbsp\;0\,&nbsp\;revealing postural configuration\, not temporal dynamics\, is the primary discriminative feature.&nbsp\;After posture removal\, degree-2&nbsp\;polynomials captured remaining discriminative dynamics. Classification and movement generation dissociated: when generating movements from averaged category weights\, TMPs preserved dynamics\, producing natural motion\,&nbsp\;whereas&nbsp\;Legendre-generated movements&nbsp\;retained&nbsp\;original posture but with unclear motion. L1 regularization&nbsp\;identified&nbsp\;10 joints carrying the most discriminative information.\n\n\nDiscussion\nPosture dominance for activity classification aligns with biological motion&nbsp\;perception\, showing form cues&nbsp\;often&nbsp\;suffice for action-type recognition. The dissociation between classification and generation shows discriminative and generative adequacy&nbsp\;are distinct properties: Legendre coefficients excel at categorization\, TMPs preserve temporal structure for synthesis\, and autoencoders achieve&nbsp\;optimal&nbsp\;dimensionality reduction from&nbsp\;240 (5 primitives × 48 coordinates) to 32.&nbsp\;Beyond classification\, these results reveal that movement is organized into separable postural and dynamic components\, opening avenues&nbsp\;to explore minimum temporal duration for motion&nbsp\;perception\, whether partial cycles suffice\, and how accuracy scales with available dynamics.\n\n\nReferences\nKnopp\, B.\,&nbsp\;Velychko\, D.\,&nbsp\;Dreibrodt\, J.\, & Endres\, D. (2019b). Predicting perceived naturalness of human animations based on generative movement primitive models.&nbsp\;ACM Transactions on Applied Perception\,&nbsp\;16(3)\, 1–18.&nbsp\;https://doi.org/10.1145/3355401Lange\, J.\, & Lappe\, M. (2006). A Model of Biological Motion Perception from Configural Form Cues.&nbsp\;Journal of Neuroscience\,&nbsp\;26(11)\,&nbsp\;2894–2906.&nbsp\;https://doi.org/10.1523/jneurosci.4915-05.2006Ghorbani\, S.\,&nbsp\;Mahdaviani\, K.\, Thaler\, A.\,&nbsp\;Kording\, K.\, Cook\, D. J.\, Blohm\, G.\, & Troje\, N. F. (2021).&nbsp\;MoVi: A large multi-purpose human motion and video dataset.&nbsp\;PLoS&nbsp\;ONE\,&nbsp\;16(6)\, e0253157.&nbsp\;https://doi.org/10.1371/journal.pone.0253157&nbsp\;\n\n\nAcknowledgement\nThis work was supported by&nbsp\;the Natural Sciences and Engineering Research Council (NSERC) of Canada.&nbsp\;The research was undertaken&nbsp\;thanks in part to funding from the Connected Minds Program\, supported by Canada First Research Excellence Fund\, Grant #CFREF-2022-00010. Also\,&nbsp\;we&nbsp\;thank&nbsp\;the creators of the&nbsp\;MoVi&nbsp\;dataset for making their data publicly available.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:ff2ec5bb7f7e4db678dc39a0e34bce18
URL:http://cns2026.sched.com/event/ff2ec5bb7f7e4db678dc39a0e34bce18
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SUMMARY:P009: Optimal Self-Organization in Mean-Field Multi-Agent Neuronal Networks
DESCRIPTION:Introduction\nNeuronal networks (NNs) are predominantly modeled as dynamical systems\, requiring ad hoc analyses to explore the interplay between population codes (PCs) and computation [3\,4]. By viewing PCs as distributions over the NN state space [2] we develop a framework for modeling emergent computation through the lens of multi-agent (MA) optimal control (OC). In it\, neurons regulate their local parameters to collectively control the PC and (in turn) optimize a cost function. A mean-field (MF) limit is derived for a large class of NNs\, whence we prove theorems establishing global optima as laws of self-organization. Such models are built on a rich mathematical literature\, with great potential for further theoretical results and learning algorithms.\n\nMethods\nMean-field game/control theory is an analytical tool for characterizing/learning optimal decision-making in complex strategic systems. MF limits are useful because they ‘average out’ microscale fluctutations to distill macroscale behavior\, dramatically simplifying controls while providing powerful approximations for large\, finite populations. However\, traditional MF theories fail for general networks. We resolve this limitation and maximize the class of compatible models/cost functions while preserving a detailed theoretical characterization. The setup in [1] closely resembles what we propose\, but lacks control. Moreover\, the MF is taken to model a PC by characterizing the NN up to each neuron’s identity.\n\nResults\nFor a large class of computational tasks where neurons contribute to the PC anonymously\, we characterize OCs without any a priori restriction on their structure or the information available to each neuron. Specifically\, under an OC: (i) neurons are decentralized (acting independently given their local state and each subpopulation’s MF) such that the emergent parameter regime is realized through a process of self-organization\, and (ii) neurons of the same species deploy an identical strategy\, reinforcing its biological plausibility\; neurons of the same type will behave identically under fixed conditions. As a concrete example\, we construct a multi-population Hodgkin Huxley network designed to express a binary decision via its MF PC.\n\nDiscussion\nReformulating NNs as MF MA systems unlocks a wealth of analytical tools\, learning algorithms and theoretical guarantees ripe for neuroscience applications. This work is a crucial first step in bridging the gap. Our technical results are complemented by the conjecture that PCs are expressed through the MF\, which in turn emerges from optimal laws of self-organization at the microscale. Unlike many cost function-based NNs\, these are global optima\, affording greater normative potential. In exchange\, models require target computations to be expressed as explicit cost functions assessing the PC directly\, which has received little attention to-date. Future work will focus on developing this aspect further\, along with simulation/learning studies.\n\nReferences\n1.&nbsp\;Baladron\, J.\, Fasoli\, D.\, Faugeras\, O.\, & Touboul\, J. (2012). Mean-field description and propagation of chaos in networks of hodgkin-huxley and fitzhugh-nagumo neurons. Journal of Mathematical Neuroscience\, 2\, 10. doi: 10.1186/2190-8567-2-10\n2. Beck\, J. M.\, Latham\, P. E.\, & Pouget\, A. (2011). Marginalization in neural circuits with divisive normalization. The Journal of Neuroscience\, 31(43)\, 15310–15319. doi: 10.1523/JNEUROSCI.1706-11.2011\n3.&nbsp\;Denève\, S.\, & Machens\, C. K. (2016). Efficient codes and balanced networks. Nature Neuroscience\, 19(3)\, 375–382. doi: 10.1038/nn.4243\n4. Wong\, K.-F.\, & Wang\, X.-J. (2006). A recurrent network mechanism of time integration in perceptual decisions. The Journal of Neuroscience\, 26(4)\, 1314–1328. doi: 10.1523/JNEUROSCI.3733-05.2006\n\nAcknowledgement\nWe acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC).\n\nNous remercions le Conseil de recherches en sciences naturelles et en génie du Canada (CRSNG) de son soutien.\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:a48c331a020cc592fd538ac6d69cccad
URL:http://cns2026.sched.com/event/a48c331a020cc592fd538ac6d69cccad
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SUMMARY:P010: Computing Motor Error with Inhibition: A Minimal Excitatory-Inhibitory Circuit Motif
DESCRIPTION:Introduction\nMotor control requires continuous comparison between desired and actual states\, yet how error signals are computed at the cellular level is not well understood. Optimal Feedback Control theory presents what computations the brain might perform during movement but not how neurons implement them [1\,2]. Inhibition in sensorimotor cortex is typically framed as maintaining excitatory-inhibitory balance\, shaping activity patterns\, or providing gain control [3]\, not as computing error signals. We propose a minimal excitatory-inhibitory (E-I) circuit motif in which inhibition implements subtraction\, providing a mechanistic account of error computation.\n\n\nMethods\n\nResults\nIndividual E-I pairs produce rectified subtraction and track sinusoidal inputs up to 5 Hz with a gain above 0.5 before attenuating at higher frequencies\, comfortably exceeding the approximately 2.4 Hz bandwidth imposed by muscle-tendon dynamics [4]. At the population level\, linear decoders achieve R-squared greater than 0.85 for position error and velocity during centre-out reaching tasks.\n\n\nDiscussion\n\nReferences\n[1] Scott\, S. H. (2004). Optimal feedback control and the neural basis of volitional motor control. Nat Rev Neurosci\, 5(7)\, 532-546.\n[2] Todorov\, E. & Jordan\, M. I. (2002). Optimal feedback control as a theory of motor coordination. Nat Neurosci\, 5(11)\, 1226-1235.\n[3] Isaacson\, J. S. & Scanziani\, M. (2011). How inhibition shapes cortical activity. Neuron\, 72(2)\, 231-243.\n[4] Crevecoeur\, F. & Scott\, S. H. (2014). Beyond Muscles Stiffness: Importance of State-Estimation to Account for Very Fast Motor Corrections. PLoS Comput Biol\, 10(10)\, e1003869.\n\n\nAcknowledgement\nThis work was supported by a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant to G. Blohm.\n\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:846a12988b6984969a6c9364d46ff3bc
URL:http://cns2026.sched.com/event/846a12988b6984969a6c9364d46ff3bc
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SUMMARY:P011: Dendrites Learn to Detect Input Sequences Through Local Plasticity
DESCRIPTION:Introduction\n\nNeurons can encode information not only by which cells fire\, but also by the order in which they fire: n active neurons can represent n! sequences. Hippocampal place cells fire in such sequences during navigation and replay these sequences during rest. During replay\, synapses can be activated sequentially from tip to soma along dendrites [1]. A dendrite can selectively advance depolarization in response to tip-to-soma inputs\, making it sequence selective [2]. But this selectivity operates within a limited range of AMPA conductance that changes with synaptic spacing\, so we ask whether local plasticity can tune these conductances. Our plasticity rule may offer a mechanism for the emergence of plateaus like those observed in BTSP [3].\n\n\nMethods\nVoltage-dependent NMDA and KIR channels make each dendritic segment bistable: stable at rest and at plateau [4]. A synaptic input briefly opens AMPA channels\, depolarizing the segment and potentially transitioning it from rest to plateau. For plateau advancement to be sequence selective\, AMPA conductance must be just strong enough that an input transitions a segment from rest to plateau only with the support of depolarizing current from its plateauing tip-side neighbor. We therefore strengthen AMPA conductance when a segment fails to plateau despite its tip-side neighbor plateauing\, and weaken AMPA conductance when it plateaus despite its tip-side neighbor resting. We tested whether this rule operates across different synaptic spacings.\n\nResults\nRepeated tip-to-soma and shuffled input presentations robustly tune each segment’s AMPA conductance into its sequence-selective range. Starting from weak AMPA conductances\, analogous to AMPA-silent synapses\, inputs initially fail to advance the plateau. Under our plasticity rule\, the AMPA conductance of the segment adjacent to the established plateau increases until the segment begins plateauing\, after which tuning progresses to the next segment (Fig. 1A). Thus\, segments are tuned sequentially from tip to soma\, and the number of presentations increases linearly. Because each synapse independently converges on the conductance range it requires\, the rule remains effective across heterogeneous synaptic spacings.\n\n\nDiscussion\nBecause plateau initiation occurs at a bifurcation\, gradual AMPA tuning produces an abrupt transition: a segment initially fails to plateau\, then suddenly starts plateauing once AMPA conductance enters the sequence-selective range (Fig. 1B). This sudden plateauing may offer a mechanistic interpretation of BTSP\, in which plateaus emerge abruptly after repeated trials: during the initial trials\, dendritic plateaus may remain local\, but after tuning is complete they may propagate to the soma. Glutamate uncaging experiments could test whether repeated tip-to-soma stimulation selectively strengthens AMPA conductances from tip to soma. Future work should identify a local signal reporting whether the tip-side neighbor plateaued.\n\nLocal AMPA plasticity tunes dendritic sequence selectivity. Repeated tip-to-soma and shuffled inputs progressively strengthen AMPA conductance from tip to soma (A). When a segment (green) receives input while its tip-side neighbor is plateauing (yellow)\, weak AMPA initially fails to trigger a plateau. As AMPA increases\, the segment abruptly transitions to plateau at a bifurcation (B).References\n\nIshikawa\, T.\, & Ikegaya\, Y. (2020). Locally sequential synaptic reactivation during hippocampal ripples. Science Advances\, 6(7)\, Article eaay1492. https://doi.org/10.1126/sciadv.aay1492Boahen\, K. (2022). Dendrocentric learning for synthetic intelligence. Nature\, 612(7938)\, 43–50. https://doi.org/10.1038/s41586-022-05340-6Bittner\, K. C.\, Milstein\, A. D.\, Grienberger\, C.\, Romani\, S.\, & Magee\, J. C. (2017). Behavioral time scale synaptic plasticity underlies CA1 place fields. Science\, 357(6355)\, 1033–1036. https://doi.org/10.1126/science.aan3846Sanders\, H.\, Berends\, M.\, Major\, G.\, Goldman\, M. S.\, & Lisman\, J. E. (2013). NMDA and GABAB (KIR) conductances: The “perfect couple” for bistability. The Journal of Neuroscience\, 33(2)\, 424–429. https://doi.org/10.1523/JNEUROSCI.1854-12.2013\n\n\nAcknowledgement\n\nThis study was supported by the Stanford Bioengineering Department and Masason Foundation.&nbsp\;\n\n\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:4dfde58226c8ff75c9fff5c98fe4a66e
URL:http://cns2026.sched.com/event/4dfde58226c8ff75c9fff5c98fe4a66e
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SUMMARY:P012: Integrating Neuroimaging and Omics to Characterize Parkinson’s Disease Progression
DESCRIPTION:Introduction\nParkinson's disease (PD) is a progressive neurodegenerative disorder characterized by dopaminergic neuronal loss and heterogeneous clinical trajectories with motor and non-motor symptoms [1]. Predicting disease progression requires biomarkers capturing both brain alterations and underlying molecular mechanisms. Neuroimaging reveals structural brain changes [2]\, while transcriptomic and proteomic analyses characterize molecular processes involved in disease pathology [3]. However\, these modalities are often studied separately. Here we integrated neuroimaging and omics data to investigate how molecular signatures relate to brain alterations during PD progression.\n\n\nMethods\nWe analyzed data from the Parkinson’s Progression Markers Initiative (PPMI) [4]\, a longitudinal study including de novo PD patients\, prodromal individuals\, and healthy controls. Subjects were selected if both brain imaging and molecular measurements were available\, including cerebrospinal fluid proteomics or whole-blood RNA-seq data collected at different disease stages. Differential gene and protein expression analyses were performed\, followed by functional enrichment analysis. In parallel\, quantitative features such as contrast ratios and volumes of neuromelanin-rich areas were extracted from 2D gradient echo (GRE) brain images with magnetization transfer (MT). Associations between omics and imaging-derived features were then evaluated.\n\nResults\nA subset of genes and proteins showed significant associations with imaging features reflecting brain alterations typically observed in Parkinson’s disease. These findings suggest that disease progression is reflected by both molecular and imaging signatures. Further analysis revealed that progression does not follow a simple linear trajectory but instead involves distinct stages. Moreover\, patterns of molecular and structural changes differed between sexes\, highlighting heterogeneity in disease progression and suggesting potential sex-specific mechanisms.\n\nDiscussion\nThese findings highlight the value of integrating imaging and omics data to better characterize PD progression. Linking molecular signatures with structural brain alterations may improve our understanding of disease mechanisms and support the development of multimodal biomarkers for monitoring disease evolution.\n\n\nReferences\n[1]: Kalia\, L. V.\, & Lang\, A. E. (2015). Parkinson’s disease. The Lancet\, 386(9996)\, 896–912.\n[2]: He\, H.\, et al. (2020). Progressive brain changes in Parkinson’s disease: A meta-analysis of structural magnetic resonance imaging studies. Brain Research Bulletin\, 164\, 272–279.\n[3]: Sharma\, S.\, & Dhamija\, R. K. (2025). The quest for Parkinson’s disease biomarkers: Traditional and emerging multi-omics approaches. Molecular Biology Reports\, 52\, Article 831.\n[4]: Parkinson’s Progression Markers Initiative (PPMI). https://www.ppmi-info.org\n\nAcknowledgement\nData were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org). PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:1913e7614e89e7370acb48a323448194
URL:http://cns2026.sched.com/event/1913e7614e89e7370acb48a323448194
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SUMMARY:P013: Relationships Between Connectivity and Longitudinal Memory Decline Using Network Analysis and Partial Least Squares
DESCRIPTION:Introduction\n\nThere has been recent evidence to suggest that autistic adults have higher risk of neurodegenerative disease and dementia compared to non-autistic adults [1]. Previous studies have found some age-related brain differences between autistic and non-autistic adults\, but how these differences may contribute to increased dementia risk remain unclear. We used a combination of network analysis and partial least squares to identify functional and structural connectivity patterns that correlate with long-term memory decline in both autistic (n=40) and non-autistic (n=33) adults using data from a longitudinal study.\n\n\nMethods\n\nWe obtained T1\, diffusion\, and functional MRI data. Brain networks with 96 regions of interest were constructed using the CONN [2] and TVB-UKBB [3] pipelines for functional and structural connectivity from participants’ first scan. Long-term memory change was measured using the slope of a mixed effects model for the delayed recall (A7) score of the Auditory Verbal Learning Test evaluated 2-5 times across 2-9 years of follow-up. Networks were thresholded and quantified with the Brain Connectivity Toolbox in MATLAB [4]. Network measures were residualized using age and sex as covariates. Behavioral partial least squares was used to identify multivariate correlations between network measures and long-term memory outcomes [5].\n\n\n\nResults\n\nFor the non-autistic adults\, there was a significant latent variable relationship between structural connectivity and long-term memory change\; weaker structural connectivity in classic memory regions (e.g. hippocampus) correlated with greater memory decline. The autistic adults showed significant latent variable relationships between functional connectivity and long-term memory change\; weaker\, less interconnected\, and less organized functional connectivity across the whole brain correlated with greater memory decline. For both cases\, there was a significant difference between groups for the latent variable relationship\, demonstrating differing relationships between connectivity and memory.\n\n\n\nDiscussion\nBoth the autistic and non-autistic adults showed significant relationships between connectivity and memory decline. We found that\, for the non-autistic adults\, memory decline was related to structural connectivity patterns that involved classic memory regions (e.g. hippocampus). For the autistic adults\, memory decline was related to functional connectivity patterns across the whole brain. These findings suggest the potential for unique MRI based biomarkers to identify increased risk of accelerated memory decline in autistic adults.\n\n\nReferences\n\nStarkstein\, S.\, Gellar\, S.\, Parlier\, M.\, Payne\, L.\, & Piven\, J. (2015). High rates of parkinsonism in adults with autism. J. Neurodev. Disord.\nWhitfield-Gabrieli\, S.\, & Nieto-Castanon\, A. (2012). Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain connectivity.\nFrazier-Logue\, N.\, Wang\, J.\, Wang\, Z.\, Sodums\, D.\, Khosla\, A.\, Samson\, A. D.\, ... & Shen\, K. (2022). A robust modular automated neuroimaging pipeline for model inputs to TheVirtualBrain. Front. Neuroinform.\nRubinov\, M.\, & Sporns\, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage.\nKrishnan\, A.\, Williams\, L. J.\, McIntosh\, A. R.\, & Abdi\, H. (2011). Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review. Neuroimage.\n\n\n\nAcknowledgement\nWe would like to acknowledge funding sources for our project\, the National Institute on Aging [P30 AG072980]\, the National Institute of Mental Health [R01MH132746\; K01MH116098]\, the Department of Defense [AR140105]\, and the Arizona Biomedical Research Commission [ADHS16-162413].\n\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:83593ca19336bfd643ab7c7858d9f5dc
URL:http://cns2026.sched.com/event/83593ca19336bfd643ab7c7858d9f5dc
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SUMMARY:P014: Modeling box jellyfish obstacle avoidance behavior with evolutionary optimization and small feedforward neural networks
DESCRIPTION:Introduction\nBox jellyfish (Tripedalia cystophora) are animals without a centralized brain [1]. Despite their small decentralized nervous system\, they can perform visually guided obstacle avoidance behavior (OAB) [2]\, which is crucial for survival in their natural habitat. Recent work has shown that box jellyfish are even capable of associative learning and identified the learning center to be the rhopalial nervous system (RNS) [2]. These abilities raise the question which level of neural complexity is required to perform such actions. Here we investigate the innate OAB with a minimal sensorimotor architecture including a multilayer perceptron (MLP) optimized with a biologically plausible learning algorithm not including gradient descent.\n\n\nMethods\nWe developed a rectangular two-dimensional simulation platform containing walls and obstacles with varying luminance values. Agents\, each steered by an MLP\, receive these values by a vector depending on the directional visual sensors (Fig.1) along with a physical sensor indicating a previous collision. Inputs are fed into the MLP to make the movement decision\, resulting in a movement trajectory. Agents are rewarded when randomly placed food items are retrieved and penalized for collisions\, resulting in a fitness value. Weights of the MLP are optimized by evolutionary search using fitness\, following a neuroevolutionary paradigm used for autonomous navigation and neural control systems [3\,4]. Agents are then tested in different environments.&nbsp\;\n\n\nResults\nIn different runs with various environmental and fitness conditions\, agents consistently developed OAB strategies by trying to minimize collisions while continuing to forage (Fig. 1). We study the quality of OAB when training parameters\, including training time and training arenas\, are varied and find that agents show best behavior for an intermediate amount of training time and in arenas where strong contrasts between wall elements where present. Overall\, trajectories showed qualitative and quantitative similarities to the innate behavior of true box jellyfish. In particular\, learning to avoid high-contrast objects does not lead to avoidance of objects with uniform luminosity irrespective of their distance [2].\n\n\nDiscussion\nOur results show that evolutionary training enables small MLPs to successfully control OAB in agents mimicking box jellyfish. Whereas MLPs are feedforward neural networks\, the biological RNS is a recurrent neural network [1]\, and therefore\, future work will integrate more biologically plausible neural network architectures. Furthermore\, in a next step\, we will examine how the successfully learned innate OAB leads to associative learning by using our highly customizable setup in circular arenas with differing wall contrasts\, similar to the experiments performed in [2]. Ultimately\, our results will enable us to derive minimal requirements for neural architectures underlying associative learning\, allowing for comparisons across organisms [5].\n\nExample trajectories (green) of an agent (light blue circle with blue rays indicating visual sensors) in an arena with three high-contrast obstacles. A: The agent forages where no obstacles are present. It also frequents the part of the arena with obstacles\, but never collides with them. B: Trajectory for an agent with less training times\, leading to frequent collisions (yellow dots).References\n1. Nielsen\, Sofie K.D. et al. (2021). Journal of Comparative Neurology.&nbsp\;https://doi.org/10.1002/cne.25148\n2. Bielecki\, J. et al. (2023).&nbsp\;Curr. Biol.&nbsp\;https://doi.org/10.1016/j.cub.2023.08.056\n3. Floreano\, D.\, & Mondada\, F. (1998).&nbsp\;Neural Networks. https://doi.org/10.1016/S0893-6080(98)00082-3&nbsp\;\n\n4. Whitley\, D. et al. (1993).&nbsp\;Machine Learning.&nbsp\;https://doi.org/10.1023/A:1022674030396\n5. Zhou\, Baohua et al. (2022). elife.&nbsp\;&nbsp\;https://doi.org/10.7554/eLife.72067\n\n\n\n\n\nAcknowledgement\nWe would like to thank Christoph Speckgens and Hermann Kohlstedt for helpful discussions.&nbsp\;Funded by the Deutsche Forschungsgemeinschaft (DFG\, German Research Foundation) – Project-ID 434434223 – SFB 1461.&nbsp\;
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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SUMMARY:P015: A biologically grounded spiking model of feedback-gated prediction broadcasting in cortical Layer 5
DESCRIPTION:Introduction\n\nPredictive coding proposes that cortical circuits continually compare incoming sensory signals with top-down expectations and propagate mismatches across a hierarchy to refine perception and behaviour [1\,2]. Although influential\, many existing models remain abstract and do not explain how deep cortical layers transform superficial prediction errors into broadcast predictions. They often omit spiking dynamics\, laminar specialization\, interneuron diversity\, and compartmental dendritic processing [3\,4]. Here we present a biologically grounded model of cortical Layer 5 in primary visual cortex that links signed prediction errors in Layer 2/3 to feedback-gated prediction broadcasting through dendritic coincidence and inhibitory control.\n\nMethods\n\nWe extended our hierarchical spiking predictive-coding framework by building on our previous Layer 4 sensory encoding model and Layer 2/3 prediction-error circuit [5\,6]. Layer 5 pyramidal neurons were modelled as two-compartment spiking units with somatic input from PE+ neurons\, encoding features present in feedforward input but absent from feedback\, and PE- neurons\, encoding features predicted by feedback but absent from feedforward input. Apical dendrites received top-down feedback. A local VIP-PV-SOM microcircuit gated dendritic Ca2+ spikes and burst output (Fig. 1) [3\,4\,7]. Connectivity followed Gabor-based feature tuning\, and simulations tested aligned strong\, aligned weak\, and mismatched feedback conditions.\n\nResults\n\nThe model reproduced distinct Layer 5 output regimes across conditions. When feedback was aligned with sensory evidence\, VIP-mediated disinhibition enabled apical Ca2+ spikes\, loosened dendritic excitation-inhibition balance\, and drove strong burst firing in pyramidal neurons. With aligned but weaker feedforward input\, bursting persisted at lower rates. In contrast\, mismatched feedback-maintained SOM/PV inhibition\, suppressed dendritic amplification\, and produced predominantly tonic firing. Decoding Layer 5 population activity reconstructed a refined sensory prediction by integrating complementary signals from both PE+ and PE- populations\, linking local error signals to updated top-down output.\n\nDiscussion\n\nThese findings identify Layer 5 as a conditional broadcast stage in hierarchical predictive coding\, where dendritic coincidence detection determines whether local evidence is sufficient to support a top-down prediction. By combining laminar circuit organization\, interneuron diversity\, feature-selective connectivity\, and spiking dynamics\, the model links cortical physiology with predictive computation in a mechanistic way. It also generates experimentally testable predictions about feedback-gated bursting\, dendritic coincidence\, compartment-specific inhibition\, and precision-weighted prediction in cortical circuits\, while providing a compact framework for biologically inspired and neuromorphic inference systems.\n\nLayer 5 predictive-coding microcircuit. A two-compartment Layer 5 pyramidal neuron integrates somatic input from Layer 2/3 prediction-error populations (PE+ and PE-) and Layer 4 feature neurons with apical feedback from higher cortical areas. A local VIP-PV-SOM motif regulates apical Ca2+ spikes and burst output.\n\nReferences\n\n\n Rao\, R. P. N.\, & Ballard\, D. H. (1999). Nature Neuroscience\, 2\, 79-87. Keller\, G. B.\, & Mrsic-Flogel\, T. D. (2018). Neuron\, 100\, 424-435. Hertäg\, L.\, & Clopath\, C. (2022). PNAS\, 119\, e2115699119. Mikulasch\, F. A.\, et al. (2023). Trends in Neurosciences\, 46\, 45-59. Nemati\, E.\, Davey\, C. E.\, Meffin\, H.\, & Burkitt\, A. N. (2025). bioRxiv. 10.1101/2025.10.20.683584. Nemati\, E.\, Davey\, C. E.\, Meffin\, H.\, & Burkitt\, A. N. (2025). bioRxiv. 10.1101/2025.11.01.686040. Larkum\, M. (2013). Trends in Neurosciences\, 36\, 141-151. \n\n\nAcknowledgement\n\n\nANB and HM acknowledge support by the Australian Government through the Australian Research\nCouncil’s Discovery Projects funding scheme [DP220101166].\nEN acknowledges support from a Melbourne Research Scholarship\, and the Diane Lemaire and Dee\n& John Collier Travel Scholarships at the University of Melbourne.
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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URL:http://cns2026.sched.com/event/48e327955f739e492ab294b180241157
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SUMMARY:P016: Vestibular predictions during maternal gait help shape development of neural of timekeeping
DESCRIPTION:Introduction\nHumans develop beat perception and rhythm synchronization remarkably early\, suggesting that prenatal experience may play a formative role. The neural basis behind this remains poorly understood. We propose that maternal gait during pregnancy helps shape the development of neural timekeeping by pairing rhythmic auditory events with correlated smooth vestibular input that the fetus learns to anticipate.\n\n\nMethods\nWe developed a biologically grounded recurrent neural network with parallel auditory and vestibular pathways. One version of the network contained generic excitatory and inhibitory units\; another incorporated a diversity of units modeled from cortical neurons. The models were trained via single-step backpropagation with auditory pulses paired with sinusoidal vestibular waveforms mimicking maternal locomotion. The model was trained to predict the input five timesteps in advance — representing vestibular predictions during maternal gait — across a range of tempos. Vestibular input was gradually removed as training performance improved\, encouraging the network to rely on internally generated predictions given only auditory pulses.\n\nResults\nWe explored the effects of networks incorporating multiple biologically realistic cell types\, which outperformed single-type networks on synchronization tasks. The dual auditory-vestibular architecture further improved both synchronization and continuation performance compared to either network on its own. Weighted tempo sampling\, based on training loss\, reduced drift toward preferred tempos during continuation and could represent musical training during life.\n\n\nDiscussion\nThese results demonstrate that a biologically inspired predictive network can be trained through a plausible developmental curriculum to internalize and maintain rhythmic structure across different tempos. This model offers a platform for investigating the neural basis of timekeeping and how early sensory experience — beginning in utero and refined by musical training — may scaffold the rhythm synchronization abilities universal to humans.&nbsp\;\n\n\nReferences\nYousefabadi\, M.\, & Cannon\, J. (2025). Maternal Gait Contributes To Development Of Beat Perception And Urge To Move To Music In A Predictive Processing Network Model. Zenodo. https://doi.org/10.5281/zenodo.17247501\n\nAcknowledgement\nThis research was funded in part by the Natural Sciences and Engineering Research Council of Canada\n\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:1f32bdbdf66cc1024c1a2d715f0c55ee
URL:http://cns2026.sched.com/event/1f32bdbdf66cc1024c1a2d715f0c55ee
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SUMMARY:P017: Adapting the reconstruction of the cerebellar cortex to the shape of the brain
DESCRIPTION:Introduction\nCurrent knowledge on the cellular composition and local connectivity of the cerebellar cortex has enabled the reconstruction of detailed microcircuit models [1\, 2]. However\, up to now\, none of these models take into account the real convoluted shape of the cerebellar cortex. We aim at reconstructing and simulating atlas-mapped mouse cerebellar regions\, capturing the relationship between structure\, dynamics\, and function.&nbsp\;\nWe have developed a pipeline to reconstruct the mouse cerebellar cortex embedded into the Allen Mouse Brain Atlas (AMBA) [3]. Using the Brain Scaffold Builder (BSB) framework [1]\, we placed\, oriented\, bent and connected the neurons. The generated circuit can be simulated and validated against experimental findings.\n\nMethods\nWe extracted a column of the mouse declive (vermal part of the Lobule VI) from the AMBA (Fig. 1A). We placed cells based on literature densities [1]\, including the unipolar brush cells [4]\, and proposed a new strategy to place Purkinje cells based on linear density [5] (Fig. 1D). To connect the cells\, we computed the orientation and depth [6] of each voxel (Fig. 1BC). These fields were used to bend the&nbsp\;cells’ neurites following the local curvature (Fig. 1E). We applied voxel intersection on these bended cells [1]. We assigned point-neuron electrical parameters for each cell type and synaptic parameters for each connection type [7]. We compared this model to our previous nonspecific and regular-paralleliped circuit (canonical circuit) [1].\n\nResults\nOur pipeline employed constraints for each neuron type\, and the produced circuit indeed preserved the morphological properties of the canonical circuit\, such as maintaining fibers parallel for granule cells (Fig. 1E). More importantly\, the pipeline guaranteed a coherent connectome\, which matched the synaptic convergences/divergences of the canonical circuit. We proved that\, without proper bending and scaling\, the number of synapses would be underestimated\, especially for longer intersomatic distances.\nFinally\, we simulated that circuit using the BSB interfacing with the NEST simulator [8] in resting state and under stimulus. The signal propagation and population-specific firing properties were well reproduced\, as in the canonical circuit.\n\nDiscussion\nThe developed pipeline is able to leverage atlas data to estimate the heterogeneous spatial properties of the cerebellum\, embedding them into circuit reconstructions. The atlas registration will also facilitate the integration of our model into larger brain circuits [9]. The morphology bending algorithm will be soon enhanced in order to adapt the spatial distribution of neurites to match the expected densities of fibers in the considered regions.&nbsp\;\nWe plan to leverage the Blue Brain Cell Atlas pipeline [6] to reconstruct the whole declive as well as different regions of the cerebellar cortex\, to study how the heterogeneity of their local properties gives rise to differences in their structure and function at the macroscale level. \n\nFigure 1.&nbsp\;Reconstruction pipeline. A. Declive layers shown in colors with the selected column highlighted. B. Orientation field showing the local axons’ main axis. Colors represent the vectors’ norm. C. Distance to the outside border\, following the orientation field. D. E. Purkinje and granule cells´ morphology scaled and bent according to the declive shape.​\n\nReferences\n1. https://doi.org/10.1038/s42003-022-04213-y\n2. https://doi.org/10.1038/s41598-025-25727-5\n3. https://doi.org/10.1111/j.1601-183X.2009.00552.x\n4. https://doi.org/10.1007/s00429-013-0531-9\n5. https://doi.org/10.1002/jnr.24206\n6. https://doi.org/10.1371/journal.pcbi.1010739\n7. https://doi.org/10.3389/fncom.2019.00068\n8. https://doi.org/10.4249/scholarpedia.1430\n9. https://doi.org/10.1523/ENEURO.0111-17.2017
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:ecef8ccc7aaa05b7dafd70c972b51a98
URL:http://cns2026.sched.com/event/ecef8ccc7aaa05b7dafd70c972b51a98
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SUMMARY:P018: Digital Twins in stroke and spreading depression
DESCRIPTION:Introduction\nMultiscale modeling (MSM) permits us to better understand how ischemia and spreading depolarization (SD) together damage neurons. However\, it is a long step from there to producing clinical tools to counter or prevent stroke. Such tools must include clinical data from neurology and cardiology\, endocrinology and other clinical specialties\, as well as from other biomedical sciences\, and must engage with body sensors (data integrators -DIs -for sensor information consolidation) and with the patient him or herself. We are developing digital twins (DTs) to incorporate these elements to extend personalized care. DTs will incorporate MSMs and DIs with large language models (LLMs) to communicate with the patient and with clinicians.\n\n\nMethods\nWe have developed LLM to interact with patients and now combine them with our MSMs that include neural and vascular elements. MSM simulates & constrains detailed reaction--diffusion\, electrophysiolo- gy\, circuit models. LLM correlates literature and simulation details to identify simulation boundaries.\n\n\nResults\n\nDiscussion\nDT medical personalization can help distinguish multiscale parameters\, enabling patient-specific predictions and suggest therapy testing. Pairing of MSM detailed models with LLMs allows ingesting large electronic medical record (EMR) and archival research text to structured knowledge\, further augmented with DI access to personal (digital watch and monitors) and clinical tools. Brain ischemia is a bridge disease since mutli-organ (cardiac\, brain\, vessel\, lung) \; detailed clinical correlates and preventive strategies. microscale\; multi-physics\;&nbsp\; multi-specialty: neurology\, vascular\, cardiac\, endocrine.\n\n\nReferences\nnone\n\nAcknowledgement\nSupported by NIH&nbsp\;R01MH086638\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:5fac7ea72b01eaa45afb46aa74bb3c1d
URL:http://cns2026.sched.com/event/5fac7ea72b01eaa45afb46aa74bb3c1d
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SUMMARY:P019: A Unified Deep Oscillatory Network Model of the Hippocampal Sharp Wave Ripples
DESCRIPTION:Introduction\nThe well-known link between neural dynamics of spatial navigation and hippocampus is reflected in characteristic phenomena like neurons encoding spatial and temporal variables\, and oscillatory dynamics such as phase precession in locomotion and sharp-wave ripples at rest. Existing computational models like oscillatory interference models\, continuous attractor network and deep learning models either account for oscillatory behaviors or spatial coding within a rate-coded framework\, capturing only a subset of features not addressing rest or temporal dynamics [1\,2\,3].&nbsp\;We propose an oscillatory hippocampus model that comprehensively captures these constructs\, providing a unified framework to study translation of neural activity into navigation.\n\n\nMethods\nA complex valued deep oscillatory neural network is trained to estimate position coordinates of a 1D trajectory from limb oscillations and environmental visual cues of a quadruped (animal) that alternates between motion and rest [4].&nbsp\;The oscillatory layers in the network include a central layer with an intrinsic theta band (4-8 Hz) enabling study of hippocampal spatial navigation. The network’s complex hidden layer activations are analyzed to study the encoding of spatiotemporal information. Statistic measures are applied to the mean firing rates across spatial and temporal bins to identify place and time cells. Oscillatory&nbsp\;behaviors&nbsp\;are shown using&nbsp\;Hebbian learning and regression analyses on the complex oscillatory layer activations.\n\n\nResults\nPlace cells identified from the complex activations were found to tile the traversed trajectory. Time cells were observed to encode elapsed time during the task\, independent of state of motion or rest. Position and velocity were encoded through oscillator population dynamics - position reflected in the mean phase and velocity in the mean frequency of the oscillator population. Sharp-wave ripple–like events generated via Hebbian learning exhibited higher amplitudes at periods of rest\, indicating increased synchrony among oscillators. These findings are consistent with existing experimental observations\, offering new insights into how spatiotemporal information can be represented through the joint encoding of frequency\, phase\, and amplitude.\n\n\nDiscussion\nSpatial and temporal representations emerged naturally as the model learned to map sensorimotor inputs to position. Rate-coded properties were evident at the level of individual neurons\, and oscillatory phenomena at the level of neuronal populations. The internal oscillatory dynamics are interpretable through the parameters of amplitude\, phase and frequency. These results suggest that the proposed model offers a unified framework that can capture spatiotemporal representations during motion and rest. Its ability to encode information in interpretable oscillatory&nbsp\;variables enables investigation of broader hippocampal functions - navigation\, associative memory\, and working memory\, across diverse task structures and environmental conditions.\n\n​Figure 1.&nbsp\;(a) Model Flowchart\, (b) Input Data\, (c) Oscillatory Neural Network Diagram\, (d) Trajectory Prediction\, (e) Place Cells - different colors correspond to different neurons\, (f) Sharp Wave Ripples​\n\nReferences\n1.&nbsp\;O’Keefe\, J.\, & Recce\, M. L. (1993). Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus\, 3(3)\, 317–330.https://doi.org/10.1002/hipo.450030307\n2.&nbsp\;Burgess\, N.\, Barry\, C.\, & O’Keefe\, J. (2007). An oscillatory interference model of grid cell firing. Hippocampus\, 17(9)\, 801–812.https://doi.org/10.1002/hipo.20327\n3.&nbsp\;Buzsáki\, G. (2015). Hippocampal sharp wave–ripple: A cognitive biomarker for episodic memory and planning. Hippocampus\, 25(10)\, 1073–1188.https://doi.org/10.1002/hipo.22488\n4.&nbsp\;Rohan\, N. R.\, Vigneswaran\, C.\, Ghosh\, S.\, Rajendran\, K.\, Gaurav\, A.\, & Chakravarthy\, V. S. (2025). Deep oscillatory neural network.&nbsp\;Scientific Reports\,&nbsp\;15(1)\, 40968.\n\nAcknowledgement\nMy supervisor Prof. V. Srinivasa Chakravarthy\,&nbsp\;\nMentors from Computational Neuroscience Lab and the Dept. of Medical Sciences and Technology\nMore importantly\, my parents.
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SUMMARY:P020: Prefrontal and Parietal Local Field Potentials Employ Different Visuospatial Codes for Reach: A Complex-Valued Network Classification Approach
DESCRIPTION:Introduction\nUnderstanding how cortical oscillations coordinate spatial memory and motor planning is a central challenge in systems neuroscience. We tested whether phase–amplitude dynamics in cortical local field potentials (LFPs) encode distributed versus region-specific signals for spatial memory and planning under varying visuospatial conditions.\n\n\nMethods\nWe developed a Complex-Valued Neural Network (CVNN) model [1\, 2]&nbsp\;to decode landmark-dependent spatial states from LFPs recorded in the posterior ventrolateral prefrontal cortex (pVLPFC\, 128 channels) and intraparietal sulcus (IPS\, 32 channels) of a female rhesus monkey performing memory-guided reaching tasks in which visual landmarks were stable\, shifted 8° in one of eight directions\, or absent&nbsp\;[3\, 4]. Preprocessed LFPs were transformed into complex-valued time series using the Hilbert transform to preserve phase and amplitude information&nbsp\;[5].\n\n\nResults\nWe trained separate CVNN models on IPS or pVLPFC signals which classified the three landmark conditions with &gt\;90% training accuracy and more than 51% overall validation accuracy\, significantly above chance (33%). However\, validation performance revealed inter-regional specialization: the IPS model performed best for no-landmark trials (88.35% ± 6.99)\, whereas the pVLPFC model showed superior performance for shifted-landmark trials (71.73% ± 8.59). We then trained dual-stream models combining pVLPFC and IPS recordings. The single-region results were confirmed via region occlusion analysis after training:&nbsp\;removing pVLPFC improved no-landmark classification\, while removing IPS improved shifted-landmark classification.\n\n\nDiscussion\nThese findings suggest that IPS specializes in maintaining spatial representations for reach plans in egocentric coordinates\, whereas pVLPFC shows enhanced encoding in the presence of visual landmarks\, especially in the dynamic landmark-shift conditions\, indicating complementary computational roles in maintaining and updating spatial representations for reach.\n\n\nReferences\nN. Benvenuto and F. Piazza\, "On the complex backpropagation algorithm\," in IEEE Transactions on Signal Processing\, vol. 40\, no. 4\, pp. 967-969\, April 1992\, doi: 10.1109/78.127967.\nG. M. Georgiou and C. Koutsougeras\, "Complex domain backpropagation\," in&nbsp\;IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing\, vol. 39\, no. 5\, pp. 330-334\, May 1992\, doi: 10.1109/82.142037.Lin\, J.\, Wang\, H.\, Sun\, S.\, Yan\, X.\, & Crawford\, J. D. (2023). Influence of a visual landmark shift on memory-guided reaching in monkeys.&nbsp\;Journal of Vision\,&nbsp\;23(9)\, 4828–482&nbsp\;https://doi.org/10.1167/jov.23.9.4828.Lin\, J. Y. X. (2024).&nbsp\;Influence of a visual landmark shift on memory-guided reaching in the monkey.Freeman\, W. J. (2007). Hilbert transform for brain waves. Scholarpedia\, 2(1)\, 1338.\nAcknowledgement\nThis research was funded by the Connected Minds Program\, supported by the Canada First Research Excellence Fund.\n\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:6f9c0dcf13c9bcce3d22566b702d2752
URL:http://cns2026.sched.com/event/6f9c0dcf13c9bcce3d22566b702d2752
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SUMMARY:P021: Local dendritic voltage provides a reliable read-out of global synaptic activity
DESCRIPTION:Introduction\nNeurons receive synaptic inputs across a spatially extended dendritic tree [1]. Recent work has shown that neuronal excitability is independent of the size of the dendritic tree when distributed dendritic\, instead of somatic\, inputs are considered [2]. Such dendritic normalisation has also been shown to improve the speed and robustness of learning [3]. The question remains\, however\, whether the same principle applies to local dendritic voltages across the entire neuron\, and whether this might be computationally useful.\n \n\n \n\nMethods\nWe derive analytical results using the cable equation [4] in passive dendritic structures\, and validate our results using simulations of passive and active cells\, including detailed and biophysically validated multicompartmental models\, in the Matlab Trees Toolbox package [5]\, T2N [6]\, and the NEURON environment [7].\n\nResults\nWe first show analytically that the steady state voltage response of a dendritic cable receiving distributed inputs is completely independent of dendrite size and measurement location\; a dendrite acts like a ‘bucket’ filling with synaptic ‘water’. We investigate how far perturbations due to stochastic inputs impact the ‘bucketness’ of a cell\, and find that the local dendritic voltage at every location in the dendrite typically reflects the strength of global inputs. We confirm that calcium concentrations are much longer-lived and more local than voltages. We finally show that the interaction between calcium and voltage could provide a substrate for robust learning by reinterpreting long-term plasticity rules [8\,9].\n\nDiscussion\nDendritic voltages are surprisingly global and quickly equalise deviations in synaptic inputs. In contrast\, calcium transients can provide a long-lived record of local afferents. The interplay between these two indicators provides a continuous\, biophysically grounded\, learning signal at every point in a dendritic tree. Our results provide a foundation for further studies into the many ways dendrites provide a space for complex computations at the single neuron level.\n\nReferences\n1.\tChklovskii D. Neuron. 2004\;43(5):609–17. 10.1016/j.neuron.2004.08.012\n 2.\tCuntz H\, Bird A\, et al. Neuron. 2021\;109(22):3647-3662.e7. 10.1016/j.neuron.2021.08.028 PMID: 34555313.\n 3.\tBird AD\, Jedlicka P\, Cuntz H. PLOS Comp Bio. 2021\;17(8):e1009202. 10.1371/journal.pcbi.1009202\n 4.\tRall W. Ann NY Acad Sci. 1962\;96(4):1071–92. 10.1111/j.1749-6632.1962.tb54120.x\n 5.\tCuntz H\, Forstner F\, et al. PLOS Comp Bio. 2010\;6(8):e1000877. 10.1371/journal.pcbi.1000877\n 6.\tBeining M\, Mongiat L\, et al. eLife. 2017\;6:e26517. 10.7554/eLife.26517\n 7.\tHines M\, Carnevale N. Neural Comput. 1997\;9(6):1179–209. PMID: 9248061.\n 8.\tBienenstock EL\, Cooper LN\, Munro PW. J Neurosci. 1982\;2(1):32–48. 10.1523/JNEUROSCI.02-01-00032.1982 PMID: 7054394.\n 9.\tOja E. J Math Biology. 1982\;15(3):267–73. 10.1007/BF00275687\n\nAcknowledgement\nNone.\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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URL:http://cns2026.sched.com/event/660d859e471490a69725240a3f04935a
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SUMMARY:P022: Electrophysiological and computational analysis of burst generation in Drosophila class III cold nociceptors
DESCRIPTION:Introduction\nIn Drosophila larvae\, Class III (CIII) primary sensory neurons detect nociceptive cold temperatures\, with about half responding to rapid cooling with transient bursting (1\,2). Cold responses have been linked to activation of thermosensitive TRP channels\, including TRPM\, PKD2\, and NOMPC (1\,3). We previously showed that lowering extracellular Cl⁻ enhances spiking and promotes bursting in CIII neurons\, consistent with a depolarizing shift of the Cl⁻ reversal potential. Here\, we test whether pharmacological or ionic perturbations that produce appropriate membrane depolarization are sufficient to create bursting mechanisms in CIII neurons at room temperature\, revealing a mechanism that does not rely on TRP channel activation.\n\nMethods\nIntracellular recordings were obtained from CIII neurons in Drosophila larvae under pharmacological and ionic manipulations. Experimental conditions included reduced extracellular Cl⁻ (6 mM\; 134 mM control)\, elevated extracellular K⁺ (15 mM\; 3 mM control)\, Ca²⁺ removal\, and tetrodotoxin (TTX\, 20 nM) to block voltage-gated Na⁺ channels. Direct current injection was used to characterize transitions between silence\, spiking\, and bursting across conditions. In parallel\, a biophysical computational model of the CIII neuron was developed and constrained by experimental measurements. Model parameters were tuned to reproduce passive electrical properties and validated by comparison with experimentally observed activity patterns.\n\nResults\nIn control\, current injection&nbsp\;(5–20 pA) produced tonic spiking. In low-Cl⁻ saline\, the same stimulation&nbsp\; induced bursting in 90% of neurons (18/20). Elevated extracellular K⁺ promoted bursting in all neurons examined (6/6)\, indicating that global depolarization facilitates burst generation. Removal of extracellular Ca²⁺ did not eliminate bursting\, suggesting that Ca²⁺ influx is not strictly required for burst generation under these conditions. In contrast\, tetrodotoxin (20 nM) abolished both spikes and the underlying depolarizing potentials. Biophysical modeling reproduced these transitions and suggested that the voltage-gated Na⁺ current plays a prominent role in sustaining the depolarizing envelope supporting burst generation.\n\nDiscussion\nThese results demonstrate that CIII neurons can generate the full spectrum of activity patterns—silence\, tonic spiking\, and bursting—without activation of thermosensitive TRP channels. Depolarizing manipulations such as reduced extracellular Cl⁻\, elevated K⁺\, or current injection reliably promoted bursting. These findings suggest that practically all CIII neurons are intrinsically burst-capable when operating within an appropriate depolarized regime. Biophysical modeling reproduced the observed transitions and dissected the contributions of ionic gradients and membrane conductances\, providing a mechanistic framework in which Na⁺ channel dynamics contribute prominently to the generation of bursting activity.\n\nReferences\n1. Turner\, H. N.\, et al. (2016). The TRP Channels Pkd2\, NompC\, and Trpm Act in Cold-Sensing Neurons to Mediate Unique Aversive Behaviors to Noxious Cold in Drosophila. Current Biology\,&nbsp\; 26(23): 3116-3128. https://doi.org/10.1016/j.cub.2016.09.038\n2. Maksymchuk\, N.\, et al. (2022). Transient and Steady-State Properties of Drosophila Sensory Neurons Coding Noxious Cold Temperature. Frontiers in Cellular Neuroscience\,16\, 831803. https://doi.org/10.3389/fncel.2022.831803\n3. Himmel\, N. J.\, et al.\, (2023). Chloride-dependent mechanisms of multimodal sensory discrimination and nociceptive sensitization in Drosophila. eLife\, 12\, e76863. https://doi.org/10.7554/eLife.76863\n\nAcknowledgement\nNIH grant R01NS115209 to DNC and GSC.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:a02d1d857f5cee1e334cac30e256301c
URL:http://cns2026.sched.com/event/a02d1d857f5cee1e334cac30e256301c
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SUMMARY:P023: Low-Dimensional Projections of Neural Population Activity in M1\, PMd\, and PMv Hand Representations Demonstrate Differences in Effector Dependence
DESCRIPTION:Introduction\nThe activity of the primary motor cortex (M1) across stages of a motor task evolves through attractors that capture the dominant activity when preparing and executing a task [1\, 2]. This suggests that dimensionality reduction (DR) plays a key role in how M1 controls movements. In primates\, in addition to M1\, motor commands are generated by a network of frontal areas\, the premotor cortex. Neurons in the premotor ventral (PMv) and dorsal (PMd) cortices discharge in relation to various parameters of movements and send projections to M1 [3]. We extend DR techniques to PMv\, PMd\, and M1 to characterize variation in neural population activity in context of reaching and grasping movements and identify the most explanatory neurons in these cortices.\n\n\nMethods\nOur data was collected from four rhesus macaque monkeys implanted with microelectrode arrays in the distal (hand) representation of M1\, PMv\, and PMd. We recorded isolated neurons spiking activity while monkeys performed a custom-made reach-to-grasp task. Following instruction cues\, they reached with their left or right arm to grab a pellet or press on a plate using precision grasps in a vertical or horizontal orientation. For each neuron\, we computed spike density estimates (SDE) by splicing peri-event windows and normalizing across all trials (like in [4]) for each hand-orientation combination. The condition-wise SDEs of all neurons were concatenated along the time dimension to perform principal component analysis (PCA) for each cortex.\n\n\nResults\nPCA of the neural population activity in each cortex demonstrates differences across conditions. For each cortex\, the first 3 principal components capture over 90% of the variance of the neural population dynamics. Low-dimensional trajectories of neural population activity in M1 shows greater divergence in neural activity when varying the hand used than varying target orientation. However\, these low-dimensional trajectories across conditions are more similar for the premotor areas\, with PMv having the most similarity. Moreover\, the principal angles between the subspaces of the principal components for the hand used show that the neuron weights are more consistent for PMv\, demonstrating less effector dependence in PMv than in PMd or M1.\n\n\nDiscussion\nThe principal components (PCs) in each cortex indicate the weight assigned to each neuron which yields a sorting based on the explainability of the population dynamics. Combining this sorting with independent classification techniques of individual neurons allows for selection and classification of the most important neuron types in a population. Meanwhile\, the differences in effector dependence and principal angles between M1\, PMd\, and PMv suggest a hierarchical structure of signals. Effector-independent PMv activity may structure the common movement parameters before PMd facilitates the more effector-dependent preparation. Low-dimensional representations such as PCA could explain this structure through the coupling of PCs across cortices.\n\n\nReferences\n[1] Churchland\, M. M.\, et al. Stimulus onset quenches neural variability: a widespread cortical phenomenon. Nature neuroscience\,&nbsp\;13(3)\, 369–378. https://doi.org/10.1038/nn.2501\n[2] Davare\, M.\, et al. Dissociating the role of ventral and dorsal premotor cortex in precision grasping. The Journal of neuroscience\,&nbsp\;26(8)\, 2260–2268. https://doi.org/10.1523/JNEUROSCI.3386-05.2006 \n[3] Shenoy\, K. V.\, Sahani\, M. et Churchland\, M. M. (2013). Cortical Control of Arm Movements: A Dynamical Systems Perspective. Annual Review of Neuroscience\, 36\, 337–359. https://doi.org/10.1146/annurev-neuro-062111-150509\n[4] Zimnik\, A. A.-O.\, et al. Identifying Interpretable Latent Factors with Sparse Component Analysis. bioRxiv: the preprint server for biology\, https://doi.org/10.1101/2024.02.05.578988\n\nAcknowledgement\nThis research was supported by CIHR Grant No. 175069 and the FRQNT Strategic Clusters Program (Centre UNIQUE - Centre de recherche Neuro-IA du Québec).\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:10015269332c5c1e7ba6fe6cebd99bc8
URL:http://cns2026.sched.com/event/10015269332c5c1e7ba6fe6cebd99bc8
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SUMMARY:P024: Shared-input structure determines functional connectivity in neural oscillator networks
DESCRIPTION:Introduction\nFunctional connectivity (FC) describes statistical dependencies between the activity of neurons or groups of neurons&nbsp\;[1]. Comparing FC with anatomical connectivity (SC) has&nbsp\;emerged&nbsp\;as a promising avenue to study how brain structure supports function&nbsp\;[1\,2].&nbsp\;Studies have reported a wide range of&nbsp\;SC–FC correspondence values&nbsp\;[1\,2]\, highlighting the need for theoretical insights into&nbsp\;these relationships&nbsp\;[3].&nbsp\;We derive here a closed-form analytical mapping from&nbsp\;SC to FC&nbsp\;for&nbsp\;any&nbsp\;oscillator network\,&nbsp\;showing that shared presynaptic inputs govern coactivity and&nbsp\;identifying&nbsp\;an optimal&nbsp\;regime&nbsp\;of maximal&nbsp\;SC–FC alignment\, with a lower&nbsp\;bound on&nbsp\;SC&nbsp\;reconstruction&nbsp\;from FC.&nbsp\;An empirical whole-brain larval zebrafish connectome&nbsp\;is used for validation&nbsp\;[4\,5].\n\n\nMethods\nWe develop an analytical framework linking SC to FC in coupled neural systems. We study&nbsp\;coupled&nbsp\;neural oscillators on a heterogeneous\,&nbsp\;weighted&nbsp\;and directed network using the Kuramoto model&nbsp\;[3]. A second-order perturbative expansion is obtained in the reduced coupling&nbsp\;λ/N&nbsp\;around the uncoupled regime\, valid for arbitrary network size and topology. Time-averaged correlations are&nbsp\;expanded&nbsp\;and a stationary filter&nbsp\;identifies&nbsp\;finite contributions as&nbsp\;T→∞&nbsp\;(Fig. 1a). Averaging over intrinsic frequencies drawn from a Cauchy–Lorentz distribution&nbsp\;of&nbsp\;width&nbsp\;γ&nbsp\;yields a closed-form prediction of FC. The coupling strength&nbsp\;λ*&nbsp\;maximising&nbsp\;SC–FC alignment is obtained analytically by&nbsp\;minimising&nbsp\;a&nbsp\;normalised&nbsp\;Frobenius distance between predicted and simulated FC.\n\n\nResults\nFunctional connectivity is&nbsp\;determined&nbsp\;by shared presynaptic inputs and not by direct synaptic connections. The stationary expansion&nbsp\;retains&nbsp\;only a second-order structure proportional to&nbsp\;KKᵀ&nbsp\;(Fig. 1b)\, yielding&nbsp\;Ĉ = I + (5λ²)/(4γ²N²)&nbsp\;·&nbsp\;(KKᵀ&nbsp\;-&nbsp\;diag(KKᵀ)).&nbsp\;First-order terms cancel\, so direct connections do not contribute to FC\, and the first anatomical fingerprint appears through shared-input structure. The&nbsp\;optimal&nbsp\;coupling&nbsp\;λ*\, derived solely from&nbsp\;K\, defines a theoretical lower bound on SC–FC reconstruction error (Fig.&nbsp\;1c). Simulations on an empirical whole-brain larval zebrafish connectome&nbsp\;[4] show&nbsp\;an excellent&nbsp\;agreement between predicted and simulated FC (cosine similarity&nbsp\;≥ 0.97) across the valid coupling regime&nbsp\;(Fig.&nbsp\;1d).\n\n\nDiscussion\nOur closed-form expression reveals three results not accessible from simulation alone. First\, stationary coactivity is&nbsp\;determined&nbsp\;by shared presynaptic inputs rather than by direct synaptic connections. Second\, direct connections do not contribute to stationary coactivity in the canonical Kuramoto model\, cautioning against using raw FC as a direct estimator of SC. Third\, the explicit&nbsp\;prefactor&nbsp\;5/(4γ²)&nbsp\;obtained through a non-trivial analytical derivation\, reveals that broader intrinsic-frequency dispersion weakens the structural imprint on FC\, making reconstruction of SC from FC harder in heterogeneous neural populations.\n\nFigure 1.&nbsp\;Predicting coactivity from anatomy in neural oscillators. (a) Derivation of predicted functional connectivity: phase trajectories are expanded\, correlations averaged\, and stationary terms selected. (b) Example for N=2 oscillators. (c) SC–FC reconstruction error follows theory up to synchronization (λc = 2.32). (d) Predicted and simulated FC remain highly similar (cosine similarity ≥ 0.97).​\n\nReferences\n[1] Fotiadis\, P.\, et al. (2024). Structure–function coupling in macroscale human brain networks.&nbsp\;Nature Reviews Neuroscience\,&nbsp\;25(10)\, 688–704.\n[2] Zamani&nbsp\;Esfahlani\, et al. (2022). Local structure-function relationships in human brain networks across the lifespan.&nbsp\;Nature Communications\,&nbsp\;13(1)\, 2053.\n[3] Pope\, M.\, et al. (2021). Modular origins of high-amplitude&nbsp\;cofluctuations&nbsp\;in fine-scale functional connectivity dynamics.&nbsp\;Proceedings of the National Academy of Sciences\,&nbsp\;118(46)\, e2109380118.\n[4] Kunst\, M.\, et al. (2019). A&nbsp\;Cellular-Resolution&nbsp\;Atlas of the Larval Zebrafish Brain.&nbsp\;Neuron\,&nbsp\;103(1)\, 21-38.e5.\n[5] Légaré\, A.\, et al. (2025). Structural and genetic determinants of zebrafish functional brain networks.&nbsp\;Science Advances\,&nbsp\;11(28)\, eadv7576.\n\n\nAcknowledgement\nWe thank Benjamin Claveau\, Antoine Légaré and Vincent Thibeault for helpful discussions\, and Paul De Koninck’s lab for generating the data that&nbsp\;initiated&nbsp\;this project.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:44346f5d0e200fe098bd5b8768804583
URL:http://cns2026.sched.com/event/44346f5d0e200fe098bd5b8768804583
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SUMMARY:P025: A Conductance-Based Whole-Brain Modeling Framework for Isolating Pharmacological Effects on Excitation-Inhibition Dynamics
DESCRIPTION:Introduction\nThe excitation-inhibition (E:I) ratio is a key biomarker in psychiatric conditions\, and can be modulated by pharmacological interventions. Ketamine\, an NMDA receptor antagonist\, blocks NMDA receptors on inhibitory neurons\, driving cortical disinhibition. Electrophysiologically\, ketamine reduces the mismatch negativity (MMN) signal\, which is a measure of sensory surprise within the brain's predictive coding framework [1]. Connectome-based neural-mass models excel at linking macroscopic electrophysiology to microcircuit mechanisms. Here\, we extend a conductance-based neural mass model into a whole-brain framework to validate its capacity to capture ketamine's specific effects on NMDA receptor dynamics.\n\n\nMethods\nWe modeled a previously published EEG dataset from 19 subjects recorded during a roving auditory MMN task under placebo and ketamine conditions [2] using a whole-brain modeling framework. We parcellated the brain into 200 distinct regions using Schaefer atlas. We developed an extension of the conductance-based neural-mass model introduced in [3] to simulate voltage (v) and gating (g) for AMPA\, GABA\, and NMDA receptors across pyramidal\, excitatory\, and inhibitory populations in the parcellated regions. We computed normalized gating by voltage interactions and applied principal component analysis (PCA) across different conditions to isolate and compare dominant temporal trajectories between pharmacological interventions.\n\n\nResults\nAnalysis of the primary temporal trajectories (PC1) revealed distinct activation profiles across AMPA\, GABA\, and NMDA receptors following stimulus onset. Under placebo conditions\, the network exhibited a robust MMN response. This was particularly evident in the normalized integrated synaptic activity (NMDA+AMPA+GABA)\, which produced a prominent deflection in both excitatory and pyramidal populations. Administration of ketamine markedly attenuated this effect across these key populations. Decomposing these network-level changes by receptor type demonstrated that ketamine primarily blunted the differential activation within GABA and NMDA signaling pathways.\n\n\nDiscussion\n\nReferences\nGarrido\, M. I.\, Kilner\, J. M.\, Stephan\, K. E.\, & Friston\, K. J. (2009). The mismatch negativity: A review of underlying mechanisms. Clinical Neurophysiology\, 120(3)\, 453–463. https://doi.org/10.1016/j.clinph.2008.11.029&nbsp\;\nSchmidt A\, Bachmann R\, Kometer M\, Csomor PA\, Stephan KE\, Seifritz E\, & Vollenweider FX. (2012). Mismatch negativity encoding of prediction errors predicts S-ketamine-induced cognitive impairments. Neuropsychopharmacology\, 37(4)\, 865–875. https://doi.org/10.1038/npp.2011.261&nbsp\;\nMarreiros\, A. C.\, Kiebel\, S. J.\, Daunizeau\, J.\, Harrison\, L. M.\, & Friston\, K. J. (2009). Population dynamics under the Laplace assumption. NeuroImage\, 44(3)\, 701–714. https://doi.org/10.1016/j.neuroimage.2008.10.008&nbsp\;\n\nAcknowledgement\nWe acknowledge the support of Canadian Institute of Health Research (CIHR-Project Grant) and Swiss Neuromatrix Foundation.\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:ed9d4ace7b64d3f162d1aac5dc6ec7e0
URL:http://cns2026.sched.com/event/ed9d4ace7b64d3f162d1aac5dc6ec7e0
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SUMMARY:P026: Compartment-specific calcium dynamics drive local\, co-dependent excitatory and inhibitory plasticity in cortical networks
DESCRIPTION:Introduction\nLearning requires neural circuits to remain adaptable while preserving learned representations—a fundamental trade-off known as the plasticity-stability dilemma. Dendritic arbors equipped with compartment-specific inhibition support local gating of excitatory plasticity\, allowing multiple input streams to be integrated independently within a single neuron\, without disrupting existing knowledge [1]. Co-dependent excitatory and inhibitory plasticity has been shown to account for quick\, stable\, and long-lasting memory storage in biological networks [2]. However\, this co-dependence has been formalized through phenomenological spike-timing rules\, leaving the underlying biophysical mechanisms unspecified.\n\n\nMethods\nMotivated by its central role in dendritic integration and long-term plasticity\, we hypothesized that intracellular calcium orchestrates the local induction of excitatory and inhibitory plasticity. We extended a three-compartment cortical pyramidal cell model to include compartment-specific calcium dynamics from distinct sources (back-propagating action potentials\, voltage-gated calcium channels\, and NMDA receptors) and implemented co-dependent excitatory and inhibitory learning rules based on the calcium control hypothesis [3]\, driven by a shared local calcium signal. We embedded our augmented neuron model into a canonical cortical microcircuit model with cell type-specific connectivity and compartment-specific\, differential inhibition.\n\n\nResults\nOur calcium-based learning rules yielded balanced networks with enhanced memory capacity and robustness to noise and continual learning. We identified compartment-specific fixed points for excitation-inhibition balance. Targeted perturbation of compartment-specific calcium dynamics resulted in selective memory retrieval with transient disruption of the local excitation-inhibition balance.\n\n\nDiscussion\nOur findings support a biophysically plausible role for calcium compartmentalization in coordinating excitatory and inhibitory plasticity through local heterosynaptic interactions. The compartment-specific excitation-inhibition fixed points likely arise from the locality of calcium signals and their distinct sources\, providing mechanistic insight into how cortical networks achieve compartment-specific control of learning-induced plasticity. Altogether\, these results bridge synaptic biophysics and network-level computation while generating generalizable principles to inform the development of more efficient\, biologically grounded adaptive systems.\n\n\nReferences\n1. Yang\, G. R.\, Murray\, J. D.\, & Wang\, X.-J. (2016). A dendritic disinhibitory circuit mechanism for pathway-specific gating. Nature Communications\, 7(1)\, 12815. https://doi.org/10.1038/ncomms12815\n2. Agnes\, E. J.\, & Vogels\, T. P. (2024). Co-dependent excitatory and inhibitory plasticity accounts for quick\, stable and long-lasting memories in biological networks. Nature Neuroscience\, 27(5)\, 964–974. https://doi.org/10.1038/s41593-024-01597-4\n3. Graupner\, M.\, & Brunel\, N. (2012). Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern\, rate\, and dendritic location. Proceedings of the National Academy of Sciences\, 109(10)\, 3991–3996. https://doi.org/10.1073/pnas.1109359109\n\nAcknowledgement\nThis work was supported by national funds through FCT—Foundation for Science and Technology\, I.P.\, under the project HetSyn (2023.13758.PEX).\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:5b948bad66d075250c66be985f6742ac
URL:http://cns2026.sched.com/event/5b948bad66d075250c66be985f6742ac
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SUMMARY:P027: Adaptive Homeostasis: Coupling Synaptic Plasticity\, Homeostatic Scaling\, and Intrinsic Plasticity
DESCRIPTION:Introduction\n\nSynaptic plasticity underlies learning and memory. To prevent instability from unconstrained Hebbian modifications\, neurons engage homeostatic processes to globally adjust synaptic weights and membrane excitability [1]. Despite co-occurring\, Hebbian and homeostatic plasticity are traditionally modeled independently\, leaving their molecular crosstalk unresolved [2]. Recent evidence identifies stargazin\, a TARP\, as a critical link: it undergoes phosphorylation during long-term potentiation (LTP) and dephosphorylation during homeostatic downscaling [3]\, and interacts with Kv7.2 subunits to modulate intrinsic excitability [4]. No existing model unifies this coupling across scales\; we present a multi-resolution framework that bridges this gap.\n\nMethods\nWe developed a multi-scale model extending resource competition principles [5] from single synapses to the whole neuron. At the biophysical level\, calcium-dependent competition between kinases and phosphatases governs stargazin phosphorylation\, which regulates AMPAR trafficking across synapses and Kv7.2 surface expression. A reduced formulation eliminates fast variables (calcium quasi-steady-state\, AMPAR equilibrium) to yield a tractable per-synapse/per-branch description. A conceptual model further condenses dynamics into three coupled variables (weights\, resources\, excitability)\, enabling investigation of network-level consequences. \n\n\nResults\n\nSimulations reproduced the timescale separation between fast calcium transients\, rapid LTP-driven AMPAR insertion\, and gradual resource-constrained downscaling (Fig. 1). The biophysical model produces three compensatory tiers: AMPAR redistribution via pool competition (seconds–minutes)\, M-current adjustment via Kv7.2 trafficking (hours)\, and synaptic scaling via stargazin pool dynamics (days). Under 48-hour TTX and bicuculline protocols\, the model reproduces bidirectional scaling consistent with experimental data [1]. The reduced and conceptual models preserve quantitative accuracy with fewer variables\, enabling network-level investigation.\n\n\n\nDiscussion\n\nOur findings provide a biophysical account of how neurons maintain stability while preserving the capacity for input-specific memory allocation. The three-tier model hierarchy (from molecular cascades to analytically tractable abstraction) enables both detailed validation against experimental data and the investigation of how homeostasis interacts with ongoing learning dynamics in network settings. The model highlights the necessity of multi-scale molecular crosstalk\, positioning stargazin as a core integrator of synaptic plasticity and multi-scale homeostasis..&nbsp\; Embedding the conceptual model in recurrent circuits allows us to investigate how this multi-scale\, compartmentalized integration constrains learning and computation.&nbsp\;\n\nFigure 1.&nbsp\;Simulated homeostatic response to inactivity (TTX). (A) Multiplicative synaptic scaling: upscaling (1.75×) and downscaling (0.22×) at 48 h. (B) Stargazin phosphorylation (φ̄_stg) lags the homeostatic target (φ_target) due to enzymatic inertia. (C) Three compensatory tiers emerge sequentially: AMPAR redistribution (seconds–min)\, Kv7.2 adjustment (hours)\, and synaptic scaling (days).​\n\nReferences\n\n[1] Turrigiano\, G. G.\, Leslie\, K. R.\, Desai\, N. S.\, Rutherford\, L. C.\, & Nelson\, S. B. (1998). Activity-dependent scaling of quantal amplitude in neocortical neurons. Nature\, 391(6670)\, 892-896. \n[2] Turrigiano\, G. G. (2017). The dialectic of Hebb and homeostasis. Philosophical Transactions of the Royal Society B\, 372(1715)\, 20160258.\n&nbsp\;[3] Louros\, S. R.\, Caldeira\, G. L.\, & Carvalho\, A. L. (2018). Stargazin Dephosphorylation Mediates Homeostatic Synaptic Downscaling of Excitatory Synapses. Frontiers in Molecular Neuroscience\, 11\, 328.&nbsp\;\n[4] Rodrigues\, M. V.\, et al. (2024). Type I TARPs regulate Kv7.2 potassium channels and susceptibility to seizures. bioRxiv.\n[5] Triesch\, J.\, Vo\, A. D.\, & Hafner\, A. S. (2018). Competition for synaptic building blocks shapes synaptic plasticity. eLife\, 7\, e37836.\n\n\n\nAcknowledgement\nThis work was supported by national funds through FCT—Foundation for Science and Technology\, I.P.\, under the project HetSyn (2023.13758.PEX).\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:d6a813eb9ec6c5921f81075d1eb6b4b9
URL:http://cns2026.sched.com/event/d6a813eb9ec6c5921f81075d1eb6b4b9
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SUMMARY:P028: Reliability of Eigenspectra Decay and Variance Scaling in 3T and 7T fMRI
DESCRIPTION:Introduction\nA system organized to criticality is able to switch phases from small inputs[1]. Prior research indicates that neural systems are organized to near-criticality across multiple scales[2]. One modality for which criticality analysis is novel is fMRI. fMRI allows for the analysis of resting state brain networks (RSNs)\, groups of units that coactivate with one another and reflect specific cognitive processes[3\,4].&nbsp\;\nAs a step towards characterizing the critical dynamics of brain networks\, it is necessary to first assess the reliability with which critical metrics can be estimated. To identify network-specific factors from individual-specific ones\, we compared metrics obtained for each RSN across scanners and across participants.&nbsp\;\n\nMethods\nThe metrics of criticality examined are derived using a new method\, phenomenological renormalization group (pRG) [5]. In this approach\, units are paired based on correlation. These pairs are summed together and normalized. A correlation matrix is calculated between the new clusters. This process is repeated such that\, at each iteration n of coarse-graining\, the number of units\, k represented by each cluster is 2n.\nFollowing coarse-graining\, several metrics are calculated: μ\, the power law exponent describing the covariance eigenspectra decay for a cluster of size K (λ ~ (r/K)-μ) and α\, the scaling exponent for cluster variance (σ2(K) ~ Kα). These analyses are performed for each network within each participant at each scan strength (3T\, 7T).&nbsp\;\n\nResults\npRG analysis in fMRI data yielded power-law relationships showing scaling of variance with cluster size and eigenvalues with rank. Within individuals\, we compared exponents obtained from 7T and 3T scans for each network. While values of exponents varied across networks\, we found a high degree of correlation between exponents in 3T and 7T data: 0.791 (μ) and 0.523 (α) for participant 1. For participant 2\, correlation coefficients were 0.818 (μ) and 0.934 (α).&nbsp\;\nWe examined whether exponents were similar between participants. When comparing exponents of distinct networks between participants 1 and 2\, we found correlation coefficients of 0.368 (μ) and 0.5493 (α) for 3T and coefficients of 0.747 (μ) and 0.795 (α) for 7T.\n\nDiscussion\nOur results indicate that RSNs possess critical dynamics that correlate with themselves across scanner strengths and individuals\, indicating that RSNs have intrinsic dynamics likely reflecting different cognitive processes.&nbsp\;\nInterestingly\, there is a large difference in α correlation coefficients (3T vs 7T) between participants 1 and 2. This may indicate that the stability of critical dynamics between scanner strengths varies across individuals. Also\, the stronger correlations between participants at 7T compared with 3T are likely because of the stronger signal-to-noise ratio at 7T.\nFuture directions are to assess reliability of criticality metrics in future participants and to characterize specific metrics of criticality within RSNs.\n\nReferences\n1. Fontenele\, A. J.\, et al. (2019). Criticality between cortical states. Physical Review Letters\, 122(20)\, 208101.\n2. Hengen\, K. B.\, & Shew\, W. L. (2024). Is criticality a unified set-point of brain function? (p. 2024.09.02.610815). bioRxiv. https://doi.org/10.1101/2024.09.02.610815\n3. Meshulam\, L.\, et al. (2019). Coarse Graining\, Fixed Points\, and Scaling in a Large Population of Neurons. Physical Review Letters\, 123(17)\, 178103. https://doi.org/10.1103/PhysRevLett.123.178103\n4. O’Byrne\, J.\, & Jerbi\, K. (2022). How critical is brain criticality? Trends in Neurosciences\, 45(11)\, 820–837. https://doi.org/10.1016/j.tins.2022.08.007\n5. Rosazza\, C.\, & Minati\, L. (2011). Resting-state brain networks: Literature review and clinical applications. Neurological Sciences\, 32(5)\, 773–785.\n\nAcknowledgement\nVM was supported through a PhD candidate research assistantship at the University of Minnesota. fMRI data was able to collected through use of Center for Magnetic Resonance Research resources at the University of Minnesota. Analysis was conducted using Minnesota Supercomputing Institute resources at the University of Minnesota.&nbsp\;\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:952f4d75769f1a135e85cdebcd846114
URL:http://cns2026.sched.com/event/952f4d75769f1a135e85cdebcd846114
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SUMMARY:P029: Modeling Emotional Contagion in Rats Using Dynamical State-Space Models
DESCRIPTION:Introduction\nEmotional contagion\, the sharing of another individual’s emotional state\, is a key component of social behavior and empathy. Neural population dynamics underlying internal affective states are increasingly studied using latent dynamical models that reveal structured activity patterns associated with behavioral states (Nair et al.\, 2022). In rodents\, socially relevant experiences can also shape memory and internal state representations (Veyrac et al.\, 2015). Here\, we investigate neural and behavioral dynamics in observer rats witnessing conspecifics receiving footshocks.\n\n\nMethods\nAdult observer rats were implanted with Neuropixels probes to record large-scale neuronal population activity while simultaneously measuring locomotor speed and pupil diameter. Animals observed a demonstrator receiving footshocks in an adjacent compartment. Spike trains were converted to firing rates and analyzed using a recurrent Switching Linear Dynamical System (rSLDS)\, a state-space model that captures both discrete neural states and continuous latent dynamics underlying population activity.\n\n\nResults\nShock observation produced significant increases in pupil dilation and reductions in locomotor speed\, with MANOVA revealing significant inter-rat and inter-shock variability. The rSLDS identified discrete neural states that shifted around shock events and captured coordinated dynamics across neural and behavioral signals. Importantly\, a distinct neural latent state emerged following shock observation and persisted longer than the behavioral responses\, indicating sustained internal processing beyond immediate physiological changes.\n\n\nDiscussion\nOur findings demonstrate that latent dynamical modeling reveals structured neural state transitions associated with socially transmitted distress. While neural and behavioral responses showed synchronized shifts around shock events\, neural population activity displayed a prolonged state not fully explained by pupil dilation or immobility. These results support the presence of emotional contagion\, suggesting that observer animals maintain a sustained neural representation of another individual’s distress.\n\n\nReferences\nNair\, A. et al. (2022).An approximate line attractor in the hypothalamus encodes an aggressive state. Cell\, 185(25)\, 4841–4859.https://doi.org/10.1016/j.cell.2022.11.027\nVeyrac\, A.\, Allerborn\, M.\, Gros\, A.\, Michon\, F.\, Raguet\, L.\, Kenney\, J.\, Godinot\, F.\, Thevenet\, M.\, García\, S.\, Messaoudi\, B.\, Laroche\, S.\, & Ravel\, N. (2015). Memory of occasional events in rats: Individual episodic memory profiles\, flexibility\, and neural substrate. Journal of Neuroscience\, 35(20)\, 7575–7586. https://doi.org/10.1523/JNEUROSCI.3941-14.2015\n\n\nAcknowledgement\nThis work is supported by Dutch Brain Interface Initiative (DBI2)\, project number 024.005.022 of the research programmed Gravitation\, which is financed by the Dutch Ministry of Education\, Culture\, and Science (OCW) via the Dutch Research Council (NWO)\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:e1701b77a52d8ded3d9e7c2f6a7121fb
URL:http://cns2026.sched.com/event/e1701b77a52d8ded3d9e7c2f6a7121fb
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SUMMARY:P030: Scalable Modular Architectures for Naturalistic Behavior and Cognitive Mapping in Biological and Artificial Intelligence
DESCRIPTION:Introduction\nBiological intelligence is far more adaptive\, autonomous and efficient than current artificial intelligence\, despite recent advances. Neuroethological comparisons and computational simulations show that nervous systems across phyla share a conserved modular organization for information flow from sensation to behavior [1](Fig. 1). Differences between mammals and primitive soft-bodied invertebrates are largely in kind and amount of detail handled by different modules. 4 computations (What is it? Where is it? How do I feel about it? What should I do?) compose waking consciousness\, while cognitive mapping enables intricate subjective experience\, adding the question What is it doing? We explore these computations in agent-based simulations.\n\nMethods\nThe agent-based simulation Cyberslug [2] was modeled on the core decision-making circuitry of a relatively simple animal\, the predatory sea slug Pleurobranchaea californica\, which retains character of the last common ancestor of bilaterally symmetric animals. Cyberslug is essentially an easily-scalable hybrid dynamical system which reproduces Pleurobranchaea’s decision-making in foraging. With small\, biologically-plausible additions to Cyberslug\, we developed the ASIMOV model with reward‑dependent plasticity for sensory valuation [3]\, as well as a Feature Association Matrix (FAM)\, a memory module inspired by hippocampal architecture [4]. Agents were tested in various simulated spatial environments with olfactory cues\, coded in NetLogo.\n\nResults\nThe modular neural organization of Pleurobranchaea was shown to be markedly similar to that of vertebrates and other invertebrates (e.g.\, insects\, cephalopods) providing a conserved core circuitry for computational modeling and expansion [1\,2]. Cyberslug reproduced adaptive cost‑benefit decision‑making in foraging [2]. ASIMOV extensions captured realistic sensory valuation\, including addiction‑like dynamics [3]. Addition of the FAM showed how episodic memory and spatial cognitive mapping emerge from simple associative learning rules. This enabled the latest ASIMOV-FAM agent for efficient spatial navigation\, one‑shot learning\, and improved performance in sparse‑reward environments compared to standard reinforcement learning [4].\n\nDiscussion\nOur results show that conserved modular architectures can organize the flow of information in animals and support naturalistic behavior\, episodic memory\, and cognitive mapping in artificial agents. Simple associative mechanisms analogous to hippocampal function were sufficient for sequence learning\, spatial cognitive mapping\, and recall\, highlighting a plausible computational basis for subjective‑like experience and flexible intelligence. Our incremental elaboration of biologically grounded circuits produce increasingly complex cognition and dynamic behavior\, providing a scalable computational framework for neuroethological studies\, as well as further development of flexible\, autonomous artificial intelligence (AI).\n\nFigure 1.&nbsp\;Both simple and complex animals handle flow of information from sensation to behavior with a common modular nervous system organization. Stimuli characteristics\, incentives and locations are integrated with memory\, motivation and affect for decisive action selection\, with 5 critical computations from “What is it?” to “How should I do it?”\, and cognitive mapping adding “What is it doing?”.​\n\nReferences\nGribkova\, E. D.\, Lee\, C. A.\, Brown\, J. W.\, Cui\, J.\, Liu\, Y.\, Norekian\, T.\, & Gillette\, R. (2023). A common modular design of nervous systems originating in soft-bodied invertebrates.&nbsp\;Frontiers in physiology\,&nbsp\;14\, 1263453.Brown\, J. W.\, Caetano-Anollés\, D.\, Catanho\, M.\, Gribkova\, E.\, Ryckman\, N.\, ... & Gillette\, R. (2018). Implementing goal-directed foraging decisions of a simpler nervous system in simulation.&nbsp\;Eneuro\,&nbsp\;5(1).Gribkova\, E. D.\, Catanho\, M.\, & Gillette\, R. (2020). Simple aesthetic sense and addiction emerge in neural relations of cost-benefit decision in foraging.&nbsp\;Scientific reports\,&nbsp\;10(1)\, 9627.Gribkova\, E. D.\, Chowdhary\, G.\, & Gillette\, R. (2024). Cognitive mapping and episodic memory emerge from simple associative learning rules.&nbsp\;Neurocomputing\,&nbsp\;595\, 127812.\n\n\nAcknowledgement\nThis work was supported by the Office of Naval Research (MURI grant N00014-19-1-2373).
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:135745f56b3952ebca708da0dd20357c
URL:http://cns2026.sched.com/event/135745f56b3952ebca708da0dd20357c
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SUMMARY:P031: Pathway-Specific Temporal Structure of Behavior-Predictive Modeling from Dopaminoceptive Striatal Activity
DESCRIPTION:Introduction\nForecasting behavioral actions from neuronal circuit activity requires selecting an appropriate prediction horizon and history window for the model. This choice depends on the signal timescale\, circuitry\, target behaviors\, and task structure. In dopaminoceptive striatal circuits\, D1R signals may carry both fast action-related and slower reward-expectation components [1]. Modeling based on inhibitory D2R signals is equally important but more challenging\, because suppression predictors relate to events less directly\, interact with state-dependent effects\, and may therefore yield weaker or less interpretable forecasts. In this study\, we present an extension of a previously reported D1R forecasting hyperparameter search to D2R signal analysis.\n\n\nMethods\nData came from 9 D1-Cre and 11 A2A-Cre mice with fiber-photometry recordings of&nbsp\;GCaMP6 signal from the ventrolateral striatum during head-fixed anticipatory licking protocol [2]. Ca2+\, isosbestic channels and licking were sampled at 20 Hz. Signals were detrended for photobleaching\, low-pass filtered\, artifact-corrected by robust regression of the control channel\, and normalized [3]. Licks and lick bursts were extracted as prediction targets. Predictors used fixed-basis representations of signal history [4]\, with grid search over preset history windows (L) and forecast horizons (H). With binomial logistic GLM\, leave-one-mouse-out testing compared dopamine-channel and isosbestic-only models using Precision@1%.\n\n\nResults\nAcross leave-one-mouse-out testing\, D1R signaling improved high-confidence forecasting beyond the isosbestic control in a timescale-dependent manner: for single licks\, the strongest gain occurred at short windows (best at L = 0.5 s\, H = 0.25 s\; ΔP@1% = 0.148)\, whereas burst-start prediction peaked at an intermediate forecast horizon (H = 1 s)\, with history peaks at L = 1 s (ΔP@1% = 0.091) and L = 8 s (ΔP@1% = 0.098)\, consistent with both rapid event-related and slower schedule-aligned dynamics. D2R signaling showed its strongest advantage for burst events at longer history and wider forecast windows (L = 3 s\, H = 3 s\; ΔP@1% = 0.204) and for single licks at the shortest windows (L = 0.25 s\, H = 0.25 s\; ΔP@1% = 0.111).\n\nDiscussion\nWe extended a temporal hyperparameter search developed for D1R fiber-photometry signals to D2R signals to compare the optimal forecast horizon and history length for excitatory and inhibitory predictors. For single-lick events\, both pathways showed similar dynamics: behavior was best predicted over short horizons from recent signal history. For state-dependent events such as licking bursts\, the models diverged. D1R signals peaked at a 1 s horizon with 1 s of history\, consistent with consummatory or anticipatory state changes. D2R signals were most informative at a 3 s horizon with a 3 s history window\, reflecting distinct underlying mechanisms. These findings further indicate the importance of temporal parameter selection for analysis.\n\n\nReferences\n1. Kim\, H. R.\, et al. (2020). A unified framework for dopamine signals across timescales. Cell\, 183(6)\, 1600–1616. https://doi.org/10.1016/j.cell.2020.11.013\n2. Toda\, K.\, et al. (2017). Nigrotectal stimulation stops interval timing in mice. Current Biology\, 27(24)\, 3763–3770. https://doi.org/10.1016/j.cub.2017.11.003\n3. Keevers\, L. J.\, & Jean-Richard-dit-Bressel\, P. (2025). Obtaining artifact-corrected signals in fiber photometry via isosbestic signals\, robust regression\, and dF/F calculations. Neurophotonics\, 12(2)\, 025003–025003. https://doi.org/10.1117/1.NPh.12.2.025003\n4. Pillow\, J. W.\, et al. (2005). Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model. Journal of Neuroscience\, 25(47)\, 11003–11013. https://doi.org/10.1523/JNEUROSCI.3305-05.2005\n\nAcknowledgement\nThe authors have no additional acknowledgments to report\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:08fd27fc1a5560a87094b577e5bf80c0
URL:http://cns2026.sched.com/event/08fd27fc1a5560a87094b577e5bf80c0
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SUMMARY:P032: AnalySim data and project sharing site: admin panel\, notebook and CSV browser expansion
DESCRIPTION:Introduction\nIn this poster\, we present the updates in the development of the AnalySim science gateway for data sharing and analysis of computational neuroscience projects. AnalySim is an open source project that runs as a web service that allows creating scientific projects and sharing them. An early testing version of the gateway is currently hosted at https://analysim.tech\, supported by the NSF-funded ACCESS advanced computing and data resource. The gateway can be installed and run within a lab or larger organization. AnalySim facilitates data sharing\, data hosting for publications\, interactive visualizations\, collaborative research\, and crowdsourced analysis. It differs from Github by offering a notebook-oriented interface for a research audience.\n\nMethods\nAnalySim is built with a .Net backend API service with C# and an Angular frontend using Typescript. The data is saved in a relational database\, PostGreSQL\, including binary blob storage for files. The interface has a file browser\, including a CSV viewing tool\, and notebook editing and display capabilities. Hosted projects can have multiple notebook files\, where one can be designated as the main project description. Projects can be shared with the public or kept private and shared only with collaborators. Notebooks can be in Python or Javascript backend\; ObservableHQ online notebooks are also supported. The source code can be found at https://github.com/soft-eng-practicum/AnalySim along with instructions on how to install and run it.\n\nResults\n\n\n AnalySim has been a participant of the International Neuroinformatics Coordinating Facility (INCF) Google Summer of Code (GSoC) program since 2021. Participation in GSoC 2025 added major features. The user interface was improved to have a more consistent style\, and new pages were added to support new functionality\, together with less visible improvements in the backend. The changes were: (1) addition of an admin panel that allows browsing and deleting users\, projects\, datasets\, and notebooks\; (2) improving the CSV data browser\; (3) improvements in the notebook list and using the default notebook as the project description.\n\nDiscussion\n\nReferences\nN/A\n\nAcknowledgement\nWe thank INCF and GSoC for supporting AnalySim. This work used Jetstream2 at Indiana University through allocation BIO220033 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program\, which is supported by National Science Foundation grants #2138259\, #2138286\, #2138307\, #2137603\, and #2138296.\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:dda3e94e7753168e92fe9697ab4c938a
URL:http://cns2026.sched.com/event/dda3e94e7753168e92fe9697ab4c938a
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SUMMARY:P033: Greater segregation\, not integration\, accompanies increasing working memory load
DESCRIPTION:Introduction\n\n\nIntroduction\nThe brain reorganizes its network architecture to meet cognitive demands [1]\, and the balance between segregated and integrated systems is thought to shift with cognitive load [2]. How system segregation changes as load increases\, and whether those changes support performance\, remains unresolved. This is the case particularly for working memory\, where prior findings conflict on whether segregation or integration underpins successful function\, and have mainly focused on differences between rest and task rather than incremental increases in task load [3].\n&nbsp\;\n\n\n\nMethods\n\n\nMethods\nUsing functional MRI in healthy adults\, we measured system segregation across five canonical resting-state networks from rest (N=69) to an N-back working memory task (N=27). We also measured system segregation across four levels of task load (0- to 3-back). We correlated system segregation against working memory accuracy and used repeated measures analyses of variance (RM-ANOVA) to compare segregation across task loads. Reproducibility testing included measuring system segregation across structural and functional (Yeo\, [4]) parcellations\,&nbsp\;&nbsp\;sparsity thresholds\, and approximating segregation with modularity\, a parcellation-independent measure. For a granular understanding\, segregation was measured on a subnetwork level as well.\n&nbsp\;\n\n\n\nResults\n\n\nResults\nAt rest\, greater system segregation predicted higher working memory accuracy after controlling for age\, sex\, and motion. Within the task\, segregation increased with load\, and both 3-back segregation and its change across load predicted 3-back accuracy. These load effects reproduced when using the parcellation-independent modularity measure\, but not under the functional parcellations. This may be related to the divided representation of executive-cognitive regions in these functional networks\, as subnetwork analyses revealed that load effects in our primary parcellation was&nbsp\;&nbsp\;driven by increased segregation of the unified attention/executive network.\n&nbsp\;\n\n\n\nDiscussion\n\n\nDiscussion\nThese findings indicate that the relationship between segregation and cognition is state- and scale-dependent. Load-dependent strengthening of cognitive-network connectivity\, rather than global integration\, appears to support working memory performance\, refining the common assumption that higher cognitive demand requires greater network integration.\n&nbsp\;\n\n\nReferences\n\n\n[1]&nbsp\;Park\, H. J.\, & Friston\, K. (2013). Structural and functional brain networks: from connections to cognition.&nbsp\;Science\,&nbsp\;342(6158)\, 1238411.\n[2]&nbsp\;Cohen\, J. R.\, & D\'Esposito\, M. (2016). The segregation and integration of distinct brain networks and their relationship to cognition.&nbsp\;Journal of Neuroscience\,&nbsp\;36(48)\, 12083-12094.\n[3]&nbsp\;Finc\, K.\, Bonna\, K.\, He\, X.\, et al\, & Bassett\, D. S. (2020). Dynamic reconfiguration of functional brain networks during working memory training.&nbsp\;Nature communications\,&nbsp\;11(1)\, 2435.\n[4]&nbsp\;Yeo\, BT Thomas\, Fenna M. Krienen\, .. Roffman et al. "The organization of the human cerebral cortex estimated by intrinsic functional connectivity."&nbsp\;Journal of neurophysiology&nbsp\;(2011).\n\nAcknowledgement\n\n\n\nCanadian Institute of Health Research (CIHR) Project Grant [PJT-168878]*&nbsp\;\nNSHA Fibromyalgia Research Fibromyalgia Grant*&nbsp\;
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:e9a0975a8d396692165820509d32dfc9
URL:http://cns2026.sched.com/event/e9a0975a8d396692165820509d32dfc9
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SUMMARY:P034: Rising threat\, merging networks: dynamic heat pain lowers segregation and turns S2\, a core pain region\, into an integrative hub
DESCRIPTION:Introduction\n\n\nHow stronger threat reshapes large-scale brain network organization remains untested. Heightened arousal via sympathetic activation has been proposed to increase functional integration\, but no study has directly tested this (1-3). We compared two equivalent heat pain stimuli\, a static ramp-and-hold and a dynamically escalating profile\, the latter more strongly engaging pain\, threat\, and reward-punishment circuitry (Sunavsky\, in review).\n\nMethods\n\n\nIn 30 participants\, we extracted epochs spanning 39 TRs from different stimulus conditions and computed region-to-region functional connectivity across a 131-node parcellation. Matrices were proportionally thresholded (0.05 to 0.5) and characterized using graph metrics: system segregation\, Louvain modularity\, nodal degree\, and participation coefficient (4). Conditions were compared with paired t-tests at each threshold\, FDR-corrected across all nodes and thresholds.\n\nResults\n\n\nDynamically escalating threat reduced system segregation relative to the static condition\, consistent with greater between-network integration under heightened arousal. Whole-brain FDR-corrected analysis showed increased participation coefficient in bilateral secondary somatosensory cortex (S2\, parietal operculum)\, indicating stronger cross-network connector behavior. Among regions more strongly activated by the dynamic stimulus\, only S2 and nucleus accumbens also showed increased hubness (binarized degree). Modularity did not differ between conditions.\n\nDiscussion\n\n\nEscalating threat shifts network organization toward integration rather than reconfiguring module structure\, since segregation fell while modularity was preserved. The convergence of higher participation and degree at S2 identifies it as a key integrative hub recruited by dynamic threat\, with the accumbens implicating reward-punishment circuitry. These findings provide direct evidence that arousal-linked threat promotes functional integration in the human brain.\n\nReferences\n\n\n1. Shine\, J.M.\, Bissett\, P.G.\, Bell\, P.T.\, Koyejo\, O.\, Balsters\, J.H.\, Gorgolewski\, K.J.\, Moodie\, C.A.\, & Poldrack\, R.A. (2016). The dynamics of functional brain networks: integrated network states during cognitive task performance. Neuron\, 92(2)\, 544-554.\n2. Shine\, J.M. (2019). Neuromodulatory influences on integration and segregation in the brain. Trends in Cognitive Sciences\, 23(7)\, 572-583.\n3. Chan\, M.Y.\, Park\, D.C.\, Savalia\, N.K.\, Petersen\, S.E.\, & Wig\, G.S. (2014). Decreased segregation of brain systems across the healthy adult lifespan. Proceedings of the National Academy of Sciences\, 111(46)\, E4997-E5006.\n4. Rubinov\, M.\, & Sporns\, O. (2010). Complex network measures of brain connectivity: uses and interpretations. NeuroImage\, 52(3)\, 1059-1069.\n\nAcknowledgement\n\n\n\nCanadian Institute of Health Research (CIHR) Project Grant [PJT-168878]*&nbsp\;\nNSHA Fibromyalgia Research Fibromyalgia Grant*&nbsp\;
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:473c92947aeb98c62961ee14cc849ff6
URL:http://cns2026.sched.com/event/473c92947aeb98c62961ee14cc849ff6
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SUMMARY:P035: Chronic Pain Uncouples Functional Brain Network Segregation From Cognitive Performance in Aging
DESCRIPTION:Introduction\nChronic pain (CP) disproportionately affects older adults\, not only because it is more prevalent in later life\, but also because it may exacerbate cognitive aging and is associated with increased dementia risk [1]. Recent research has implicated accelerated brain aging in CP patients as a potential mechanism behind their disrupted cognitive aging [1]. However\, this work remains limited in its focus on structural features of brain aging in CP. Alternatively\, the role of functional brain network segregation in normative neurocognitive aging has been robustly characterized\; it declines with age\, with lower segregation linked to cognitive decline and dementia risk [2\,3]. Whether CP disrupts these relationships has not yet been examined.\n\nMethods\nParticipants included healthy controls without ongoing pain\, and CP patients with chronic back pain or fibromyalgia. After age-sex matching and censoring participants with fMRI head motion above acceptable values\, the final sample included 60 controls and 141 CP patients. Executive cognition was assessed using the n-back task\, stop-signal task\, and Stroop task. Functional brain network segregation was quantified using the system segregation metric\, with network communities defined using the Harvard-Oxford Optimized parcellation. PROCESS moderation analysis in SPSS was used to test interaction effects.\n\nResults\nOverall system segregation and cognition did not differ between healthy controls and CP patients. However\, older age predicted poorer performance across all tasks in the patient group\, but only stop-signal performance in controls\, with diagnosis significantly moderating the association between age and stroop task performance. Despite this accelerated cognitive aging pattern\, system segregation declined with age in controls but not CP patients\, with diagnosis also moderating this association. Finally\, CP diagnosis significantly reversed the association between working memory (n-back accuracy) and system segregation: higher segregation predicted better working memory in controls but worse working memory in CP patients.\n\nDiscussion\nWe found that chronic pain was associated with an altered brain aging trajectory in which network segregation is relatively preserved but becomes cognitively maladaptive\, predicting worse rather than better working memory performance.&nbsp\; If age-related declines in segregation reflect compensation for structural degeneration\, this pattern may indicate impaired functional compensation in chronic pain patients\, who already show accelerated structural brain aging [1]. Overall\, our findings suggest that the role of system segregation in neurocognitive aging is disrupted in the presence of CP\, with important implications for interpreting related neuroimaging biomarkers and developing cognitive interventions in this population [3].\n\nReferences\n1: Zhao\, L.\, Zhang\, L.\, Tang\, Y.\, & Tu\, Y. (2025). Cognitive impairments in chronic pain: A brain aging framework. Trends in Cognitive Sciences. https://doi.org/10.1016/j.tics.2024.12.004&nbsp\;\n2: Calder\, C. N.\, Helmick\, C.\, & Hashmi\, J. A. (2026). High brain network system segregation is differentially linked with cognitive performance across the life span. Network Neuroscience\, 10(2)\, 352–373. https://doi.org/10.1162/NETN.a.542\n3: Zhang\, Z.\, Chan\, M. Y.\, Han\, L.\, Carreno\, C. A.\, Winter-Nelson\, E.\, Wig\, G. S.\, & Alzheimer’s Disease Neuroimaging Initiative. (2023). Dissociable effects of Alzheimer’s disease-related cognitive dysfunction and aging on functional brain network segregation. Journal of Neuroscience\, 43(46)\, 7879–7892. https://doi.org/10.1523/JNEUROSCI.0579-23.2023\n\nAcknowledgement\nI would like to thank my supervisor\, Dr. Javeria Hashmi\, and the entire Netphys lab for supporting this research and fostering a passionate environment for scientific thought.\nMy work was supported by the Brain Repair Centre through the Dalhousie Faculty of Medicine 2025 Graduate Studentship program and by the CIHR through the Canada Graduate Scholarship – Master’s Program.\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:683b3942ca98ac8d80e5f57f6b4cb4f2
URL:http://cns2026.sched.com/event/683b3942ca98ac8d80e5f57f6b4cb4f2
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SUMMARY:P036: Inferring microcircuit aging from subject EEG and linking to cognitive decline
DESCRIPTION:Introduction\nCognitive decline occurs in aging [1] and is accompanied by changes in electroencephalography (EEG) signals [2]. However\, the cellular mechanisms underlying these EEG alterations cannot be directly assessed in living humans. Key cellular and microcircuit mechanisms implicated in human aging include reductions in inhibition from different interneuron types\, dendritic spine density\, and NMDA receptor signaling [3–5]\, but the links to age-related cognitive decline and EEG changes remain to be established.\n\n\nMethods\nTo overcome experimental limitations\, we trained artificial neural networks (ANN) to estimate microcircuit aging using simulated EEG biomarkers generated by detailed models of human cortical microcircuits that integrated key microcircuit mechanisms in human aging [6]. We then applied the ANNs to estimate microcircuit aging for each subject in the LEMON dataset [1] (ages 20-80) from their resting-state EEG and examined associations between estimated microcircuit aging and cognitive scores.\n\n\nResults\nThe simulated EEG biomarkers from aging microcircuit simulations accounted for a large portion of the range of changes in aging patient EEG. The ANNs estimated microcircuit aging with high precision in silico\, and when applied to human EEG data\, estimated microcircuit aging corresponded with subject age and was correlated with cognitive decline across multiple cognitive domains. Furthermore\, we found sex-specific differences in correlations with microcircuit age for some of the cognitive domains. Among EEG biomarkers used by the ANN\, the aperiodic features most strongly influenced microcircuit aging estimations.\n\n\nDiscussion\nWe demonstrate a modeling-informed approach to estimate microcircuit aging in human subjects from non-invasive EEG\, and showed that microcircuit aging was associated with cognitive decline. Future directions will be to estimate changes in the levels of the individual microcircuit mechanisms to tease apart their contributions to cognitive decline. Our approach and tools improve the mechanistic understanding of aging and may further serve in clinical stratification of associated pathologies.\n\n\nReferences\n1.\tBabayan A\, et al. (2019). Sci Data. 6(1):180308.&nbsp\;DOI: 10.1038/sdata.2018.308\n2.\tMerkin A\, et al. (2023). Neurobiology of Aging. 121:78–87. DOI: 10.1016/j.neurobiolaging.2022.09.003\n3.\tChen Y\, et al. (2023). Neurobiology of Aging. 125:49–61.&nbsp\;DOI: 10.1016/j.neurobiolaging.2023.01.013\n4.\tPetanjek Z\, et al. (2011). Proceedings of the National Academy of Sciences. 108(32):13281–6.&nbsp\;DOI: 10.1073/pnas.1105108108\n5.\tPegasiou CM\, et al. (2020). Cerebral Cortex. 30(7):4246–56.&nbsp\;DOI: 10.1093/cercor/bhaa052\n6.\tGuet-McCreight A\, et al. (2025). Aging Cell. e70329. DOI: 10.1111/acel.70329\n\nAcknowledgement\nAlexandre Guet-McCreight and Etay Hay thank the Krembil Foundation for their generous funding support. Alexandre Guet-McCreight thanks the Canadian Institutes of Health Research—Institute of Aging for funding support.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:f6f4e53e704639c736d2d20e620ae24e
URL:http://cns2026.sched.com/event/f6f4e53e704639c736d2d20e620ae24e
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SUMMARY:P037: The effects of reduced somatostatin interneuron inhibition in depression on multilayered human cortical microcircuit activity.
DESCRIPTION:Introduction\nMajor depressive disorder (depression) is associated with reduced cortical inhibition from somatostatin-expressing (SST) interneurons\, as indicated by decreased SST expression in human post-mortem studies[1]. We previously showed in simulations of human cortical layer 2/3 that reduced SST interneuron inhibition would increase baseline cortical activity (noise) to significantly reduce the signal-to-noise ratio in signal processing and contribute to cognitive deficits observed in depression[2]. However\, as SST interneuron proportion and connectivity vary across cortical layers\, it is unclear how reduced SST interneuron inhibition in depression differentially affects processing and signal propagation across cortical layers[3].\n\n\nMethods\nIn this study\, we generated biophysical models of multilayered human cortical microcircuits that encompass 4000 neurons with detailed morphologies spanning 12 neuron types across layers 2-5. Our models integrated human cellular\, synaptic\, neuron proportion\, and connectivity data\, such as human paired recordings and electron-microscopy reconstruction of a human cortical column[4]. To better capture biological variability\, we incorporated heterogeneity in synaptic strengths\, transmission delays\, and connection probabilities. We simulated electroencephalography (EEG) signals arising from the microcircuit using NEURON and LFPy\, and reproduced properties of the power spectrum density (PSD) using thalamic drive and adjusting connectivity.\n\n\nResults\nWe reproduced healthy baseline firing rates across cell types and oscillatory dynamics (1/f decay and peak power in alpha frequency band) as seen in human resting-state EEG. By systematically reducing SST inhibition within and across layers\, we quantified the difference in layer-specific contributions to circuit-level dysfunction and altered EEG power spectral density during resting state.\n\n\nDiscussion\nOur study characterizes the effect of reduced inhibition in depression on cortical activity and signal processing across layers\, and thereby furthering current understanding of the role dendritic inhibition plays in signal processing in health and depression. Furthermore\, our characterization of the signatures of reduced SST inhibition across layers on resting-state EEG due refine our previous biomarkers\, and may serve to improve current stratification of depression patients. Finally\, our models of multilayers human cortical microcircuits can be used by the scientific community to study cortical processing in health and other diseases.\n\n\nReferences\n\n\n1.&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Seney\, M. L.\, Tripp\, A.\, … Sibille\, E. (2015). Laminar and cellular analyses of reduced somatostatin gene expression in the subgenual anterior cingulate cortex in major depression. Neurobiology of Disease\, 73(Complete)\, 213–219. \n2.&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Yao\, H. K.\, Guet-McCreight\, A.\, … Hay\, E. (2022). Reduced inhibition in depression impairs stimulus processing in human cortical microcircuits. Cell Reports\, 38(2). \n3.&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Tremblay\, R.\, Lee\, S.\, & Rudy\, B. (2016). GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits. Neuron\, 91(2)\, 260–292. \n4.&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Shapson-Coe\, A.\, Januszewski\, M.\, A.\, … Lichtman\, J. W. (2024). A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution. Science\, 384(6696)\, eadk4858.\n\nAcknowledgement\nThis study was supported by a fellowship grant from the Labatt Family Network for Research on the Biology of Depression\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:f8e02eed94b6d67fd4cdd6cc36d1890a
URL:http://cns2026.sched.com/event/f8e02eed94b6d67fd4cdd6cc36d1890a
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SUMMARY:P038: Cerebellar Isolation with Multi-Modality MRI Images Using Deep Learning
DESCRIPTION:Introduction\nThe cerebellum is involved in motor\, cognitive\, and affective functions. A critical prerequisite for cerebellar MRI analyses is the isolation from the brain as spatial normalization to whole-brain templates misaligns the cerebellum\, compromising accuracy. To this end\, specialized tools like SUIT [1] were developed. However\, current software has two key limitations: a. They were developed on healthy adults\, lacking robustness across diverse populations\; b. They use single-modality input\, ignoring complementary contrasts like T2w. This work introduces a 3D U-Net [2] for cerebellar isolation that solves both problems\, producing reliable results across lifespan and a dual-input architecture for improved accuracy.\n\n\nMethods\nWe combined 5 different databases (N=101)\, spanning ages 0-76 years. Raw images were registered to the MNI152NLin6Asym template[3] where cropping was applied via a fixed bounding box. We implemented a 3D U-Net with four encoding/decoding stages. Each stage contains 3D convolutions\, instance normalization\, and LeakyReLU. The network accepts dual-channel inputs for T1w and T2w modality images and handles missing modalities via zero-padding. Skip connections preserve spatial details for accurate boundary delineation. Model outputs were transformed back to native space and postprocessed. The performance was measured by Dice Score Coefficient (DSC) and Hausdorff Distance.\n\nResults\n\n​We compared our U-Net against SUIT across the lifespan. On adult data (SUIT's optimal population)\, our U-Net achieved lower Hausdorff distances\, indicating superior boundary alignment. Critically\, SUIT failed completely on neonatal and elderly degenerative cases (24.4% failure rate)\, while U-Net performed consistently across all ages. For multi-modality evaluation\, U-Net outperformed SUIT with single modalities (T1w or T2w). Combined T1w+T2w inputs yielded significantly better results than either alone\, demonstrating successful fusion of complementary contrast information (See Figure 1).\n\nDiscussion\n\nThis study presents a 3D U-Net for cerebellar isolation trained on diverse multi-modal data (0-76 years\, including pathology). The model outperformed SUIT across both metrics\, particularly in boundary precision\, and generalized effectively across the lifespan where SUIT failed. A key strength is handling T1w/T2w inputs individually or jointly for improved robustness and accuracy. Another contribution is our expertly curated dataset of 101 hand-corrected masks for other researchers. Limitations include a modest sample size for rare pathologies and a focus on structural MRI only. So\, in the future\, we will expand to other contrasts and populations.\n\nFigure 1.&nbsp\;Isolation analysis. Comparison of U-Net VS SUIT for (a) Hausdorff Distance (HD) and (b) Dice Score Coefficient (DSC). c shows performance for different input modalities in the full datasets. Horizontal lines between bars with asterisks denote significant differences (paired t-tests). Baseline: Average mask prediction. d shows resulting mask in problematic subjects.​\n\nReferences\n1. Diedrichsen\, J. (2006). A spatially unbiased atlas template of the human cerebellum. NeuroImage\, 33(1)\, 127–138.&nbsp\;https://doi.org/10.1016/j.neuroimage.2006.05.056\n2. Ronneberger\, O.\, Fischer\, P.\, Brox\, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015\,&nbsp\;9351\, 234-241. Springer. https://doi.org/10.1007/978-3-319-24574-4_28\n3. Fonov\, V.\, Evans\, A.\, McKinstry\, R.\, Almli\, C.\, & Collins\, D. (2009). Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage\, 47(Supplement 1)\, S102. https://doi.org/10.1016/S1053-8119(09)70884-5\n\nAcknowledgement\nThis research was funded by the Raynor Cerebellum Project. We thank the Brain and Mind Institute at Western University for data acquisition and support. We acknowledge the contributors of the public datasets used in this work: dHCP\, BCP\, and HCP-YoungAdult. We are grateful to the expert raters for manual mask validation.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:b487f5cb57fc7e3a787e91b443dadd02
URL:http://cns2026.sched.com/event/b487f5cb57fc7e3a787e91b443dadd02
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SUMMARY:P039: The Self-Organization of Serotonergic Axons at the Surface of a Vertebrate Brain: A Myopic Self-Avoiding FBM Model
DESCRIPTION:Introduction\nSerotonergic axons (fibers) are present in virtually all brain regions of vertebrate animals (from humans to fishes). Within these regions\, individual serotonergic fibers typically grow in paths that resemble random walks with memory. At the population level\, they produce regionally-specific fiber meshworks that are thought to support the neuroplasticity of local\, non-serotonergic neural circuits. We have recently developed a computational framework in which the trajectories of serotonergic fibers are modeled as paths of reflected fractional Brownian motion (rFBM)\, with simulated fiber densities approximating the biological densities in the forebrains of two phylogenetically distant species\, the mouse [1] and the Pacific angelshark [2].\n\n\nMethods\nThis study focuses on the distribution of serotonergic fibers at the brain surface. Specifically\, we investigate the emergence of high-density fiber bands in the dorsal pallium (including the mammalian cerebral cortex) that rFBM cannot capture accurately. We introduce a new animal model\, the Pacific electric ray (Tetronarce californica)\, the forebrain of which has an extremely simple geometry with no ventricles or major fibers in the telencephalon. Its serotonergic fibers are visualized with immunohistochemistry\, with comparisons to the mouse and angelshark brains [1\,2]. We also introduce a major theoretical extension of rFBM\, the reflected myopic self-avoiding FBM\, based on a recently developed myopic self-avoiding FBM model [3].\n\n\nResults\nIn the electric ray telencephalon\, serotonergic fibers generally accumulate at higher densities near the pial surface\, consistent with findings in other vertebrate species. However\, they additionally produce dense bands in the dorsal pallium (homologous to the mammalian cerebral cortex). The emergence of this biologically important feature cannot be explained by simple rFBM but is consistent with our simulations using the reflected myopic self-avoiding FBM\, a stochastic process that includes a mean-density interaction between the members of the fiber ensemble. These simulations show that the interaction cuts off the divergence of&nbsp\;the density at a reflecting boundary\, producing a high-density plateau in the vicinity of the boundary.\n\n\nDiscussion\nOur results suggest that FBM\, with its recently developed theoretical extensions by our group [3\,4]\, can eventually predict the key features of serotonergic fiber densities in any vertebrate brain. This approach relies on the geometry and regional viscoelasticity of the brain and is agnostic to anatomically-defined brain nuclei and their biological function. This research contributes to the understanding of the minimal set of principles that lead to the self-organization of the fundamental brain architecture. In addition\, it stimulates the theoretical development of FBM-related stochastic processes\, with broad applications in other fields.\n\n\nReferences\nJanušonis\, S.\, Haiman\, J.H.\, Metzler\, R.\, Vojta\, T. (2023) Predicting the distribution of serotonergic axons [...]. Front. Comput. Neurosci.&nbsp\;17: 1189853. https://doi.org/10.3389/fncom.2023.1189853&nbsp\;&nbsp\;Janušonis\, S.\, Metzler\, R.\, Vojta\, T. (2025) The organization of serotonergic fibers in the Pacific angelshark brain [...]. Front. Neurosci. 19: 1602116. https://doi.org/10.3389/fnins.2025.1602116House\, J.\, Bakhshizada\, R.\, Janušonis\, S.\, Metzler\, R.\, Vojta\, T. (2025) Fractional Brownian motion with mean-density interaction [...]. Phys. Rev. E 112: 034119.&nbsp\;https://doi.org/10.1103/w5pk-bw5rWang\, W.\, Balcerek\, M.\, Burnecki\, K.\, et al. (2023) Memory-multi-fractional Brownian motion with continuous correlation. Phys. Rev. Res. 5: L032025.&nbsp\;https://doi.org/10.1103/PhysRevResearch.5.L032025 \nAcknowledgement\nThis research was funded by an NSF-BMBF CRCNS grant (NSF #2112862 to SJ & TV).\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:1dfc812c4061fd3d494246a21f98ff8e
URL:http://cns2026.sched.com/event/1dfc812c4061fd3d494246a21f98ff8e
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SUMMARY:P040: Dendritic calcium dynamics shape functionally relevant human E-/MEG ~20Hz beta events: a biophysical modeling study
DESCRIPTION:Introduction\nElectroencephalography (EEG) and Magnetoencephalography (MEG) are widely used non-invasive techniques to record electric brain activity from the human brain. While it has long been known that synchronous intracellular currents in dendrites of neocortical pyramidal neurons underlie E/MEG signals\, only few theories on the computational role of E/MEG dynamics consider dendritic computations. For instance\, high-amplitude transient ~20Hz oscillations known as cortical beta-events\, dominate human E/MEG signals and have been repeatedly argued to support perception and motor action by orchestrating spiking activity [1\,2]. Yet\, the neural generators of lower frequency oscillations and their effect on dendritic activity are underexplored.\n\n\nMethods\nHere\, we present a biophysically detailed neocortical circuit model that is optimized to study how dendritic processes manifest in human E/MEG signals. Somatic conductances were tuned to reproduce dynamics observed in in vitro experiments from human donors. The active conductances in the pyramidal neuron dendrites were tuned to reproduce non-linear processes associated with intracellular calcium. The layer 5 neurons exhibit calcium plateau potentials in response to high-frequency somatic spiking\, coincident somatic and dendritic inputs\, and strong feedback inputs\, as repeatedly shown in the literature [3\,4]. The layer 2/3 pyramidal neurons generate shorter dendritic spikes that have recently been reported in human neurons [5\,6]. \n\n\nResults\nExpanding prior theories on the generation of beta events [7\,8]\, we demonstrate how dendritic calcium spikes\, in combination with somatic and dendritic inhibition by GABAergic interneurons\, can produce the characteristic waveform shapes at 15-25 Hz observed in human E/MEG data. These findings complement previous reports of dendritic calcium spikes being detectable at the cortical surface in rodents [9]\, by demonstrating that these spikes are associated with large currents that dominate the E/MEG signal. To test and constrain model predictions\, we show preliminary evidence comparing simulated extracellular fields to laminar recordings during homologous beta events in rodents. \n\n\nDiscussion\nOur modeling work makes important contributions to understanding the role of dendritic calcium dynamics in the multiscale neural generators of functionally relevant human brain oscillations. Across species translation of our results provides a powerful framework to examine the causal influence of dendritic processes and other beta event generating mechanisms in sensory perception and motor action.&nbsp\; The circuit is packaged and distributed within the user-friendly Human Neocortical Neurosolver (HNN) software (https://hnn.brown.edu) designed for multiscale interpretation of human E/MEG signals\, making our tools and results available to a broad neuroscience community. \n\n\nReferences\n[1]&nbsp\;Shin\, H. et al. (2017). elife\,&nbsp\;6\, e29086.\n[2]&nbsp\;Bonaiuto\, J. J. et al. (2021). NeuroImage\,&nbsp\;242\, 118479.\n[3] Larkum\, M. E. et al. (1999).&nbsp\;Proceedings of the National Academy of Sciences\,&nbsp\;96(25)\, 14600-14604.\n[4]&nbsp\;Larkum\, M. et al. (1999).&nbsp\;Nature\,&nbsp\;398(6725)\, 338-341.\n[5]&nbsp\;Gidon\, A. et al. (2020). Science\,&nbsp\;367(6473)\, 83-87.\n[6]&nbsp\;Gooch\, H. M. et al. (2022). Cell reports\,&nbsp\;41(3).\n[7]&nbsp\;Sherman\, M. A. et al. (2016). Proceedings of the National Academy of Sciences\,&nbsp\;113(33)\, E4885-E4894.\n[8] Law\, R. G. et al. (2022). Cerebral Cortex\,&nbsp\;32(4)\, 668-688.\n[9]&nbsp\;Suzuki\, M.\, & Larkum\, M. E. (2017). Nature communications\,&nbsp\;8(1)\, 276.\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:fca449e6b28f3e62885e5309e041132a
URL:http://cns2026.sched.com/event/fca449e6b28f3e62885e5309e041132a
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SUMMARY:P041: Predicting natural video motion from spiking activity across the mouse visual pathway
DESCRIPTION:Introduction\nVisual information from individual photoreceptors represents only simple light intensity information. More complex visual information\, such as motion\, is discerned by considering the combined responses from several receptors\, or over a duration. The exact processes and locations at which encoding steps occur along the visual pathway are unclear. Yet\, by aligning response preferences of neurons to the presence of specific visual stimuli\, specialised encoding regions may be identified. Using computer vision methods\, we demonstrate the ability to extract simple visual components from natural stimuli and\, using electrophysiological data in mice\, predict neuronal optical flow response preferences across the visual pathway.\n\nMethods\nElectrophysiological recordings from the public Allen Brain Observatory dataset\, comprising responses of 32 mice (Mus musculus) to varied artificial and natural stimuli\, were processed to detect spiking action potentials [1]. Dense optical flow analysis was performed to extract motion magnitude and direction by estimating local neighbourhood displacement between frames. Magnitudes were weighted by their cosine direction components to assess correlation between spiking rate and motion magnitude in 8 directions. Moreover\, region-specific logistic regression models were trained\, using either drifting grating or natural video stimulus-response data\, to predict predominant global motion direction for a novel natural video from spiking rates.\n\nResults\nSubsets of neurons within regions displayed correlation between spiking rate and optical flow magnitude consistently across repeated presentations\, but due to motion direction bias within the video\, horizontal direction preferences were more represented than vertical ones. Regional regression models were able to predict predominant motion direction\, with accuracy varying across regions\, and specific direction performance reliant on sufficient training examples. Both models trained using only drifting gratings\, or only natural video\, displayed high direction prediction accuracy to a novel video. Hence\, we identify a subset of visual pathway cells with directional coding preferences to natural video motion consistent with rate-based coding.\n\nDiscussion\nThis study was motivated by previous attempts to train models to predict high-resolution pixel images from spiking activity\, whereby models were unable to generalise to predict novel stimuli [2]. Our work elucidates possible shortcomings in such an approach that warrant further investigation: mouse spiking activity contains less relevant pixel information than we previously believed\, quantified by our analysis\, likely due to a specialisation for motion over acuity. This study represents the first to compare regional differences in visual feature prediction\, using electrophysiological activity\, for a novel natural video. Future work aims to explore more specific feature predictions\, including foreground/background motion discrimination.\n\nReferences\n1.&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Siegle\, J. H.\, Jia\, X.\, Durand\, S.\, Gale\, S.\, Bennett\, C.\, Graddis\, N.\, Heller\, G.\, Ramirez\, T. K.\, Choi\, H.\, Luviano\, J. A.\, Groblewski\, P. A.\, Ahmed\, R.\, Arkhipov\, A.\, Bernard\, A.\, Billeh\, Y. N.\, Brown\, D.\, Caldejon\, S.\, Casal\, L.\, Cho\, A.\, … Koch\, C. (2021). Survey of spiking in the mouse visual system reveals functional hierarchy. Nature\, 592(7852)\, 86–92. https://doi.org/10.1038/s41586-020-03171-x\n2.&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Chen\, Y.\, Beech\, P.\, Yin\, Z.\, Jia\, S.\, Zhang\, J.\, Yu\, Z.\, & Liu\, J. K. (2024). Decoding dynamic visual scenes across the brain hierarchy. PLoS Computational Biology\, 20(8)\, e1012297. https://doi.org/10.1371/journal.pcbi.1012297\n\nAcknowledgement\nThis project utilises the open source Allen brain observatory visual coding neuropixels dataset from the Allen Institute for Brain Science [1].\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:c334bd89dad8dd452a5fb43646e4f3ce
URL:http://cns2026.sched.com/event/c334bd89dad8dd452a5fb43646e4f3ce
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SUMMARY:P042: Neural Heterogeneity Controls Neural Network Development
DESCRIPTION:Introduction\nWe study the role of interaction between synaptic plasticity rules and cellular physiology in producing useful connectivity in neural populations. We leverage two key aspects of biological neural networks: 1) neurons and the synapses connecting them are inherently diverse in their structure and electrophysiological properties\, and 2) synapses are highly plastic and subject to activity-dependent changes in strength\, which can be mathematically formalized by rules such as spike-timing-dependent plasticity.\n\n\nMethods\nWe address this question in networks of quadratic integrate-and-fire (QIF) neurons endowed with STDP. We develop a multi-population mean-field model [1] that incorporates spike synchronization\, allowing it to reproduce synaptic weight evolution in heterogeneous spiking neural networks — something conventional rate models fail to capture. Mathematically\, synaptic evolution is driven by variables that trace past spiking events with time constants determined by the respective learning rule [2].\n\n\nResults\nWe find that the evolving connectivity patterns are the natural result of an interaction between neural heterogeneity and STDP. The mean-field model captures complex network structure even when it is relatively coarse-grained compared to the network of QIF neurons. As potential applications\, we demonstrate that this model can flexibly store associative memory items\, and encode memory sequences with repeating items.\n\n\nDiscussion\nWe conclude that the mean-field model can accurately predict synaptic pattern formation in heterogeneous spiking networks. Not only can the model be used for analysis methods such as bifurcation analysis that are not available for discontinuous spiking neuron models\, but it should also be applicable for a larger family of synaptic plasticity rules [3].\n\n\nReferences\n[1] Richard Gast\, Thomas R. Knösche\, and Helmut Schmidt (2021). Mean-field approximations of networks of spiking neurons with short-term synaptic plasticity. Physical Review E\, 104(4):044310.\n\n\n[2] Morrison\, Abigail\, Markus Diesmann\, and Wulfram Gerstner (2008). Phenomenological models of synaptic plasticity based on spike timing. Biological Cybernetics 98(6): 459-478.\n\n\n[3] Pfister\, Jean-Pascal\, and Wulfram Gerstner (2006). Triplets of spikes in a model of spike timing-dependent plasticity. Journal of Neuroscience 26(38): 9673-9682.\n\nAcknowledgement\nThis work was supported by a Lumina-Quaeruntur fellowship (LQ100302301 awarded to H.S.) founded by the Czech Academy of Sciences\, the Czech Science Foundation (No. 25-15412L)\, and the Brain dynamics project (No. CZ.02.01.01/00/22_008/0004643) funded by the European Regional Development Fund.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:5884e9fca488e8ba2b3db8eeef4c679f
URL:http://cns2026.sched.com/event/5884e9fca488e8ba2b3db8eeef4c679f
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SUMMARY:P043: Modeling Network Effects of Transcranial Magnetic Stimulation on Obsessive Compulsive Disorder
DESCRIPTION:Introduction\n\nTranscranial magnetic stimulation (TMS) induces electric fields (E-fields) that propagate through white matter pathways\, influencing distributed brain networks beyond the stimulation site. Deep TMS using the H7 coil is an FDA-cleared treatment for obsessive-compulsive disorder (OCD)\, targeting the medial prefrontal cortex (mPFC) and anterior cingulate cortex (ACC)\, key nodes of the cortico-striato-thalamo-cortical (CSTC) circuit. However\, how individual differences in E-field distribution and network propagation contribute to clinical response remains unclear. We hypothesized that a dose-dependent local neuronal response\, amplified through structural connectivity\, predicts treatment outcome following deep TMS in OCD.\n\n\nMethods\n\nTwenty-two patients with OCD received 6 weeks of TMS (20 Hz\, 100% rMT) targeting the mPFC/ACC. Individual E-field distributions were computed and averaged within 90 AAL brain regions. Network activation was modeled as A = (I − αC)-1 r\, where C is the structural connectome\, α is the network coupling strength\, and r = f(e\; β\, θ) represents the local neuronal response to E-field magnitude e. Four neuronal response models were evaluated: excitatory/inhibitory sigmoid\, exponential decay\, and biphasic. (α\, β\, θ) were optimized across CSTC ROIs via leave-one-subject-out cross-validation. Predicted network activation was correlated with percentage improvement on the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS\, Spearman).\n\n\nResults\n\nAcross four candidate neuronal response models\, only the biphasic model yielded a cross-validated prediction that survived FDR correction (Fig. 1). Network activation within the left superior orbital frontal region was positively correlated with percentage Y-BOCS improvement (r = 0.65\, p_fdr = 0.012). The optimized parameters (β = 0.189\, θ = 37.78\, α = 0.879) defined an optimal E-field window of ~20–38 V/m\; stimulation beyond this range produces reduced neuronal responses. The strong coupling (α = 0.879) indicates local effects are greatly amplified through structural connectivity. Parameter estimates were highly stable across LOSO folds (interquartile range = 0 for all parameters).\n\n\n\nDiscussion\n\nThese findings suggest that individual differences in therapeutic response to deep TMS may depend on both local E-field magnitude and its propagation through long-range structural networks. Furthermore\, the left frontal superior orbital region might be an effective TMS target for OCD treatment. The biphasic dose-response suggests an optimal E-field window for therapeutic stimulation. Below this window\, stimulation is insufficient to drive meaningful neuronal responses\; above it\, excessive E-field likely reduces neuronal output\, forming an inverted-U dose-response relationship. Together\, these results support a network-based framework for the mechanisms of deep TMS in OCD and highlight the importance of individualized E-field optimization.\n\nFigure 1.&nbsp\;Correlations between E-field/network activation and clinical outcome in the CSTC ROIs. (a) Raw E-field showed no significant correlations. (b) The biphasic neuronal response model identified the left superior orbital frontal cortex as significantly associated with percentage Y-BOCS improvement. (c-f) Network activation/raw E-field correlation maps\, and fitted biphasic neuronal response f​References\n\n1. Harel\, M.\, Perini\, I.\, Kämpe\, R.\, Alyagon\, U.\, Shalev\, H.\, Besser\, I.\, ... & Zangen\, A. (2022). Repetitive transcranial magnetic stimulation in alcohol dependence: a randomized\, double-blind\, sham-controlled proof-of-concept trial targeting the medial prefrontal and anterior cingulate cortices. Biological psychiatry\, 91(12)\, 1061-1069.\n2. Burguiere\, E.\, Monteiro\, P.\, Mallet\, L.\, Feng\, G.\, & Graybiel\, A. M. (2015). Striatal circuits\, habits\, and implications for obsessive–compulsive disorder. Current opinion in neurobiology\, 30\, 59-65.\n\n\n\nAcknowledgement\nNo
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:99f23a83a8fb4ac741bca30410ddd784
URL:http://cns2026.sched.com/event/99f23a83a8fb4ac741bca30410ddd784
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SUMMARY:P044: Discovering the dynamics of evoked responses to near-threshold tactile stimuli: A layer-specific neural mass model of the somatosensory microcircuit
DESCRIPTION:Introduction\nThe processing of tactile stimuli relies on complex dynamics within cortical microcircuits. Near the perceptual threshold\, unperceived stimuli evoke somatosensory responses that differ from perceived ones\, a phenomenon extensively studied with EEG [1]. However\, the neural mechanisms underlying this transition are poorly understood. Neural mass models are a tool to describe dynamics of cortical circuits and give a mechanistic understanding of their functions. Here\, we present such a model to investigate these unknown dynamics.\n\n\nMethods\nThe model consists of two cortical columns representing the primary and secondary somatosensory cortex\, each with granular\, supra-\, and infra-granular layers. It includes pyramidal neurons and interneuron populations of three different types (somatostatin-\, parvalbumin-\, and vasoactive-intestinal-peptide-expressing interneurons). Mean firing rate and membrane potential are defined based on the Jansen-Rit model [2]. Connectivity\, cell counts and synaptic properties are obtained from animal studies and previous models [3\,4].\n\n\nResults\nOur approach reveals the characteristics of the somatosensory microcircuit dynamics with respect to model parameters. The model allows for precise predictions of how connectivity pattern and excitation-inhibition balance of each neuronal population shapes its individual functional role in generating tactile evoked responses and in letting input pass to higher cortical areas\, reflecting the perception of the tactile stimulus. In combination with feedforward bottom-up input\, top-down input from higher areas influences perceptual gating. An observation model transforms firing rates and membrane potential into EEG- and LFP-like signals\, allowing future fitting to real recordings to improve interpretability and validity.\n\n\nDiscussion\nOur model offers a biologically plausible approach to investigate somatosensory perception. It provides hypothetical mechanisms underlying the processing of tactile stimuli and the transition from subliminal to supraliminal responses. By bridging the gap between macroscopic measurements and microscopic neural dynamics\, this model enhances our understanding of the mechanisms underlying tactile perception at multiple levels. Future work will fit the model to empirical data from near-threshold detection tasks.\n\n\nReferences\nForschack\, N.\, Nierhaus\, T.\, Müller\, M. M.\, & Villringer\, A. (2020). Dissociable neural correlates of stimulation intensity and detection in somatosensation. NeuroImage\, 217\, 116908.\nJansen\, B. H.\, & Rit\, V. G. (1995). Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns. Biological Cybernetics\, 73(4)\, 357–366.\nIsbister\, J. B.\, Ecker\, A.\, Pokorny\, ... & Reimann\, M. W. (2024). Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part II: Physiology and Experimentation. bioRxiv.\nJiang\, H.-J.\, Qi\, G.\, Duarte\, R.\, Feldmeyer\, D.\, & Albada\, S. J. van. (2023). A Layered Microcircuit Model of Somatosensory Cortex with Three Interneuron Types and Cell-Type-Specific Short-Term Plasticity. bioRxiv.\nAcknowledgement\nThis work was supported by the IMPRS programs. We thank the members of our group BrainNets&nbsp\; and the Neurology department for insightful discussions and continuous feedback. We are also grateful to previous interns who contributed to preliminary simulations.\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:cad71a2dab7486dc4856035bdeda3e21
URL:http://cns2026.sched.com/event/cad71a2dab7486dc4856035bdeda3e21
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SUMMARY:P045: A biophysical model of coil-orientation dependent Transcranial Magnetic Stimulation (TMS) evoked I-waves in the motor cortex
DESCRIPTION:Introduction\nTranscranial magnetic stimulation (TMS) is a promising non-invasive neuromodulation procedure. The magnetic field generated by the TMS coil induces a short-lasting electric field and elicits firing in targeted cortical neurons. In experiments targeting the human motor cortex\, TMS produced repetitive descending cortical volleys known as D- and I-waves representing sustained strong firing activity for about 10ms post stimulation. The underlying biophysical mechanisms remains incompletely understood [1]. We aim to address this gap by leveraging novel modeling approaches.\n\nMethods\nWe propose a novel model of I-wave generation (Fig. 1) A). Our model builds upon a recent electric-field-coupling approach that computes precise somatic current fluctuations of Layer 5 pyramidal tract neurons (L5PT) in the motor cortex [2]. This approach reproduces the sensitivity of an average input current to those neurons to changes in the TMS-coil orientation. The activity of the L5PT neurons is modeled by a Fokker-Planck-based stochastic population model\, which allows for the recovery of the membrane potential distribution of the targeted neurons as well as a spike density. From this spike density we further compute a voltage signal that can be compared to epidural recordings of I-waves at the spinal cord.\n\nResults\nOur model is able to replicate signal characteristics of I-waves [3\, 4] within biophysically plausible parameter ranges (Fig. 1) B). It further reproduces key experimental findings\, including the sensitivity of I-waves to coil orientation\, electric field strength\, and synaptic parameters. Finally\, we extended the analysis of quantitative I-wave characteristic by peak-to-peak delays and amplitude ratios that may be more tractable for experimental comparison and compared those between our model and existing data.\n\nDiscussion\n\nUsing our method\, we were able to reproduce I-wave characteristics in unprecedented detail. The use of a neural population model for computing the I-waves allowed for a sophisticated analysis of the influence of many parametric dependencies that are commonly reserved for computationally inexpensive neural mass models\, while still retaining sudden transient effects that are outside the applicability of traditional mean field models. Our comparison to measured data proved this approach to be promising and gives rise to a bottom-up biophysically based parsimonious I-wave model that may enable predictions for changes of motor pathways under various influences\, such as plasticity\, medication\, or pathology.\n\nFigure 1.&nbsp\;A) The somatic current model [3] generates coil orientation-sensitive somatic currents (first column). They are then applied to the L5PT model which computes membrane potential distribution and spike density for it (second column). This is then transformed to a potential and compared to measured I-waves. B) Orientation dependency of I-waves (first column) and potentials for putative parietal-anter​\n\nReferences\n1. Ziemann\, U. (2020). I-waves in motor cortex revisited.&nbsp\;Exp. brain research\,&nbsp\;238(7)\, 1601-1610.\n2. Miller\, A.\, Knösche\, T. R.\, & Weise\, K. (2025). A coupling model of transcranial magnetic stimulation induced electric fields to neural state variables.&nbsp\;bioRxiv\, 2025-08.&nbsp\;\n3. Di Lazzaro\, V.\, & Ziemann\, U. (2013). The contribution of transcranial magnetic stimulation in the functional evaluation of microcircuits in human motor cortex.&nbsp\;Front. in neural circ.\,&nbsp\;7\, 18.&nbsp\;\n4. Di Lazzaro\, V.\, Pilato\, F.\, Oliviero\, A.\, Dileone\, M.\, Saturno\, E.\, Mazzone\, P.\, ... & Rothwell\, J. C. (2006). Origin of facilitation of motor-evoked potentials after paired magnetic stimulation: direct recording of epidural activity in conscious humans.&nbsp\;Jrnl of neurophys.\,&nbsp\;96(4)\, 1765-1771.&nbsp\;\n\n\n\nAcknowledgement\n-
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:6c9965efab98e28767a22fde3526a745
URL:http://cns2026.sched.com/event/6c9965efab98e28767a22fde3526a745
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DTSTAMP:20260708T114850Z
DTSTART:20260712T192000Z
DTEND:20260712T212000Z
SUMMARY:P046: Phase-Dependent Deep Brain Stimulation to Suppress Pathological Neural Oscillations in Parkinson’s Disease
DESCRIPTION:Introduction\nParkinson's disease (PD) is characterised by elevated oscillatory activity in the low-beta frequency range (13–20 Hz)\, a hallmark correlated with hypokinesia and is thought to arise from pathophysiological changes within the basal ganglia thalamocortical (BGTC) network. Phase-dependent deep brain stimulation (pdDBS) is a proposed alternative stimulation strategy to clinically standard high-frequency DBS\, delivering pulses at targeted phases of the beta oscillatory cycle to either synchronise or desynchronise oscillatory amplitude. Despite experimental and computational work in the field\, existing investigations of pdDBS have yet to examine how patient-specific responses to stimulation shape oscillatory dynamics at the network level.\n\nMethods\nWe employ a BGTC neural mass model [1]\, to investigate the effects of phase-dependent DBS across 8 populations in the BGTC network (Figure 1). Model parameters are estimated through simulation-based inference using biologically informed priors\, fit to reproduce pathological oscillatory activity seen in PD [2]. We use a novel method for modelling STN DBS incorporating orthodromic and antidromic invasion of axonal collaterals [3]\, describing how stimulation perturbs effective firing rates across the network. Approximate Bayesian Computation is used to optimise DBS activation parameters and stimulation phase for maximal suppression of oscillatory activity.\n\nResults\nConsistent with existing models\, the BGTC model successfully reproduces plausible firing rates and spectra in line with animal models. Applying the optimisation framework across a range of STN DBS activation parameters\, initial results show that the optimal stimulation phase for oscillatory suppression depends on the relative activation of fibre pathways during stimulation. Consistent with experimental findings\, this suppression is accompanied by increased oscillatory activity at adjacent frequency bands.\n\nDiscussion\nThis work offers a framework for understanding patient-specific responses to phase-dependent DBS\, showing that optimal stimulation phases are not universal but vary according to how DBS perturbs the BGTC network through different fibre pathways. Beyond its immediate relevance to optimising phase-dependent DBS in PD\, the framework generalises to various pulsatile brain stimulation strategies aimed at shifting oscillatory dynamics towards less pathological states. Ongoing work will extend the analysis to network level responses to stimulation and evaluating responses when GPi is used as the stimulation target.\n\nFigure 1.&nbsp\;The BGTC circuit connectivity implemented in the neural mass model includes cortical excitatory (E)\, inhibitory interneuron (II)\, and deep pyramidal (DP) populations\, as well as the striatum\, globus pallidus externus (GPe)\, globus pallidus internus (GPi)\, subthalamic nucleus (STN)\, and thalamic relay nuclei (REL). Shaded regions indicate nodes with spectral data used for model fitting.​\n\nReferences\n1.&nbsp\;&nbsp\;&nbsp\;&nbsp\; van Albada\, S. J.\, et al. (2009). Mean-field modeling of the basal ganglia-thalamocortical system. II: Dynamics of parkinsonian oscillations. Journal of Theoretical Biology\, 257(4)\, 664–688. https://doi.org/10.1016/j.jtbi.2008.12.013\n2.&nbsp\;&nbsp\;&nbsp\;&nbsp\; West\, T. O.\, et al. (2022). Stimulating at the right time to recover network states in a model of the cortico-basal ganglia-thalamic circuit. PLOS Computational Biology\, 18(3)\, e1009887. https://doi.org/10.1371/journal.pcbi.1009887\n3.&nbsp\;&nbsp\;&nbsp\;&nbsp\; Crompton\, et al. (2025\, April 24). A Unified Computational Framework for Implementing Impact of Deep Brain Stimulation in Neural Circuits. [Conference Presentation]. Krembil Research Day\, Toronto\, Canada\n\nAcknowledgement\nWe acknowledge the financial support of the Branch Out Neurological Foundation and the Max-Planck Center for Neural Science and Technology (P.K) as well as the Natural Sciences and Engineering Council (NSERC) RGPIN-2022-05181 (L.M).
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:f9f7d2cab27eba537a8439ec45436a53
URL:http://cns2026.sched.com/event/f9f7d2cab27eba537a8439ec45436a53
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DTSTAMP:20260708T114850Z
DTSTART:20260712T192000Z
DTEND:20260712T212000Z
SUMMARY:P047: Feature Extraction of Neuronal Morphology with a Variational Auto-Encoder
DESCRIPTION:Introduction\nA single pyramidal neuron can compute XOR using its dendritic structure [1]. This suggests that neuronal morphology is closely related to computational capability. Since neurons exhibit diverse morphologies [2]\, individual neurons may possess distinct computational capabilities. To reveal the relationship between neuronal morphology and computational capability\, simulation experiments using neuron models with diverse morphologies are effective. However\, constructing realistic neuron models that capture the morphologies of neurons is difficult because the essential features that characterize neuronal morphology are poorly understood. This study aims to investigate whether a Variational Auto-Encoder (VAE) is effective for feature extraction.\n\nMethods\nA toy neuron dataset consisting of single-branch neurons was created to train the VAE. The toy neurons were generated using several morphometric features\, including node type (elongation\, branch\, or terminal)\, angle\, and elongation length. The VAE was trained to map input neuronal morphologies to a low-dimensional latent space and reconstruct them from the space. The space was analyzed using latent traversal [3]. In this method\, the VAE was first inputted a toy neuron morphology from the dataset\, which was then reconstructed. Next\, the value of one variable spanning the space was varied while the others were fixed. We then evaluated which morphometric features the latent variable represented based on changes in the reconstructed morphology.\n\nResults\nThe VAE successfully reconstructed morphologies that resembled the toy neurons. The reconstructed morphologies gradually varied in response to changes in the latent variables. Those changes reflected the morphometric features used to create the toy neurons. When the input toy neuron data were replaced with another neuron\, the features represented by each latent variable sometimes differed\; however\, across all variables in the latent space\, all the features were extracted.\n\nDiscussion\nThese results suggest that the VAE is a useful approach for extracting morphometric features. The dependence of the extracted features on the input morphology suggests that the VAE may implicitly cluster training data in the latent space and extract cluster-specific morphometric features. Future work is to apply the proposed approach to real neuronal data\, such as pyramidal neurons.\n\nReferences\n[1] Gidon\, A.\, et al. (2020). Dendritic action potentials and computation in human layer 2/3 cortical neurons.&nbsp\;Science. 367:83–87.\n[2] Peng\, H.\, et al. (2021). Morphological diversity of single neurons in molecularly defined cell types.&nbsp\;Nature. 598 (7879):174–181.\n[3] Burgess\, C. P.\, et al. (2018) Understanding disentangling in beta-VAE. arXiv preprint arXiv:1804.03599.\n\n\nAcknowledgement\nThis research was supported by AMED under Grant Number JP25wm0625418h0001.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:576bc6b596d42767259c4239eb027fad
URL:http://cns2026.sched.com/event/576bc6b596d42767259c4239eb027fad
END:VEVENT
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DTSTAMP:20260708T114850Z
DTSTART:20260712T192000Z
DTEND:20260712T212000Z
SUMMARY:P048: Noise-Driven Spiking Dynamics and Synchronization Transitions in an Ensemble of Neuromorphic Oscillators
DESCRIPTION:Introduction\nSome neural systems are assumed to operate near a critical point between order and disorder\, where collective dynamics enable efficient information processing and computational capabilities [1\,2]. Neuromorphic hardware systems provide an experimental platform for investigating such network dynamics in an engineered system. Networks of coupled oscillators have been proposed as promising systems for studying synchronization\, stochastic spiking dynamics\, and potential signatures of critical dynamics in neural-like systems [3]. We study the synchronization behavior of a network of stochastic relaxation-type oscillators driven by noise that generate neuron-like spiking in a neuromorphic hardware system with event-based spike timing readout.\n\nMethods\nWe experimentally investigate a network of 36 neuron-inspired oscillators. Each node of the network is implemented as a relaxation-type oscillator based on a programmable unijunction transistor\, which implements neuron-like threshold firing dynamics. Stochastic spike generation is induced through externally injected electrical noise\, leading to Poisson-like spiking dynamics. The oscillators are coupled via resistive connections in an all-to-all topology with tunable coupling strength. Spike events are recorded using a novel event-based readout system that captures the precise spike times for all oscillators\, enabling spike-train based analysis of the resulting network dynamics.\n\nResults\nTo quantify synchronization in the oscillator network\, we compute the mean spike time tiling coefficient (STTC) across all oscillator pairs\, a spike-train based correlation measure that quantifies temporal spike coincidences while remaining robust to differences in firing rate. While systematically varying the coupling resistances\, the mean STTC increases continuously with coupling strength\, indicating a gradual emergence of collective synchronization in the oscillator network (Fig. 1). This behavior is consistent with theoretical predictions for synchronization transitions in ensembles of coupled oscillators\, where increasing coupling promotes phase locking and collective dynamics across the network.\n\nDiscussion\nPhase transitions are frequently discussed in the context of collective neural dynamics and potential signatures of criticality in the brain [1\,2\,4]. While the observed behavior is consistent with a continuous synchronization transition\, the presence of such a transition alone does not constitute sufficient evidence for criticality. Additional signatures such as scale-free activity statistics or critical scaling of network correlations are required to establish critical dynamics. Our results show that neuromorphic oscillator networks provide a controllable experimental platform for studying collective spike dynamics. Future work will investigate statistical indicators of criticality and the influence of coupling architecture and noise.\n\nFigure 1.&nbsp\;Mean spike time tiling coefficient (STTC) as a function of coupling strength in a network of 36 coupled stochastic relaxation-type oscillators. Increasing coupling drives the system from weakly correlated spiking activity toward global synchronization\, indicating a continuous synchronization transition.​\n\nReferences\n1.&nbsp\;Beggs\, J. M.\, & Plenz\, D. (2003). Neuronal Avalanches in Neocortical Circuits. Journal of Neuroscience\, 23(35)\, 11167-11177. https://doi.org/10.1523/JNEUROSCI.23-35-11167.2003\n2. Shew\, W. L.\, & Plenz\, D. (2012). The Functional Benefits of Criticality in the Cortex. Neuroscientist\, 19(1)\, 88-100. https://doi.org/10.1177/1073858412445487\n3. Feketa\, P.\, Meurer\, T.\, & Kohlstedt\, H. (2022). Structural plasticity driven by task performance leads to criticality signatures in neuromorphic oscillator networks. Scientific Reports\, 12(1)\, 15321. https://doi.org/10.1038/s41598-022-19386-z\n4.&nbsp\;Chialvo\, D. (2010). Emergent complex neural dynamics. Nature Physics\, 6(10)\, 744-750. https://doi.org/10.1038/nphys1803\n\nAcknowledgement\n-
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:086082f7deadcfb4379d022e3f6e8296
URL:http://cns2026.sched.com/event/086082f7deadcfb4379d022e3f6e8296
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DTSTAMP:20260708T114850Z
DTSTART:20260713T111500Z
DTEND:20260713T200000Z
SUMMARY:Registration
DESCRIPTION:\n
CATEGORIES:REGISTRATION/ORGANIZATION
LOCATION:TBA\, Halifax\, NS\, Canada
SEQUENCE:0
UID:301dc890c1fd4c728a74924fdc11ac70
URL:http://cns2026.sched.com/event/301dc890c1fd4c728a74924fdc11ac70
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DTSTAMP:20260708T114850Z
DTSTART:20260713T120000Z
DTEND:20260713T121000Z
SUMMARY:Announcements
DESCRIPTION:\n
CATEGORIES:ANNOUNCEMENTS
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:5b2c1ac50e2aa0f818aa8d4f0b1bf0f6
URL:http://cns2026.sched.com/event/5b2c1ac50e2aa0f818aa8d4f0b1bf0f6
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DTSTAMP:20260708T114850Z
DTSTART:20260713T121000Z
DTEND:20260713T131000Z
SUMMARY:Keynote 3: Blake Richards\, "Exponentiated gradients support effective learning in biologically relevant scenarios"
DESCRIPTION:Computational neuroscience relies on gradient descent (GD) for training artificial neural network (ANN) models of the brain. The advantage of GD is that it is effective at learning difficult tasks. However\, it produces ANNs that are a poor phenomenological fit to biology\, making them less relevant as models of the brain. Specifically\, it violates Dale’s law\, by allowing synapses to change from excitatory to inhibitory\, and leads to synaptic weights that are not log-normally distributed\, contradicting experimental data. Here\, starting from first principles of optimization theory\, I will present an alternative learning algorithm\, exponentiated gradient (EG)\, that respects Dale’s Law and produces log-normal weights\, without losing the power of learning with gradients. We show that in biologically relevant settings EG outperforms GD\, including learning from sparsely relevant signals and dealing with synaptic pruning. Altogether\, our results show that EG is a superior learning algorithm for modelling the brain with ANNs.\n\n \n
CATEGORIES:KEYNOTE
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:c94c7c6a6f182ddeb1345760f71e6d5a
URL:http://cns2026.sched.com/event/c94c7c6a6f182ddeb1345760f71e6d5a
END:VEVENT
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DTSTAMP:20260708T114850Z
DTSTART:20260713T131000Z
DTEND:20260713T134000Z
SUMMARY:Coffee break
DESCRIPTION:\n
CATEGORIES:BREAK
LOCATION:Ballroom Salon\, Halifax\, NS\, Canada
SEQUENCE:0
UID:2d72faf74a7c36a9d7decff03660860c
URL:http://cns2026.sched.com/event/2d72faf74a7c36a9d7decff03660860c
END:VEVENT
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DTSTAMP:20260708T114850Z
DTSTART:20260713T134000Z
DTEND:20260713T141000Z
SUMMARY:FO3: Gene Gradients Reveal Directed Structural Connectivity Across Species
DESCRIPTION:Benjamin S. Sipes*1\, Ashish Raj11Department of Radiology and Biomedical Imaging\, University of California\, San Francisco\, San Francisco\, CA\, United States*Email: benjamin.sipes@ucsf.edu\n\nIntroduction\nDiffusion MRI (dMRI) tractography estimates the brain's white matter structural connectivity (SC) in vivo\, but it cannot resolve the directionality of white matter pathways. Yet\, much recent work has shown that genes and gene co-expression maps relate to SC across species [1-4]. Here we test whether gene co-expression gradients can infer connection directionality from undirected structural connectivity using the brain’s structure–function relationship.\n\nMethods\nWe introduce asymmetry to SC (C) via a similarity transform with a node-level gauge parameterized by genetic gradients: C̃=ACA^-1\, where A=diag(e^{Ga})\, with G=[g_1\,...\,g_k] genetic gradient vectors and a=[a_1\,...\,a_k]^T gradient weights. We learn gradient weights by fitting a higher order network diffusion (HONeD) model of the SC graph Laplacian\, ℒ=I-C̃D_{in}^-1\, f(ℒ)=-κI-βℒ+ξℒ^2\, to the residual of the Lyapunov equation\, f(ℒ)^TΣ+Σf(ℒ)+I [5\,6]\, with stationary covariance (Σ) estimated from functional neuroimaging. We compared our model's performance to ground truth directionality in three species: C. elegans\, mouse\, and macaque [7-10]. We then ran our model on 770 HCP subjects [11\,12]. Public datasets supplied gene expression [13-17].\n\nResults\nModel-predicted directionality significantly correlated with ground-truth directed edges in all three species. Our model predicted neuron-to-neuron synaptic directionality in C. elegans (r=0.56\, p&lt\;10^-253) and tracer-based directionality in mouse (r=0.57\, p&lt\;10^-37) and macaque (r=0.46\, p&lt\;10^-44) (Fig.1a-b). The optimal numbers of genetic gradients was also different in each species (C. elegans: k=3\; Mouse: k=5\; Macaque: k=1). We found that humans had optimal test-retest reliability when using k=5 genetic gradients (ICC=0.46). Human predicted degree asymmetry suggests that the hippocampus and posterior cingulate are network sources while temporal poles are network sinks (Fig.1c).\n\nDiscussion\nAlthough white matter pathways exhibit directionality\, estimating their orientation has largely been restricted to tracer-based experiments and a small number of specialized imaging methods. Our results suggest that gene gradients combined with structure–function modeling provide a biologically grounded framework for inferring directed structural connectivity across species\, supporting the idea that molecular gradients may encode directional biases in large-scale brain networks. Estimating human SC directionality is valuable not only for basic neuroscience\, but also for evaluating circuit-level models of brain function and for studying diseases such as Alzheimer’s\, Parkinson’s\, and ALS that may propagate along structural pathways [18].\n\nFigure 1.&nbsp\;(a) Model-estimated directionality parameters (e^{Ga}) for the three non-human species: C. elegans (top)\, Mouse (middle)\, Macaque (bottom). In the C. elegans plot\, each dot represents a single neuron. (b) Scatter plots comparing empirical to predicted skew edges with Pearson correlations listed at the top left (all p&lt\;10^{-37}). (c) Predicted human overall degree asymmetry for 414 brain regions.​
CATEGORIES:ORAL SESSION FEATURED
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:e7738df977d67aabba14327f9201b9c8
URL:http://cns2026.sched.com/event/e7738df977d67aabba14327f9201b9c8
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DTSTAMP:20260708T114850Z
DTSTART:20260713T141000Z
DTEND:20260713T143000Z
SUMMARY:O9: Low-Dimensional Communication Subspaces Reveal Distributed Information Across Neural Areas
DESCRIPTION:Farzad Karimi*1\,2\, Javier G. Orlandi1\,21Department of Physics and Astronomy\, University of Calgary\, Calgary\, Canada2&nbsp\;Hotchkiss Brain Institute\, University of Calgary\, AB\, Canada*Email: farzad.karimi1@ucalgary.ca\n\nIntroduction\nRecent technological advances allowing us to simultaneously record across thousands of neurons have revealed the presence of distributed representations across the brain [1]. However\, the network processes and information pathways that create these distributed representations are still poorly understood. To identify these distributed representations\, we measured shared information across brain areas\, by introducing a new directed connectivity measure\, Reduced Rank Connectivity (RRC). RRC is defined through communication subspaces between neural areas\, and by comparing these subspaces we can measure the extent of distributed signals across the brain.\n\nMethods\nWe analyzed Neuropixels recordings from the Allen Institute from 54 mice performing a go/no-go visual change detection task\, focusing on six visual cortical areas (V1\, LM\, AL\, RL\, AM\, PM)\, as well as the thalamus (LP) and hippocampus (CA1)\, across two sessions: active behavior and passive replay [2]. To estimate shared information\, we applied Reduced Rank Regression (RRR) [3]\, which predicts target activity from a low-dimensional subspace of a source population. We define the total predictable target activity as a new connectivity measure\, called RRC\, and distances between subspaces quantify the similarity of shared information across neural areas.\n\nResults\nWe applied RRR to cortical and subcortical areas to analyze information flow across all area pairs combinations. We showed that model performance\, defined as the squared correlation between predicted and test data\, saturated with only a few predictive dimensions. These results identify low-dimensional communication subspaces between neural areas (Fig. 1a). We observed consistent shared information across the visual cortex\, while predictability was lower for subcortical areas (Fig. 1b). Connectivity computed using RRR differed significantly from structural connectivity [4] (Fig. 1c). Our results also show that RRC is modulated by the animal’s engagement with the task (active vs. passive).\n\nDiscussion\nUsing RRR on multi-area cortical recordings\, we identified robust shared information across visual areas during a discrimination task. RRR performance provides a connectivity measure that captures predictive subspaces rather than coarse averages. The results suggest the presence of low-dimensional communication subspaces between neural areas. Cortical areas can be more easily predicted by their own activity than subcortical areas through these communication subspaces during visual processing. RRC differed from structural connectivity and was modulated by behavioral state.\n\nFigure 1.&nbsp\;Low-dimensional communication subspaces define RRC. (a) Prediction performance vs. rank\; saturation defines optimal number of ranks and RRC. (b) Average RRC across animals\; cortical areas are more predictable than subc
CATEGORIES:ORAL SESSION TALK
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:348d9813f142c86f3abe141a16cb7ae1
URL:http://cns2026.sched.com/event/348d9813f142c86f3abe141a16cb7ae1
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DTSTAMP:20260708T114850Z
DTSTART:20260713T143000Z
DTEND:20260713T145000Z
SUMMARY:O10: A mathematical language for large-scale spike recordings from hundreds to thousands of neurons
DESCRIPTION:Alexandra Busch*\,1\,2\,3\, Roberto Budzinski2\,4\, Lyle Muller1\,2\,3\n1 Department of Mathematics\, Western University\, London ON\, Canada\n2 Fields Lab for Network Computation\, Fields Lab\, Toronto ON\, Canada\n3 Western Institute for Neuroscience\, Western University\, London ON\, Canada\n4 &nbsp\;Department of Neuroscience\, University of Lethbridge\, Lethbridge AB\, Canada\n*&nbsp\;Email:&nbsp\;abusch5@uwo.ca\n\nIntroduction\nRecent technological advances now allow simultaneously recording the activity of thousands of neurons while animals engage in cognitive tasks. These datasets can offer an unprecedented window into how the brain computes in real time\, but they also challenge existing analytical frameworks. There has been increasing interest in the possibility that coordinated patterns of spikes\, such as sequences\, may contribute to neural computation [1-3]. However\, in contrast to the many methods available for analyzing firing rates\, mathematical tools capable of systematically probing spike-time structure at the scale of these next-generation datasets remain limited.\n\nMethods\nWe introduce a decomposition operator for population spike patterns\, termed the multi-sample Discrete Helix Transform (ms-DHT). We derive a generalized inner product that allows the ms-DHT to operate directly on patterns of discrete spikes across thousands of neurons without smoothing. The ms-DHT decomposes these spike patterns into a fixed\, interpretable basis\, mapping each input pattern to a unique vector that captures the occurrence and timing of every spike (Fig.1). In this representation\, distances between spike patterns reduces to the Euclidean distance between their ms-DHT outputs. This distance is invariant to neuron ordering and allows detecting repeating structure ranging from simple spike sequences to complex population motifs.\n\nResults\nWe demonstrate several applications of the ms-DHT to large-scale datasets. Notably\, in dual Utah array recordings from the prefrontal cortex of a macaque monkey performing a virtual reality working memory task\, the ms-DHT reveals structured spike motifs that predict specific behavioural errors on single trials - before they occur. Further\, applications to spiking network simulations with 10\,000 neurons demonstrate that the transform operates effectively at the scale of next-generation neural recordings.\n\nDiscussion\nThe ms-DHT provides a flexible framework for analyzing large-scale spike patterns. By decomposing spiking activity onto a fixed\, interpretable basis using a generalized inner product\, the ms-DHT produces unique descriptions of population activity even when neurons emit variable numbers of spikes—a setting that has posed a central challenge for analytical approaches. The resulting representation supports multiple analyses\, including clustering and decoding of full spike patterns\, detecting repeating substructure through specific helix contributions\, and sliding-window analyses that trace the temporal evolution of spike patterns across long recordings.\n\nFIgure 1.&nbsp\;Decomposing spike patterns. The ms-DHT maps a spike pattern (a) to a unique complex-valued vector (b). Each component encodes the strength (amplitude) and timing (phase) of a basis sub-pattern. (c) Distances between spike patterns reduce to Euclidean distances between ms-DHT outputs\, which are invariant to neuron order\, ensuring behaviourally relevant clusters do not depend on neuron order.​\n\nReferences\n[1] Xie\, W.\, Wittig\, J. H.\, Chapeton\, J. I.\, El-Kalliny\, M.\, Jackson\, S. N.\, Inati\, S. K.\, & Zaghloul\, K. A. (2024). Neuronal sequences in population bursts encode information in human cortex. Nature\, 635(8040)\, 935–942. https://doi.org/10.1038/s41586-024-08075-8\n[2] Chettih\, S. N.\, Mackevicius\, E. L.\, Hale\, S.\, & Aronov\, D. (2024). Barcoding of episodic memories in the hippocampus of a food-caching bird. Cell\, 187(8)\, 1922–1935.e20. https://doi.org/10.1016/j.cell.2024.02.032\n[3] Busch\, A.\, Roussy\, M.\, Martinez-Trujillo\, J. C.\, et al. (2024). Neuronal activation sequences in lateral prefrontal cortex encode visuospatial working memory during virtual navigation. Nature Communications\, 15\, 4471. https://doi.org/10.1038/s41467-024-48664-9\n\nAcknowledgments\nThis work was supported by NSERC\, CFREF\, NIH\, Neuronex NSF\, and Canada Research Chairs Program. A.B. gratefully acknowledges a BrainsCAN studentship and NSERC CGS-D.&nbsp\;\n\n\n
CATEGORIES:ORAL SESSION TALK
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:77493f4cc87ac70257e593e0e31cca5d
URL:http://cns2026.sched.com/event/77493f4cc87ac70257e593e0e31cca5d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260713T145000Z
DTEND:20260713T151000Z
SUMMARY:O11: SEM to Simulation: Bringing Ultrastructural Detail to Multiscale Modeling
DESCRIPTION:Cecilia Romaro*1\, Matei Coldea2\, William W. Lytton3\,4\, and Robert A. McDougal1\,5\,6\,7\,8\n1 Department of Biostatistics\, Yale School of Public Health\, New Haven\, CT\, United States\n2 Yale College\, Yale University\, New Haven\, CT\, United States\n3 Department of Physiology and Pharmacology & Neurology\, SUNY Downstate Health Sciences University\, Brooklyn\, New York\n4 Department of Neurology\, Kings County Hospital Center\, Brooklyn\, New York\n5 Department of Biomedical Informatics and Data Science\, Yale School of Medicine\, New Haven\, CT\, United States\n6 Program in Computational Biology and Bioinformatics\, Yale University\, New Haven\, CT\, United States\n7 Wu Tsai Institute\, Yale University\, New Haven\, CT\, United States\n8 Interdepartmental Neuroscience Program\, Yale University\, New Haven\, CT\, United States\n* Email: cecilia.romaro@yale.edu\n\nIntroduction\nJust as neuron morphology influences spiking behavior and thus network interactions\, so too does the 3D placement of spines affect interaction between spines [1] and thus cellular behavior. However fine spine details are not visible under the optical microscopy used for reconstructing neuron morphology and full-cell scanning electron microscopy (SEM) images are generally not feasible due to size constraints. To address these challenges\, we developed a tool for the NEURON simulator [2] for importing and editing an SEM reconstruction of a portion of a dendrite\, selecting spines\, rotating them\, and inserting them into a full-cell reconstruction for simulation\, using our experimental support for reaction-diffusion multigridding in NEURON.\n\nMethods\nSEM images may be segmented to identify each spine using standard segmentation software then exported to a TIFF stack. We estimate key electrical properties: approximately equivalent length\, diameter\, volume\, and surface area. Our tool loads the image stack and identifies the voxels forming each spine-dendrite boundary so that we can preserve the connection location after transformations. PySide6 is used to provide a graphical interface allowing spines to be selected and manipulated into position\; this can also be done programmatically. An algorithm adds/removes voxels to connect the spine cleanly. Transformed spines can be exported to text files for easy editing\, enabling iterative refinement.\n\nResults\nWe present our graphical tool\, examples of relevant data sets\, and simulation results. The graphical tool allows visualization of both the loaded SEM data and the placed spines after transformations. The simulations leverage our previous work\, allowing a synaptic source (e.g.\, of IP3) to be placed at a precise 3D location within a spine. We validate the multigrid simulation by comparing to a single unified 3D simulation and contrast it to simplified geometry approximations\, illustrating their similarities and differences. In particular\, our tool allows toggling between the two representations.\n\nDiscussion\nSupport for imported spine morphologies brings NEURON a step closer to capturing the intricacies of the human brain. The same tool described here can also directly be used for incorporating SEM data of a dendrite as well. It is not feasible to simulate full cells and networks at this level of detail\, nor is that necessarily desirable -- simpler models are often more useful for insights -- but our approach allows us to explore localized behavior in detail in a multiscale context with full cell and network simulations. This tool can give us insight on which details model when and allow us to explore detailed biological questions of synaptic plasticity or the role of morphological changes in disease.\n\nReferences\n\n1. Huertas\, M. A.\, Newton\, A. J.\, McDougal\, R. A.\, Sacktor\, T. C.\, & Shouval\, H. Z. (2022). Conditions for synaptic specificity during the maintenance phase of synaptic plasticity. Eneuro\, 9(3). https://doi.org/10.1523/ENEURO.0064-22.2022\n\n2. Hines\, M. L.\, & Carnevale\, N. T. (1997). The NEURON simulation environment. Neural computation\, 9(6)\, 1179-1209. https://doi.org/10.1162/neco.1997.9.6.1179\n\nAcknowledgments\nThis research was funded by the National Institute of Mental Health\, National Institutes of Health\, grant number R01 MH086638. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
CATEGORIES:ORAL SESSION TALK
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:49cbd64c4874fc2110051ffefb802665
URL:http://cns2026.sched.com/event/49cbd64c4874fc2110051ffefb802665
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DTSTAMP:20260708T114850Z
DTSTART:20260713T151000Z
DTEND:20260713T153000Z
SUMMARY:O12: A new platform technology to explore and leverage the computational properties of biological neural cultures
DESCRIPTION:Brett J. Kagan*1\, David Hogan1\, Andrew Doherty1\, &nbsp\;Boon Kien Khoo1\, &nbsp\;Johnson Zhou1\, &nbsp\;Richard Salib1\, &nbsp\;James Stewart1\, &nbsp\;Kiaran Lawson1\, &nbsp\;Alon Loeffler1\,\n1Cortical Labs\, Melbourne\, Australia\n2 The University of Melbourne\, Department of Biochemistry and Pharmacology\, Parkville\, Melbourne\, 3000\, Australia\n\n*Email:brett@corticallabs.com\n\n\nIntroduction\nNeural cultures are increasingly explored to understand the computational properties of neural systems due to the controllability and modifiability of these systems. However\, BNNs can only be explored reliably as reliable information-processing systems if inputs are delivered in a temporally and structurally consistent manner. In practice\, this requires stimulation with precisely controlled structure\, microsecond-scale timing\, multi-channel synchronization\, and the ability to observe and respond to neural activity in real-time. Existing approaches depend on either depend on low-level hardware mechanisms\, imposing prohibitive complexity for rapid iteration\, or they sacrifice temporal and structural control\, undermining consistency.\n\nMethods\nTo resolve this problem. We developed a bespoke but scalable system (the CL1)1&nbsp\;that coupled with a easy to use Application Programming Interface ( CL API)2&nbsp\;to &nbsp\;enables real-time\, sub-millisecond closed-loop interactions with neural cultures. The system itself provides real-time closed-loop electrophysiology with integrated life support. For the API design approach\, the CL API provides users with precise stimulation semantics\, transactional admission\, deterministic ordering\, and explicit synchronization guarantees. This contract is presented through a declarative Python interface\, enabling non-expert programmers to express complex stimulation and closed-loop behavior without managing low-level scheduling or hardware details.\n\nResults\nThe result is a scalable device for interacting with&nbsp\;in-vitro&nbsp\;neural cell cultures via electrophysiology in a closed-loop real-time environment coupled with an integrated life-support system. The devices are server rack stackable\, generating up to 6TB of neural activity data per server rack per day\, allowing detailed analysis of electrophysiological data\, where each unit can run its own embodied environment. This allows an unparalleled investigation of nearly fully controllable neural systems to explore their dynamics in depth. The flexibility of the Cl1 means that information processing and computation in neural cultures can be explored in many ways\, including as reservoir computing\, in robotics4\, or via games such "Pong"5&nbsp\;or “Doom”.\n\nDiscussion\nThe CL1 system coupled with the CL API offers a scalable system for exploring computational dynamics of biological neural networks. Aside from being possible to set up in traditional cell culture laboratories\, these systems can be accessed remotely via the cloud where the cell culture methods are managed either by a dedicated company or by partner laboratory groups. This provides a tool for computational neuroscientists\, who might otherwise not be able to access these neural cultures\, to explore research questions at scale\, with precision\, and with rapid iteration loops. It is proposed that this availability will allow computational neuroscientists to be able to explore the dynamics of biological neural systems in way never possible before.\n\nFigure 1.&nbsp\;The CL-1 device is scalable desktop device compatible with standard server racks that allows real-time closed-loop interactions with neural cells via an MEA reader. The CL-1 has onboard hardware that interprets simple code via a Python API to allow rapid code development and experimental iterations coupled with a closed-loop perfusion circuit to automatically adjusts gas levels and temperature to​\n\nReferences\n1) Kagan\, B. J. (2025). The CL1 as a platform technology to l
CATEGORIES:ORAL SESSION TALK
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:d651d8cb7388fb1427abdf9480aa1944
URL:http://cns2026.sched.com/event/d651d8cb7388fb1427abdf9480aa1944
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DTSTAMP:20260708T114850Z
DTSTART:20260713T153000Z
DTEND:20260713T170000Z
SUMMARY:OCNS Board Meeting
DESCRIPTION:\n
CATEGORIES:REGISTRATION/ORGANIZATION
LOCATION:TBA\, Halifax\, NS\, Canada
SEQUENCE:0
UID:14b7125cc7e35f20d303ab88c965dcdc
URL:http://cns2026.sched.com/event/14b7125cc7e35f20d303ab88c965dcdc
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DTSTAMP:20260708T114850Z
DTSTART:20260713T170000Z
DTEND:20260713T173000Z
SUMMARY:FO4: Selective routing of spatial information in dentate granule cells emerges through disparate combinations of synaptic and intrinsic plasticity
DESCRIPTION:\nSanjna Kumari*1&nbsp\;and Rishikesh Narayanan1\n1&nbsp\;Cellular Neurophysiology Laboratory\, Molecular Biophysics Unit\, Indian Institute of Science\, Bengaluru 560012\, India\n*Email:&nbsp\;sanjnakumari@iisc.ac.in\n\nIntroduction\nGranule cells (GCs) in the dentate gyrus (DG) receive grid-like spatial inputs and contextual inputs from the entorhinal cortex\, both broadly tuned to multiple spatial locations. Despite this\, GCs elicit sparse spatial firing that is confined to single place fields\, thus playing a central role in selective routing of spatial information to the hippocampal circuit. The mechanisms behind the transformation of broadly tuned afferent inputs into sparse and location-specific outputs remains unclear.&nbsp\;In this study\, we ask if there are physiologically relevant plasticity mechanisms that can mediate selective routing of spatial information towards place-cell emergence and spatial remapping\, especially when inhibitory synapses are absent.\n\nMethods\nWe employed morphologically and biophysically realistic models of DG GCs (Kumari & Narayanan\, 2024)\, receiving grid-like and contextual spatial inputs from the entorhinal cortex. We employed a stochastic search paradigm in the plasticity space involving fold-changes in excitatory synaptic strengths\, persistent sodium (NaP)\, hyperpolarization-activated cyclic nucleotide-gated (HCN)\, and inward rectifier potassium (Kir) conductances. We validated plasticity combinations that achieved one of four functional targets relevant to DG spatial tuning: conversion of silent neurons to place cells\, uphold existing place field firing\, spatial remapping to a new location\, and suppression of spurious place fields to obtain a single place field (Fig 1).\n\nResults\nWhile excitatory synaptic plasticity alone was insufficient to generate valid spatial tuning\, conjunctive synaptic and intrinsic plasticity yielded several valid plasticity combinations for all 4 targets (Valid/Total models for 4 targets: 243/142\,000\, 325/10\,000\, 139/5\,000\, 224/50\,000). These valid plasticity combinations manifested pronounced heterogeneity across all fold-changes\, unveiling plasticity degeneracy where disparate plasticity combinations yielded similar spatial tuning outcomes. Dimensionality reduction analyses revealed low-dimensional structures in intrinsic measurement and parameter spaces of valid models. In contrast\, the plasticity space did not manifest strong constraints on plasticity across different components.\n\nDiscussion\nWhile inhibitory synaptic inputs have been studied as mechanisms for sculpting spatial tuning\, we show that selective routing of information and suppression of off-field firing can be achieved through intrinsic plasticity. Among intrinsic components\, we predict the axonal initial segment Kir&nbsp\;conductance as the strongest determinant of spatial selectivity. We demonstrate that disparate combinations of concomitant plasticity in excitatory synaptic and intrinsic conductances can mediate the emergence\, refinement\, and remapping of place fields. We show that co-dependent plasticity in different neuronal components can enable robust yet flexible spatial representations despite heterogeneities in neuronal composition and plasticity mechanisms.\n\nFIgure 1.&nbsp\;Medial and lateral entorhinal cortex inputs impinge on a DG granule cell. Disparate combinations of synaptic and intrinsic plasticity (NaP\, HCN\, Kir channels) achieved one of four targets: convert silent cell to place cell\, uphold existing place field\, remap\, or suppress spurious firing. Our results show that robust and flexible spatial tuning is achievable through plasticity degeneracy.​References\nKumari\, S.\, & Narayanan\, R. (2024). Ion-channel degeneracy and heterogeneities in the emergence of signature physiological characteristics of dentate gyrus granule cells. J Neurophysiol\, 132(3)\, 991-1013.&nbsp\;https://doi.org/10.1152/jn.00071.2024\n\n
CATEGORIES:ORAL SESSION FEATURED
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:23c6f57976d0808c601146ab092028dc
URL:http://cns2026.sched.com/event/23c6f57976d0808c601146ab092028dc
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DTSTAMP:20260708T114850Z
DTSTART:20260713T173000Z
DTEND:20260713T175000Z
SUMMARY:O13: A Developmental Ring Attractor Model for the Head Direction System
DESCRIPTION:Shujia Liu*1\, 2\, Bailu Si1\, Michael Herrmann21School of Systems Science\, Beijing Normal University\, Beijing\, China2&nbsp\;School of Informatics\, The University of Edinburgh\, Edinburgh\, UK*Email: liu.neuroscience@gmail.com\n\nIntroduction\nMost ring attractor models hard-code and phase-biased translation kernels to obtain a stable activity profile (bump) and velocity-driven shifts [1]. This bypasses a key developmental question: Can these stabilizing and translation kernels self-organize\, without any pre-set ring topology\, from activity statistics under staged multimodal constraints? The Lateral Mammillary Nucleus--Dorsal Tegmental Nucleus (LMN--DTN) loop implicated in head direction system also lacks developmental constraints. Motivated by synfire chain theory [2] we build a plasticity enabled LMN--DTN model and propose: Spontaneous traveling wave statistics plus staged vestibular/visual constraints can drive the emergence of a ring attractor and path integration.\n\nMethods\nWe constructed a rate-based LMN--DTN circuit model with 400 neurons in LMN and two populations of 400 direction--velocity conjunctive cells in DTN. LMN follows leaky integrator dynamics with plastic recurrent excitatory connectivity and a fixed long-range inhibitory kernel. DTN to LMN feedback consists of plastic phase-biased weights gated by the angular velocity input. Training proceeds in functional stages: We first obtain stable traveling wave statistics without external velocity or vision\, then update connectivity via STDP-like and structural plasticity\, and subsequently introduce long-range inhibition and a visual teacher for representational stabilization and gain\n\nResults\nWith random sparse connectivity\, no external velocity input\, and no hand-designed ring topology templates\, LMN networks spontaneously produce a stable unidirectional traveling wave under the joint action of dominant refractory-like neuronal dynamics and global inhibition\, exhibiting consistent phase progression (Fig. 1A). STDP-like and structural plasticity then consolidate the temporal correlations into locally enhanced recurrent excitation (Fig. 1B)\; long-range inhibition transforms the traveling wave regime into a phase-selectable single bump state. Visual relearning markedly improves short-term angle tracking\, yet cumulative drift persists during pure path integration after removing visual information (Fig. 1C).\n\nDiscussion\nOur results indicate that stabilizing and translation kernels of ring attractors need not be hard-coded: intrinsic recurrent dynamics can provide a directional temporal scaffold\, which activity-dependent plasticity\, staged inhibition\, and multimodal constraints shape into a stable bump representation and a learnable translation kernel. Although residual drift remains after visual removal\, it is structured rather than arbitrary\, suggesting that the model captures much of the required computation while revealing imperfections in the learned kernel. This makes the framework useful both as a proof of principle for de
CATEGORIES:ORAL SESSION TALK
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:262aacba5b410c143e15df9bedc78277
URL:http://cns2026.sched.com/event/262aacba5b410c143e15df9bedc78277
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DTSTAMP:20260708T114850Z
DTSTART:20260713T175000Z
DTEND:20260713T181000Z
SUMMARY:O14: How synchronization\, excitability\, and variability shape CPG rhythmic bursting sequences across different time scales
DESCRIPTION:Pablo Sanchez-Martin*1\, Alicia Garrido-Peña1\, Irene Elices1\, Carlos Garcia-Saura1\, Rafael Levi1\, Francisco B. Rodriguez1\, Pablo Varona1&nbsp\;\n\n1Grupo de Neurocomputación Biológica (GNB)\, Department of Computer Engineering\, Universidad Autónoma de Madrid\, Madrid\, Spain\n\n*Email: pablo.sanchezm@uam.es\n\nIntroduction\nRhythmic sequential activity is present in many nervous systems. Neural circuits that generate this activity usually involve intrinsic neuronal variability and different synapse types [1]. Sequential rhythms often require coordination at different time scales to adapt to specific conditions\, or to adjust speed and timing to meet functional needs. Previous studies in computational models have assessed how synchronization and excitability can modulate cycle-by-cycle sequential dynamical invariants [2\,3]. In this study\, we analyzed the interplay among neural synchronization\, excitability\, and variability to understand how they are related to the sequentiality timing in CPG rhythms.\n\nMethods\nWe acquired long recordings of pyloric CPG neurons of &nbsp\;Carcinus maenas&nbsp\;&nbsp\;and extracted the spike timings from intracellular and extracellular time series followed by calculation of all sequence intervals between the PD neurons and the LP. We used metrics of synchronization between the electrically coupled PDs (Victor-Purpura distance\, Euclidean distance)\, excitability for all three neurons (Spike Density Function -SDF-\, average ISIs)\, and interval variability. We identified dynamical invariants in the form of relationships between specific intervals and the instantaneous period. To find relationships between these metrics\, we performed analysis at three time scales: whole experiment\, segments inside experiments\, and cycle-by-cycle analysis.\n\nResults\nWe observed a high level of variability for synchronization\, excitability\, and the intervals in this system. Ranking each experiment for all metrics revealed a relationship between the variability in the period\, the neurons’ SDF\, and the strength of the dynamical invariant relationship. Segmenting the data\, we found that\, in addition to these relationships\, synchronization in the PD neurons is related to their excitability. We found non-linear relationships between the excitability of all neurons and their period variability and dynamical invariants. Excitability changes in any neuron were related to the other neurons' excitability at each cycle\, although other relationships present at larger time scales were not preserved cycle-by-cycle.\n\nDiscussion\nIt is still unclear how robustness and flexibility can be autonomously balanced in neural sequences. Previous works have found evidence that suggests that connectivity asymmetry\, i.e.\, the presence of both slow and fast synapses\, could be responsible for the emergence of coordination rules such as sequential dynamical invariants [2\, 3]. The LPPDdelay interval and instantaneous period are related cycle-by-cycle\, as well as the excitability of all neurons among them. In an intermediate scale\, the excitability is non-linearly related to synchronization\, variability and strength of the dynamical invariants. In a larger time scale\, excitability\, variability\, and the strength of dynamical invariants are all related\, but not synchronization.\n\nReferences\n[1] Selverston\, A. I.\, Rabinovich\, M. I.\, Abarbanel\, H. D.\, Elson\, R.\, Szücs\, A.\, Pinto\, R. D.\, ... & Varona\, P. (2000). Reliable circuits from irregular neurons: a dynamical approach to understanding central pattern generators. Journal of Physiology-Paris\, 94(5-6)\, 357-374.&nbsp\;\n[2] Berbel\, B.\, Latorre\, R.\, & Varona\, P. (2025). Theoretical bases for the relation between excitability\, variability and synchronization in sequential neural dynamics. Neurocomputing\, 645\, 130218.&nbsp\;\n[3] Elices\, I.\, Levi\, R.\, Arroyo\, D.\, Rodriguez\, F. B.\, & Varona\, P. (2019). Robust dynamical invariants in sequential neural activity. Scientific Reports\, 9(1)\, 9048.&nbsp\;\n\nAcknowledgments\nResearch funded by grants PID2024-155923NB-I00\, PID2023-149669NB-I00 and CPP2023-010818 (MCIN/AEI and ERDF- "A way of making Europe").&nbsp\;\n\n\n
CATEGORIES:ORAL SESSION TALK
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:b17e5b313ab22b2e8eeaaed5b8d4ca4d
URL:http://cns2026.sched.com/event/b17e5b313ab22b2e8eeaaed5b8d4ca4d
END:VEVENT
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DTSTAMP:20260708T114850Z
DTSTART:20260713T181000Z
DTEND:20260713T183000Z
SUMMARY:O15: Exact mathematical description of computation with transient spatiotemporal dynamics in recurrent neural networks
DESCRIPTION:Roberto Budzinski1\,2\,#\, Alexandra Busch2\,3\,4\, Luisa Liboni2\,5\, Ján Mináč2\,3\, Lyle Muller2\,3\,4\n1 Department of Neuroscience\, University of Lethbridge\, Lethbridge AB\, Canada\n2 Fields Lab for Network Computation\, Fields Lab\, Toronto ON\, Canada\n3 Department of Mathematics\, Western University\, London ON\, Canada\n4 Western Institute for Neuroscience\, Western University\, London ON\, Canada\n5 King's University College at Western University\, London ON\, Canada\n# roberto.budzinski@uleth.ca\n\nIntroduction\nNetworks throughout physics and biology use spatiotemporal dynamics for computation [1]. In neural systems\, waves of neural activity have recently been shown to shape spiking responses\, gate perception\, and influence behaviour [2]. However\, it remains unclear how network connectivity gives rise to neural dynamics and how these dynamics support computation. To address this question\, we introduce a new type of recurrent neural network that admits an exact mathematical solution [3\,4]\, enabling us to directly relate network structure to emergent dynamics and the computations those dynamics perform.\n\nMethods\nWe introduce a nonlinear recurrent neural network in which each unit is modeled as a complex-valued oscillator. This complex-valued recurrent neural network (cv-RNN) admits a closed-form solution given by an exact propagator. Importantly\, this framework introduces a unified matrix representation of the system that encodes the network's connectivity\, including connection strengths and delays\, and the input. The exact mathematical solution allows us to control the network dynamics\, down to the fine-scale pattern of connectivity\, allowing us to use the spatiotemporal patterns that emerge for dynamics-based computation in a wide range of tasks [3\,4].\n\nResults\nWe find the cv-RNN can perform a wide range of tasks\, including working memory\, logic operations\, sequence processing\, and computer vision\, while remaining precise and interpretable mathematically [3\,4]. The analytical framework reveals the mechanisms underlying each computation. By exploiting traveling-wave dynamics\, the network performs image segmentation and generalizes across different datasets using the same recurrent weights [4]. Further\, we create a bio-hybrid version of our cv-RNN\, where we leverage patch-clamping techniques to link biological neurons to the recurrent layer\, where these neurons can decode the network’s spatiotemporal dynamics and implement computations [3].\n\nDiscussion\nThese results demonstrate that structured spatiotemporal dynamics can serve as a powerful computational substrate in recurrent neural networks. The exact solution links connectivity\, input\, and emergent dynamics within a unified operator framework. This approach provides a principled way to understand how neural circuits may compute through traveling waves and network dynamics. More broadly\, it establishes a general framework for connecting network structure\, emergent dynamics\, and computation\, offering new tools for interpreting biological neural activity and for designing transparent dynamical models in artificial intelligence.\n\nReferences\n[1] Ermentrout et al. (2001)\, "Traveling electrical waves in cortex: insights from phase dynamics and speculation on a computational role”\, Neuron 29\, 33.\n[2] Muller et al. (2018)\, “Cortical travelling waves: mechanisms and computational principles”\, Nature Reviews Neuroscience 19.\n[3] Budzinski et al. (2024) “An exact mathematical description of computation with transient spatiotemporal dynamics in a complex-valued neural network\, Communications Physics 7.\n[4] Liboni et al. (2025)\, “Image segmentation with traveling waves in an exactly solvable recurrent neural network”\, Proceedings of the National Academy of Sciences 122.\nAcknowledgments\n​This work was supported by NSERC\, CFREF\, NIH\, Neuronex NSF\, and Canada Research Chairs Program.\n\n\n
CATEGORIES:ORAL SESSION TALK
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:311e7e767392b9dc71df3fa7ccdf939f
URL:http://cns2026.sched.com/event/311e7e767392b9dc71df3fa7ccdf939f
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DTSTAMP:20260708T114850Z
DTSTART:20260713T183000Z
DTEND:20260713T185000Z
SUMMARY:O16: The role of cell types in critical neural activity
DESCRIPTION:Adrián Ponce-Alvarez*1\,\,2\,3 and Germán Sumbre4\n1 Departament de Matemàtiques\, Universitat Politècnica de Catalunya\, 08028 Barcelona\, Spain.\n2 Institut de Matemàtiques de la UPC - Barcelona Tech (IMTech)\, Barcelona\, Spain.\n3&nbsp\;Centre de Recerca Matemàtica\, Barcelona\, Spain.\n4&nbsp\;Institut de Biologie de l’ENS (IBENS)\, Département de biologie\, École normale supérieure\, CNRS\, INSERM\, Université PSL\, 75005 Paris\, France\n\n\n*Email&nbsp\;:&nbsp\;adrian.ponce@upc.edu\n\nIntroduction\nNeuronal activity shows statistics consistent with a critical point\, a regime that maximize information capacity. Yet\, the role of different cell types remains largely unexplored. Models [1] and in vitro studies [2] suggest that excitation–inhibition (E/I) balance is key for self-organized criticality\, but how E and I dynamics interact during in vivo critical activity is unclear. Similarly\, glial cells such as radial astrocytes (RAs) regulate neuronal function [3]\, but their role in criticality is unknown. Here\, we studied how E/I neuronal activity and astrocyte calcium dynamics contribute to criticality by combining transgenic zebrafish with cell-type-specific calcium indicators\, a stochastic network\, and model inference.\n\nMethods\nSpontaneous neuronal activity in the optic tectum (OT) of 10 zebrafish larvae was recorded using light-sheet microscopy. A double-transgenic line expressing GCaMP6f in all neurons and Vglut in glutamatergic neurons identified of E and I cells. Two-photon calcium imaging was performed in 7 larvae expressing GCaMP6f in neurons and RCaMP1b in RAs [3]. OT activity was recorded during spontaneous activity and after mild electrical stimulation\, which triggered synchronized Ca²⁺ transients in RAs.\nE and I activity was modelled using a stochastic network displaying critical avalanches at a E/I phase transition [1]. The maximum entropy principle mapped neuronal activity onto statistical models [4]\, quantifying criticality and detecting deviations.\n\nResults\nOur results show that neuronal avalanches approached criticality when E and I activity were balanced. Notably\, the model accurately captured the observed avalanche statistics and their sensitivity to E/I fluctuations around a critical point defined by balanced excitatory and inhibitory synaptic strengths\, where balanced amplification drives network avalanches. Furthermore\, we found that RA synchronization shifted tectal neuronal activity away from its spontaneous critical state toward a more ordered regime\, with a reduced repertoire of network states and diminished susceptibility to external inputs. These findings demonstrate that glial activity can actively regulate the state of neuronal ensembles\, including their proximity to criticality.\n\nDiscussion\nExtensive research highlights the benefits of E/I balance and critical dynamics. Balanced networks enhance amplification\, selectivity\, and stability\, while critical dynamics optimize information processing. Here\, we show that neuronal avalanche statistics and their dependence on spontaneous E/I fluctuations in the zebrafish OT match a model reaching criticality at balanced E and I couplings. Moreover\, RA synchronization in the OT reshapes collective neuronal activity\, consistent with a shift from spontaneous critical dynamics to a more ordered subcritical regime. Our findings show that radial astrocyte activity can shift the state of neuronal ensembles and modulate their proximity to criticality.\n\nReferences\n1. &nbsp\; &nbsp\; Benayoun\, M.\, et al. (2010). Avalanches in a Stochastic Model of Spiking Neurons. PLoS Comput Biol\, 6(7)\, e1000846.&nbsp\;https://doi.org/10.1371/journal.pcbi.1000846\n2. &nbsp\; &nbsp\; Shew\, W.L.\, et al. (2011). Information capacity and transmission are maximized in balanced cortical networks with neuronal avalanches. J Neurosci.\, 31(1)\, 55-63.&nbsp\;https://doi.org/10.1523/JNEUROSCI.4637-10.2011\n3. &nbsp\; &nbsp\; Uribe-Arias\, A.\, et al. (2023). Radial astrocyte synchronization modulates the visual system during behavioral-state transitions. Neuron 111\, (24)\, 4040-4057.e6.&nbsp\;https://doi.org/10.1016/j.neuron.2023.09.022\n4. &nbsp\; &nbsp\; Tkačik\, G.\, et al. (2014). Searching for Collective Behavior in a Large Network of Sensory Neurons. PLoS Comput Biol\, 10(1)\, e1003408.&nbsp\;https://doi.org/10.1371/journal.pcbi.1003408\n\nAcknowledgments\nThis study was supported by the Project PID2022-137708NB-I00 funded by MICIU/AEI /10.13039/501100011033 and FEDER\, UE. A. Ponce-Alvarez was supported by a Ramón y Cajal fellowship (RYC2020-029117-I) funded by MICIU/AEI/10.13039/501100011033 and “ESF Investing in your future”. G. Sumbre was supported by ERC CoG 726280.\n\n\n
CATEGORIES:ORAL SESSION TALK
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:149d8787697c249a718b1d5aa2fd0104
URL:http://cns2026.sched.com/event/149d8787697c249a718b1d5aa2fd0104
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DTSTAMP:20260708T114850Z
DTSTART:20260713T185000Z
DTEND:20260713T192000Z
SUMMARY:Coffee break
DESCRIPTION:\n
CATEGORIES:BREAK
LOCATION:Ballroom Salon\, Halifax\, NS\, Canada
SEQUENCE:0
UID:a8e00a76d69e99355d9b79fa0414966e
URL:http://cns2026.sched.com/event/a8e00a76d69e99355d9b79fa0414966e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260713T192000Z
DTEND:20260713T212000Z
SUMMARY:Poster Session 2
DESCRIPTION:\n
CATEGORIES:POSTER SESSION
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:5d8756eb4e5a8b1d66c4a35044e2b244
URL:http://cns2026.sched.com/event/5d8756eb4e5a8b1d66c4a35044e2b244
END:VEVENT
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DTSTAMP:20260708T114850Z
DTSTART:20260713T192000Z
DTEND:20260713T212000Z
SUMMARY:P049: Extracellular dipole and quadrupole fields from axonal branching patterns
DESCRIPTION:Introduction\nWhile the fields of extracellular neural recordings are well understood and mostly dominated by the somatic spikes and dendritic activity [1\,2]\, there are some unnecessarily neglected sources. One of these is axonal branching patterns\, that can under correct circumstances make a large contribution extracellularly to both near and far fields. These circumstances include\, e.g.\, a synchronous volley of spikes in a branching axonal bundle\, as often observed in the auditory brainstem [3]. I address the question under which circumstances the fields from axonal branching patterns can be non-negligible\, and whether their fields are fully explained by their dipole contribution.\n\nMethods\nI simulate single multi-compartment cells with NEURON and LFPy packages to study their extracellular potentials at distances relevant for EEGs\, often referred as far fields. I furthermore analytically study the extracellular fields of axonal branching patterns\, singling out their relative dipole and quadrupole contributions to the extracellular field both along and perpendicular to the dipole axis.\n\nResults\nAs expected\, the simulations show that the dipole between apical dendrites and the soma can determined the extracellular far fields in pyramidal-like cell morphologies. Additionally\, both the simulations and the analytics show that axonal branching patterns can create similarly extracellular far fields that are similarly large in amplitude. Furthermore\, these axonal fields cannot be explained by the dipole contribution alone.\n\n\nDiscussion\nAs conventionally assumed\, the dipole spanned between the dendrites and soma is the main source of the electro-encephalography (EEG) signals of cortical pyramidal neurons [e.g. 4]. This assumption does not necessarily hold for neurons with a large axonal branching zone\, particularly when embedded in a population of neurons with similar morphologies and with synchronous population activation. These results have consequences e.g. for the interpretation of evoked somatosensory potentials\, such as the auditory brainstem response.\n\nReferences\n Gold\, C.\, et al. (2006). On the origin of the extracellular action potential waveform: A modeling study. 95(5)\, 3113–3128. https://doi.org/10.1152/jn.00979.2005 Næss\, S.\, et al. (2021). Biophysically detailed forward modeling of the neural origin of EEG and MEG signals. NeuroImage\, 225\, 117467. https://doi.org/10.1016/j.neuroimage.2020.117467 McColgan\, T.\, et al. (2017). Dipolar extracellular potentials generated by axonal projections. eLife\, 6\, 343. https://doi.org/10.7554/eLife.26106 Neymotin\, S. A.\, et al. (2020). Human Neocortical Neurosolver (HNN)\, a new software tool for interpreting the cellular and network origin of human MEG/EEG data. eLife\, 9\, e51214. https://doi.org/10.7554/eLife.51214 \n\n\nAcknowledgement\nI thank Catherine Carr\, Christine Köppl\, Richard Kempter and Ghadi El Hasbani for helpful discussions\, and Hannah Schultheiss for preliminary modeling.\nThis research was funded by the Deutsche Forschungsgemeinschaft (DFG\, German Research Foundation) grant nr. 502188599.\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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SUMMARY:P050: Fano-like information filtering profiles in coupled neuronal models.
DESCRIPTION:Introduction\nSubthreshold dynamics play a key role in spike generation\, and it is well-known that some neurons exhibit a frequency preference when integrating subthreshold input– so-called resonators [1\,2]. It has been shown\, however\, that despite the existence of subthreshold resonance\, a single resonator neuron exhibits low-pass\, i.e.\, monotonic\, information filtering (as measured by the spectral coherence). In other words\, in the subthreshold regime\, band-pass impedance does not translate to band-pass information filtering. Instead\, nonlinearities\, such as spiking dynamics\, are needed to create band-pass information transfer [3\,4].\n\n\nMethods\nHere\, we study a similar question in electrically and synaptically coupled pairs of neurons. Our goal is to evaluate whether this resonance profile imparts non-trivial information filtering capabilities to the coupled systems. We numerically simulate an integrate-and-fire coupled to a resonate-and-fire system in both the subthreshold and suprathreshold regime\, and we investigate the stimulus-response spectral coherence function of the system under perturbation by coloured noise (Ornstein-Uhlenbeck process)\, as well as the lower bound of mutual information rate.\n\n\nResults\nWith an electrical coupling between a resonator and an integrator\, we show that a Fano-like resonance profile appears in the impedance\, i.e.\, a narrow\, asymmetric peak with anti-resonance [5]. Moreover\, we observe that the coherence function is non-monotonic\, with a minimum around the frequency of the opposite neuron. We also find that with a synaptic-like coupling\, a similar Fano-like peak appears in the coherence function\, and the lower bound of mutual information rate is generally higher.\n\n\nDiscussion\nThis challenges the claim that neurons require nonlinearities to relay bandpass information filtering properties. This also gives rise to a new type of coherence function and superior information transmission rate overall. This new perspective places information filtering in the context of connection motifs where a small number of resonators and integrators interact\, rather than the context of individual neurons.\n\n\nReferences\n[1] Izhikevich\, Eugene M.&nbsp\;Dynamical systems in neuroscience. MIT press\, 2007.\n[2] Izhikevich\, Eugene M. "Resonate-and-fire neurons."&nbsp\;Neural networks&nbsp\;14.6-7 (2001): 883-894.\n[3] Lindner\, Benjamin. "Mechanisms of information filtering in neural systems."&nbsp\;IEEE Transactions on Molecular\, Biological and Multi-Scale Communications&nbsp\;2.1 (2016): 5-15.\n[4] Blankenburg\, Sven\, et al. "Information filtering in resonant neurons."&nbsp\;Journal of computational neuroscience&nbsp\;39 (2015): 349-370.\n[5] Joe\, Yong S.\, Arkady M. Satanin\, and Chang Sub Kim. "Classical analogy of Fano resonances."&nbsp\;Physica Scripta&nbsp\;74.2 (2006): 259.\n\nAcknowledgement\nI would like thank Prof. Serge Gauvin for the initial inspiration.&nbsp\;\n\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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URL:http://cns2026.sched.com/event/62a24f1ec70aaad0d0acc91706a5d255
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SUMMARY:P051: Stimulation Induced Effects on the Collective Dynamics of a Recurrently-Connected Excitatory-Inhibitory Network
DESCRIPTION:Introduction\n\n\nElectrical stimulation has been used as a treatment of a variety of neurological disorders\, including Parkinson’s Disease (PD) [1]. Despite its efficacy\, its mechanism of action on the modulation of network-level dynamics to alleviate symptoms is not fully understood. Previous computational work has addressed stimulation effects at the cellular and synaptic level\, but the underlying collective dynamics and their functional roles remain relatively unexplored [2]. This requires clinicians to rely on manual programming to determine the therapeutic effect [3]. A mechanistic understanding of stimulation effects on the neuronal circuitry is necessary for the development of closed-loop stimulation techniques that could improve patient outcomes.\n\nMethods\nUsing a sparse\, recurrently connected excitatory-inhibitory (E-I) network of leaky integrate-and-fire neurons\, we extend the Brunel architecture [4] with short-term synaptic plasticity (STP) [5] and characterise its effects on the original network and changes in the canonical activity states (Fig 1A).\nUsing an implementation of deep brain stimulation (DBS) that aligns with experimental observations like axonal depolarisation [6]\, somatic suppression [7]\, and efferent activation [8] (Fig 1B)\, we examine the effects of stimulation across the parameter space of E-I balance and external drive. We capture the effect of stimulation on individual neurons and the population-level activity through metrics such as desynchronisation and regularisation.\n\nResults\nA known biomarker for PD is the presence of abnormally strong oscillations in the beta band (13-30 Hz) [9]. To model the effects of stimulation on PD treatment\, we measured the reduction of the beta-band oscillatory activity\, correlated with alleviation of PD-associated motor symptoms [10]. High-frequency stimulation has a strong effect on suppressing strong beta-oscillations in networks receiving low external drive and having a high level of inhibition (Fig 1C\, D). Using measures of criticality\, we show that the presence of strong beta-oscillations is linked to the network going through a phase transition. Introduction of electrical stimulation prevents this transition from occurring\, thereby preventing the pathological oscillatory state.&nbsp\;\n\nDiscussion\nOur findings show that metrics of criticality can be an effective biomarker for therapeutic efficacy\, indicating a transition away from a pathological state. Using this in addition to the power spectral density can allow better control of clinical protocols. We provide a framework for evaluating the effect of stimulation on the collective dynamics of the network across connectivity regimes and activity states to predict behaviour in biologically realistic circuits. We hope to extend this work to computational models of the basal ganglia and hippocampus: two well-utilised sites of high-frequency stimulation [11\, 12]\, to investigate the effect of electrical stimulation on their activity and the mechanisms of action of clinical therapies.\n\nFigure 1.&nbsp\;A: Schematic of the network architecture. A subset of the excitatory population is the target of stimulation. B: Schematic of the stimulation model. C: The firing rate\, regularity\, synchrony\, and beta-band power across the parameter space for baseline (left) and 130 Hz stimulation (right). D: The effect of stimulation on metrics of therapeutic efficacy​\n\nReferences\nhttps://doi.org/10.1016/b978-0-444-53497-2.00010-3&nbsp\;https://doi.org/10.1038/s41582-018-0128-2.https://doi.org/10.1001/archneur.63.9.1266&nbsp\;https://doi.org/10.1023/A:1008925309027https://doi.org/10.1162/089976698300017502https://doi.org/10.1016/j.expneurol.2008.11.024&nbsp\;https://doi.org/10.1093/brain/awh616&nbsp\;https://doi.org/10.1523/jneurosci.23-05-01916.2003&nbsp\;https://doi.org/10.1152/jn.00697.2006https://doi.org/10.1002/mds.22419https://doi.org/10.1016/j.baga.2011.05.001&nbsp\;https://doi.org/10.1038/nature15694\nAcknowledgement\nI wish to acknowledge everyone in the Neural Systems & Brain Signals Processing Lab and Krembil Computational Neuroscience for their help and support\, especially David Crompton\, Xiangyu Ma\, and Zoe Paraskevopoulos. I also want to acknowledge CIHR and NSERC for their funding for my doctoral research.
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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URL:http://cns2026.sched.com/event/711cea1c563607d0a1727fb9ac40cca4
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SUMMARY:P052: Recurrent Inhibition Drives Frequency-Selective Deep Brain Stimulation Efficacy via Bistability and Hopf Bifurcation in a Continuous Attractor Network
DESCRIPTION:Introduction\nDBS alleviates Parkinsonian symptoms at high frequencies (&gt\;90 Hz) yet worsens them at low frequencies (&lt\;60 Hz) across STN\, GPi\, VIM\, and SNr\; no mechanism explains why frequency alone reverses outcome [1\,2]. Pathological beta-band (13–30 Hz) synchronisation in the basal ganglia–thalamocortical loop is the hallmark of Parkinson's disease (PD)\; its suppression is the leading hypothesis for DBS efficacy [2\,3]. High-frequency DBS depresses glutamatergic over GABAergic terminals\, shifting E/I balance toward inhibition — an asymmetry attenuated at low frequencies [4\,5]. We present a bi-population CANN [6\,7] that unifies bistability\, beta oscillations\, spectral criticality\, and spatial responses in a single tractable framework.\n\n\nMethods\nE and I populations sit on a periodic ring with exponential connectivity and ReLU transfer functions [6\,7]. DBS is a periodic pulse train\; glutamatergic drive scales as F(f) = max(1 + βf\, 0)\, β &lt\; 0\, the linearised Tsodyks–Markram depression [8]. GABAergic terminals receive a fixed fraction η without attenuation\, encoding differential terminal depression [4]. Under uniform stimulation the network reduces to coupled ODEs with Jacobian eigenvalues λ₁\,₂ = α ± iω₀ around the active fixed point. Spatial profiles use Fourier decomposition and a Green's function with exact ring boundary conditions. Stochastic fluctuations enter via the linear noise approximation.\n\nResults\nAttenuating excitatory drive produces a boundary equilibrium bifurcation at f_th ≈ 90–140 Hz\, matching the clinical therapeutic window [1]: below f_th\, bistability and hysteresis coexist\; above it\, excitatory firing is suppressed while inhibitory output grows linearly with frequency\, explaining the paradoxical GABAergic increase [4\,5]. Beta oscillations emerge when Δ &lt\; 0 and α &gt\; 0\; the Hopf boundary depends only on intrinsic parameters and is invariant to DBS frequency. The spectral criticality index C = ω₀/(2|α|) diverges as α → 0⁻\, providing a real-time LFP biomarker for pathological synchrony. Mean-field theory agrees with simulation (N = 100 per population) quantitatively across 1–200 Hz (Fig. 1).\n\nDiscussion\nDBS therapy and PD pathology act through distinct bifurcations — fully decoupling therapeutic from pathological mechanisms — explaining why DBS is effective without resolving the underlying circuit vulnerability. Testable predictions include: frequency-ramp hysteresis\; slope discontinuity in the inhibitory rate–frequency curve at f_th\; pre-symptomatic spectral narrowing\; nucleus-specific spatial footprints scaling with axonal reach\; and stronger pre-operative beta power predicting faster therapeutic onset.\n\nFigure 1.&nbsp\;Mean-field theory vs. simulation (N=100 per population). (a)-(c) Excitatory rate R(t) over 0-3 s at f=5\, 49\, 153 Hz (blue)\; grey dashed: mean-field prediction\; rate fluctuates around mean. (e)-(g) Inhibitory rate R'(t) (red)\, same convention. (d) Mean R and (h) mean R' at steady state vs. DBS frequency (log scale)\; circles: simulation\; grey line: theory.​\n\nReferences\n[1] McIntyre\, C. C.\, et al. (2004). Clinical Neurophysiology\, 115(6)\, 1239–1248.\n[2] Neumann\, W.-J.\, et al. (2023). Brain\, 146(11)\, 4456–4468. \n[3] Brittain\, J.-S.\, & Brown\, P. (2014). NeuroImage\, 85\, 637–647. \n[4] Li\, J.\, et al. (2025). Nature Neuroscience\, 28\, 341–355. \n[5] Xu\, H.\, et al. (2025). Nature Communications\, 16\, 245. \n[6] Wilson\, H. R.\, & Cowan\, J. D. (1972). Biophysical Journal\, 12(1)\, 1–24. \n[7] Amari\, S. (1977). Biological Cybernetics\, 27(2)\, 77–87. \n[8] Tsodyks\, M. V.\, & Markram\, H. (1997). PNAS\, 94(2)\, 719–723.\n\nAcknowledgement\nSupported by the Krembil Brain Institute and the Department of Physiology\, University of Toronto. Authors thank colleagues at the Krembil Computational Neuroscience group for discussions.
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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SUMMARY:P053: Decoding decisions from neuronal activity in different animals with canonical correlation analysis
DESCRIPTION:Introduction\nRecent studies have investigated the geometric similarity of task structure by developing novel approaches to decode of brain states across animals [1\,2\,3]. The extent to which neuronal representations are similar within an animal at disparate times or between different animals performing the same task is not well understood. Quantifying the representational similarity of brain states will be critical for understanding disorders that involve impairment of neuronal dynamics. Here\, we employed canonical correlation analysis (CCA) to quantify similar network states across time and between animals in recordings of rodent anterior cingulate cortex (ACC) during a decision-making task in which an animal must select one of two choices.\n\n\nMethods\nCCA identifies the strongest overlapping patterns between two different datasets by providing correlation coefficients (CCs) ordered by magnitude. These optimal CCs are identical to the singular values of the cross-covariance matrix calculated after orthogonalizing the data via QR decomposition (described in [2]).\nWe processed multi-unit neuronal recording into a set of fixed-length firing rate time series\; each was synchronized to the time that the animal indicated its choice within a trial. CCA was computed for each pair of trials across all recording sessions to obtain CCs from each comparison. \n\n\n\nResults\nWithin- and between- session trial-to-trial recurrence matrices were constructed using the 1st (i.e. the maximal) CC from each CCA comparison. \nUsing the within-session recurrence matrix for each session\, we clustered trials (KMeans\; k=2 clusters) and used cluster labels to decode the animal’s choice. Decoding performance for each session was quantified with Dice distance and evaluated via permutation tests against surrogate data from shuffled cluster labels. In 36/52 sessions\, this metric indicated choice decoding was better than chance. \nUsing the within and between-session recurrence matrices\, we clustered all trials from all sessions (KMeans\; k=2)\, and choice decoding was better than chance in 30/52 sessions. \n\nDiscussion\nWe used CCA for pairwise trial comparison to align neural data within and between sessions\, using these alignments to build trial-to-trial recurrence matrices that reveal representational similarities in neuronal activity. Notably\, the successful decoding of choice from these neural metrics demonstrates that rodent ACC network states exhibit a common\, low-dimensional structure across different animals.\n\n\nReferences\n1.&nbsp\;Melbaum\, S.\, Russo\, E.\, Eriksson\, D.\, Schneider\, A.\, Durstewitz\, D.\, Brox\, T.\, & Diester\, I. (2022). Conserved structures of neural activity in sensorimotor cortex of freely moving rats allow cross-subject decoding. Nature communications\, 13(1)\, 7420. \n2.&nbsp\;Gallego\, J.\, Perich\, M.\, Chowdhury\, R.\, Solla\, S.\, & Miller\, L. (2020). Long-term stability of cortical population dynamics underlying consistent behavior. Nature neuroscience\, 23(2)\, 260–270. \n3.&nbsp\;Safaie\, M.\, Chang\, J.\, Park\, J.\, Miller\, L.\, Dudman\, J.\, Perich\, M. & Gallego\, J. (2023). Preserved neural dynamics across animals performing similar behaviour. Nature\, 623(7988)\, 765–771.\n\nAcknowledgement\nThis work was supported by grants to CCL from NIH (AA029970\, AA029409).\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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URL:http://cns2026.sched.com/event/c1fa73840a6dba913919758dcc6291a3
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SUMMARY:P054: Single-cell adaptation makes heterogeneity a dynamic feature of neural networks
DESCRIPTION:Introduction\n\n\nThroughout biology\, diversity plays an important role in maintaining robustness and stability [1]. The same is true of the brain [2]\, where recent datasets [3\,4] have shown widespread heterogeneity\, marking it as an unavoidable component of neuronal composition. While heterogeneity has been linked to stability and increased computational potential [2]\, recent experiments have shown its loss accompanies pathological states [5]\, suggesting an important functional role. Despite this\, how changes in heterogeneity arise remains unknown. Oftentimes considered to be a static metaparameter resulting from solely genetic disposition\, heterogeneity is\, in fact\, a highly dynamic property of biological networks [3] arising from various sources.\n\nMethods\n\n\nWe endowed a simple network model of excitatory neurons with a candidate mechanism for homeostatic adaptation of neural excitability [6]. Through combined analytical and numerical approaches\, we measured the effect of input statistics on the excitability of individual cells and how this translated into changes in network heterogeneity at the population scale.\n\nResults\n\n\nOur results indicated that\, through adaptation\, diversity in synaptic inputs promotes heterogeneity in cell-to-cell excitability due to changes in the statistics of presynaptic firing rates and network topology. In contrast\, whenever the statistics of synaptic inputs between cells were too similar\, the same adaptation mechanism promoted the decline in heterogeneity. Further\, we demonstrate that these changes in heterogeneity can coexist with degeneracy in firing rates between neurons\, reminiscent of what is observed in cortical neurons [3].\n\nDiscussion\n\n\nWe have demonstrated that a degenerate adaptation rule is a viable mechanism for dynamically regulating heterogeneity in an excitatory network. Specifically\, we showed that this adaptation can sustain\, increase\, or decrease diversity. Such “dynamic diversity” is dependent on the input statistics to each neuron\, which are manipulated by external stimuli\, and the amount of cell-to-cell diversity in the network itself. These results thus form the framework for future investigation into how the statistics that arise in more complex networks may influence the heterogeneity and hence functional capacity&nbsp\;and resilience of neuronal networks.\n\nReferences\n\n\n[1] Landi\, P\, et al (2018). Complexity and stability of ecological networks: a review of the theory.&nbsp\;Popul. Ecol.\,&nbsp\;60(4). \n[2] Hutt\, A\, et al (2023). Intrinsic neural diversity quenches the dynamic volatility of neural networks.&nbsp\;PNAS\,&nbsp\;120(28). \n[3] Lee\, B R\, et al (2023). Signature morphoelectric properties of diverse GABAergic interneurons in the human neocortex.&nbsp\;Science\,&nbsp\;382(6667). \n[4] Braun\, E\, et al (2023). Comprehensive cell atlas of the first-trimester developing human brain.&nbsp\;Science\,&nbsp\;382(6667). \n[5] Rich\, S\, et al (2022). Loss of neuronal heterogeneity in epileptogenic human tissue impairs network resilience to sudden changes in synchrony.&nbsp\;Cell Rep.\,&nbsp\;39(8).\n[6] Trotter\, D\, et al (2026). Intrinsic Plasticity Underlies the Malleability of Neural Network Heterogeneity.&nbsp\;PRX Life\,&nbsp\;4(1).\n\nAcknowledgement\nThe authors thank Andre Longtin for helpful discussions.&nbsp\;\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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URL:http://cns2026.sched.com/event/2a3f0077a97b774027cd68061b5bd2f1
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SUMMARY:P055: Thalamocortical myelination controls cortical states
DESCRIPTION:Introduction\nMyelin maintains the precise timing and coordination of neural signalling by regulating action potential conduction. Maladaptive myelination disrupts this process and underlies many neurological disorders [1]. We recently showed that cortical demyelination induced by cuprizone\, a central nervous system demyelinating drug\, shifts cortical excitability and synchrony\, leading to motor deficits [2]. However\, cuprizone impacts myelinated fibres across the brain\, including thalamocortical projections of the internal capsule\, an essential yet understudied motor pathway. We propose that demyelination of these pathways impedes thalamic control of cortical states [3] and may contribute more to motor impairments than intracortical dynamics.\n\nMethods\nWe built a sparsely connected spiking neural network comprising four populations: cortical excitatory and inhibitory neurons\, the ventral lateral thalamic nucleus\, and the thalamic reticular nucleus. Parameters were fitted to Neuropixels data to generate biophysically realistic yet computationally tractable neuronal responses. Demyelination was simulated by either decreasing axonal conduction velocity or increasing spike propagation failure rate\, each scaled to the putative severity of cuprizone-induced damage. Key metrics such as firing rate\, firing patterns\, and spike correlations were analyzed under both conditions and compared to assess how thalamocortical demyelination alters network excitability and synchrony.\n\n\nResults\nSimulations reveal that decreasing conduction velocity or increasing propagation failure rate significantly impacts thalamocortical network dynamics. That is\, demyelination impairs the ability of the thalamus to control cortical dynamics and generates network-wide hypoexcitability and decorrelates spiking activity. These results support our hypothesis that demyelination of thalamocortical pathways contributes to network dysfunction.\n\n\nDiscussion\nOur preliminary results suggest that thalamocortical demyelination alters action potential conduction\, triggering shifts in network excitability and synchrony that may underlie the motor deficits observed in demyelinating disorders. Investigating demyelination also provides an effective framework for exploring the fundamental role of myelin in neural circuits. Future work will assess how these changes in neural dynamics contribute to motor control impairments\, which may provide insight on conditions such multiple sclerosis. These findings highlight the importance of axonal pathways that extend beyond the cortex in understanding how demyelination disrupts neural communication.\n\n\nReferences\n1. Knowles\, J. K.\, Batra\, A.\, Xu\, H.\, & Monje\, M. (2022). Adaptive and maladaptive myelination in health and disease. Nature Reviews Neurology\, 18\, 735–746. https://doi.org/10.1038/s41582-022-00737-3\n2. Gagnon\, K.\, Flora Nunes\, G. D.\, Nettles\, D.\, Nguyen\, T.\, Carter\, E. R.\, Lins\, A.\, Williamson\, R.\, Lefebvre\, J.\, Denman\, D.\, Hughes\, E. G.\, & Welle\, C. G. (2025). Myelin supports cortical circuit function underlying skilled movement. bioRxiv. https://doi.org/10.64898/2025.12.23.696289\n3. Poulet\, J. F.\, Fernandez\, L. M.\, Crochet\, S.\, & Petersen\, C. C. (2012). Thalamic control of cortical states. Nature Neuroscience\, 15\, 370–372. https://doi.org/10.1038/nn.3035\n\n\n\nAcknowledgement\nWe would like to thank the National Research Council of Canada (NSERC GRANT RGPIN-2017-06662)\, the Canadian Institute of Health Research (CIHR GRANT NO PJT-156164) and National Institutes of Health (NIH GRANT NS115975) for funding.\n\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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URL:http://cns2026.sched.com/event/898d999323486e67a1859ba406172222
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SUMMARY:P056: Hippocampus-Inspired Artificial Neural Network Enables Robust Classification under Sparsity: Structured versus Brute-Force Robustness
DESCRIPTION:Introduction\nWe investigate robustness to structural sparsity in a hippocampus-inspired artificial neural network (OurANN) for image classification. OurANN is composed of functional modules—dentate gyrus (DG)\, CA3\, and CA1—rather than conventional hidden layers (see Figure 1). DG enforces sparse competitive representations\, CA3 provides recurrent stabilization\, CA1 integrates stabilized activity for readout\, and shortcut connections (EC→CA3 and EC→CA1) preserve signal flow under sparse connectivity.\n\nMethods\nAs a baseline\, we use a conventional multilayer perceptron (CANN) with three feedforward hidden layers whose numbers of units match those of DG\, CA3\, and CA1. OurANN classifier is trained using standard backpropagation (Ref. [1])\, ensuring direct comparability with the CANN baseline. Using the MNIST dataset as a controlled benchmark\, we sweep the inter-layer connection probability pc from 1.0 down to 0.01 and evaluate robustness using global degradation rates and robustness indices\, together with local performance metrics.\n\n\nResults\nIn the dense and moderate regimes (pc=1.0 ~ 0.1)\, OurANN and CANN exhibit nearly identical performance\, indicating no intrinsic advantage under weak sparsity. Differences begin to emerge in the sparse regime (pc = 0.1 ~ 0.05)\, where OurANN shows slower performance degradation\, in contrast to the CANN. In the extremely sparse regime (pc = 0.05 ~ 0.01)\, OurANN exhibits clear and persistent robustness\, while CANN performance rapidly collapses.\n\n\nDiscussion\nAlthough CANN robustness can be partially recovered through brute-force scaling of layer size\, achieving robustness comparable to OurANN requires substantially increased parameter redundancy. These results distinguish structural robustness in OurANN\, arising from hippocampus-inspired architectural organization\, from brute-force scaling robustness in CANN\, achieved through parameter expansion.\n\nFigure 1.&nbsp\;Hippocampus-inspired artificial neural network (ANN). Feedforward: EC (entorhinal cortex) → DG (dentate gyrus) → CA3 and Shortcuts (SCs): EC → CA3 and EC → CA1 and inhibitory backprojection: CA3 → DG. S: subiculum.​References\n[1] Rumelhart\, D. E.\, Hinton\, G. E.\, & Williams\, R. J (1986) Learning representations by back-propagating errors.&nbsp\;Nature\, 323\, 533-536.\n\n\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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URL:http://cns2026.sched.com/event/4828c91c224c14bf90c862efcaa0e4b6
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SUMMARY:P057: Input-side Competition Predicts Action Selection and Switching in The Basal Ganglia
DESCRIPTION:Introduction\nAction selection in the basal ganglia (BG) is often inferred from the mean firing rate (MFR) of the output nucleus\, substantia nigra pars reticulata (SNr). Because SNr MFR is ultimately driven by its synaptic inputs\, we introduce the competition degree Cd\, an input-side indicator that directly quantifies how Direct Pathway (DP) and Indirect Pathway (IP) inputs determine selection among competing channels in a spiking neural network (SNN) (see Figure 1).\n\nMethods\nFor each channel\, Cd = SDP/SIP\, where SDP and SIP denote the presynaptic current strengths via DP and IP arriving at SNr\, respectively (Ref. [1]). The selected action corresponds to the channel with the largest Cd\, providing a cause-and-effect mapping from input-side competition to output-side selection\, without relying on SNr MFR readout.\n\nResults\nTo our knowledge\, this is the first quantitative formulation of action selection based on input-side DP/IP competition in a multi-channel BG SNN. Our results further show that shifts in Cd predict channel switching when cortical inputs change\, indicating that input-side DP/IP competition mechanistically determines selection and switching in BG.\n\nDiscussion\nThese findings identify the competition degree Cd as a quantitative substrate for linking DP/IP competition to selection and switching in BG. Thus\, Cd offers a mechanistic predictor of channel dominance: the selection outcome is not merely read out at SNr\, but is determined presynaptically by DP/IP competition upstream of SNr output.\n\nFigure 1.&nbsp\;(a) Single-channel BG circuit. Green and red lines represent direct pathway (DP) and indirect pathway (IP) to the output nucleus\, SNr\, respectively. (b) Three-channel BG SNN. Channels 1-3 represent actions and are shown in orange\, purple\, and gray\, respectively.​References\n[1] Kim\,&nbsp\;S.-Y.\, & Lim\, W. (2024) Quantifying harmony between direct and indirect pathways in the basal ganglia\; healthy and Parkinsonian states. Cognitive Neurodynamics 18\, 2809-2829.\n\n\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:82da7df383b1c7aa6bb03271df142faf
URL:http://cns2026.sched.com/event/82da7df383b1c7aa6bb03271df142faf
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DTSTART:20260713T192000Z
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SUMMARY:P058: Direct Quantification of Stability for Linear Dynamical Systems on Networks
DESCRIPTION:Introduction\nStability\, the ability of a system to return to a steady state after perturbation\, is a fundamental property of dynamics on networks\, critical in brain networks with regards to epilepsy for example\, and in other contexts ranging from ecological networks to power grids. Despite its importance\, existing approaches to stability assessment\, such as linear stability analysis based on the dominant eigenvalue of the Jacobian matrix\, offer only heuristic insights since they ignore the remainder of the eigenspectrum and do not directly quantify deviation from stability. Moreover\, current approaches do not give a full interpretation of how individual nodes or motifs of the network structure contribute to the stability (or lack thereof) of the system.\n\n\nMethods\nHere\, we introduce a novel technique for directly quantifying the expected deviation from stability of the network dynamics&nbsp\;x(t) as a function of the directed network structure&nbsp\;C&nbsp\;(with&nbsp\;Cji\, the connection weight from node&nbsp\;j&nbsp\;to&nbsp\;i) assuming linear dynamics around a fixed point:&nbsp\;dx(t) = (I − C)x(t)θ dt + ζ dw(t). Here\, the process has reversion rate&nbsp\;θ&gt\;0\, and is driven by uncorrelated noise terms with strength&nbsp\;ζ2&nbsp\;(for a multivariate Wiener process&nbsp\;w(t)).\nOur measure for the deviation from stability\,&nbsp\;Dst\, is computed analytically via a power series expansion of network's weighted\, directed connectivity matrix&nbsp\;C&nbsp\;(building on formulations of the network covariance matrix in this fashion [1\,2\,3]).\n\nResults\nWe demonstrate that the deviation from stability Dst directly corresponds to a weighted sum of convergent paired walks on the network (Fig. 1). Our measure explains how dynamics become more stable through small-world transitions to random networks dynamics\, which dominant eigenvalues can remain blind to.\n\nMoreover\, we introduce novel centrality measures capturing how individual nodes contribute to the network’s deviation from stability as sources and targets\, respectively\, of the convergent walks in (Fig. 1).\n\nWe apply the measure to the Epileptor model [4]\, efficiently distinguishing spreading and non-spreading seizures\, and successfully identifying the susceptibility of nodes to seizure dynamics in terms of their embedding in the network.\n\nDiscussion\nOur method provides the first full characterisation of how stability in dynamics relates to underlying network structure. This is more complete than heuristics focused on only dominant eigenvalues\, and provides the interpretation that stability depends solely on convergent walks in the network. The contribution of individual nodes can also be assessed\, as novel meaningful centrality measures.\n\nThis approach holds much promise for the study of epilepsy\, as demonstrated in early application to the Epileptor model where the required change in excitability of a node to cause seizures was found to be directly related to its contribution to network deviation from stability in our framework\, providing a network-based explanation of this sensitivity.\n\nFigure 1.&nbsp\;Deviation from stability D_st corresponds to a weighted count of convergent walks on the network C\n\nReferences\n[1] Schwarze\, A. C.\, & Porter\, M. A. (2021). Motifs for processes on networks. SIAM Journal on Applied Dynamical Systems\, 20(4)\, 2516–2557.\n[2] Barnett\, L.\, Buckley\, C. L.\, & Bullock\, S. (2009). Neural complexity and structural connectivity. Physical Review E\, 79(5)\, 051914.\n[3] Lizier\, J. T.\, Bauer\, F.M.\, Atay\, F.\, & Jost\, J. (2023). Analytic relationship of relative synchronizability to network structure and motifs. Proceedings of the National Academy of Sciences\, 120(37)\, e2303332120.\n[4] Proix\, T.\, Bartolomei\, F.\, Chauvel\, P.\, Bernard\, C.\, & Jirsa\, V. K. (2014). Permittivity coupling across brain regions determines seizure recruitment in partial epilepsy. Journal of Neuroscience\, 34(45)\, 15009–15021.\n\nAcknowledgement\nWe acknowledge the use of The University of Sydney’s high-performance computing cluster Artemis and National Computational Infrastructure in generating results.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:0a879ee13b5bf90ac3f4360789ffc7bd
URL:http://cns2026.sched.com/event/0a879ee13b5bf90ac3f4360789ffc7bd
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SUMMARY:P059: Whole-brain effective connectivity from residual-based ridge regression in resting-state fMRI
DESCRIPTION:Introduction\nLinear predictive models provide a computationally efficient starting point for estimating effective connectivity. However\, multicollinearity of fMRI is a major challenge\, which may cause overfitting and instability. Previous approaches have used partial conditioning or sparse modelling to reduce overfitting which may exclude relevant predictors [1]. Moreover\, low-frequency BOLD signals result in strong autoregression in the timeseries\, which dominates the models. In this work\, we evaluate – in terms of cross-validated predictability – various approaches to building multivariate autoregressive (MVAR) whole-brain effective network models of fMRI brain activity\, which specifically handle their strong multicollinearity and autoregression.\n\n\nMethods\nThe HCP rfMRI data were denoised\, detrended\, and deconvolved [2]. The data were parcellated using the Gordon atlas and analysed as a 333-node whole-brain ROI network. For each node\, a first-order univariate autoregressive model was fitted\, and its out-of-sample R2&nbsp\;was computed as the baseline. MVAR models were then built incorporating all other sources at the previous time step\, to predict both original time series and residuals after subtracting autoregressive components\, using ridge-regularised first-order least-squares regression. Model performance was evaluated using the mean out-of-sample R2 across nodes (90% training / 10% testing). The ridge penalty λ with the highest mean R2 was selected.\n\nResults\nThe problem of collinearity and high dimensionality is highlighted in that MVAR models predicting original time series perform worse than baseline self-predictive models\, whether ridge regression is included or not. Improvements were only achieved with models predicting residuals after autoregression\, with optimal ridge parameter (λ = 30.0) giving a mean out-of-sample R2 of 0.0347. Using the Yeo 7 modules [3]\, visual and somatomotor systems exhibited the highest predictability (R2 &gt\; 0.0400). Limbic regions showed lower predictability (R2 of 0.00658). The effective connectivity matrix from the residual model exhibits asymmetric directed influences\, with modular organisation aligned with the Yeo 17 modules&nbsp\;(Fig. 1) [3].\n\nDiscussion\nThe higher predictability of visual and somatomotor regions in the one-step model is consistent with their relatively short temporal windows\, which may support rapid perceptual and sensorimotor processing [4]. Within the visual system\, the strong links between Visual A and Visual B are also consistent with its hierarchical feedforward and feedback organisation. Visual A appears to pass information to Visual B\, while Visual B shows strong coupling with control networks. This may reflect a pathway through which visual information is processed\, then activates frontoparietal control network. In contrast\, the lower predictability of limbic regions may reflect slower\, more internally driven dynamics related to memory and emotion [4].\n\nFigure 1.&nbsp\;Connectivity matrix estimated from residual model using ridge regression (λ = 30.0) and reordered by Yeo 17 functional networks. The value at row i and column j represents the predictive weight from source node i to target node j. &nbsp\;Red: positive predictive weights. Blue: negative predictive weights.​\n\nReferences\n\n Valdés-Sosa\, P. A.\, et al. (2005). Estimating brain functional connectivity with sparse multivariate autoregression. Philosophical Transactions of the Royal Society B: Biological Sciences\, 360(1457)\, 969–981. Smith\, S. M.\, et al. (2013). Resting-state fMRI in the Human Connectome Project. NeuroImage\, 80\, 144–168. Yeo\, B. T. T.\, et al. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology\, 106(3)\, 1125–1165. Gollo\, L. L.\, et al. (2015). Dwelling quietly in the rich club: Brain network determinants of slow cortical fluctuations. Philosophical Transactions of the Royal Society B: Biological Sciences\, 370(1668)\, Article 20140165. \n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:251ff58260558ab6438e8f30c4d13fec
URL:http://cns2026.sched.com/event/251ff58260558ab6438e8f30c4d13fec
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SUMMARY:P060: Biological Reservoir Computing in Modular Human iPSC-Derived Neuronal Networks
DESCRIPTION:Introduction\nWhile human brain mapping characterizes these relationships at the macroscale\, advancements in Synthetic Biological Intelligence (SBI) now allow us to investigate them in controllable\, human iPSC-derived neural systems [1]. To systematically probe these mesoscale dynamics\, we utilized the CL1 platform\, which facilitates high-level programmability of in vitro networks\, allowing for precise spatiotemporal electrical stimulation and real-time functional readouts. Treating cultures as physical reservoirs\, we ask whether enforcing modular connectivity (segregation + integration) enhances separability of neural state trajectories compared with unstructured 2D monolayers\, across spatial\, temporal\, and spatio-temporal classification benchmarks.\n\nMethods\nIn this work\, human iPSC-derived cortical and hippocampal neurons were cultured as 2D monolayers or in 60-module PDMS microfluidic devices enforcing modular connectivity) coupled by a peripheral loop enabling re-entrant paths. Spikes were recorded on CL1 at 25 kHz\; stimulation/noise artifacts were removed via waveform PCA + GMM clustering. We tested reservoir encoding with spatial source discrimination\, Morse ‘S’ vs ‘O’ sequence decoding\, and MNIST driven as 16-channel\, 5-step spiking tensors. Spike counts were binned to form state vectors x(t) and decoded by logistic regression with 5-fold CV\, against shuffled and test-chip controls.\n\nResults\nAll biological cultures supported above-chance spatial decoding\, but modular devices improved fidelity\, with mixed cortical–hippocampal modular networks reaching ~96% median accuracy (Fig.1). Temporal Morse decoding depended on network dynamics: only highly active modular cultures outperformed shuffled controls\, while hippocampal monolayers were near chance. For MNIST\, monolayers performed poorly\, whereas high-activity modular cultures achieved 69–75% median (max 82–88%) accuracy\; shuffled and test-chip controls stayed at chance. PCA of reservoir states revealed class-separable manifolds only in real modular data.\n\nDiscussion\nWhile physical Reservoir Computing has been demonstrated in non-biological substrates\, its validation in human iPSC-derived neural networks remained limited [2]. In this work\, we show that human iPSC neuronal cultures can act as robust biological reservoirs\, and enforced modular topology functions as a computational regularizer that expands functional dimensionality and supports fading memory for complex spatio-temporal separation. The synergy of biological identity (hippocampal + cortical) and engineered modular connectivity suggests a programmable route to test how structural constraints enable—or impair—computation\, with implications for both SBI applications and mechanistic models of dysconnectivity in brain disorders.\n\nFigure 1.&nbsp\;Binary classification (all distances). Red: neuronal cultures\; cyan: shuffled controls. (B) Morse code: accuracy for letter prediction\; X-axis cell type/activity\; dark green real\, light green shuffled. (C) MNIST: digit-prediction accuracy\; same axes/colors\; dashed line chance. (D) Cortical 60-module MNIST accuracy vs activity (low/med/high). *p&lt\;0.05\, **p&lt\;0.01\, ***p&lt\;0.001.\n​\n\nReferences\n\n1. Kagan\, B. J.\, Kitchen\, A. C.\, Tran\, N. T.\, Habibollahi\, F.\, Khajehnejad\, M.\, Parker\, B. J.\, ... & Friston\, K. J. (2022). In vitro neurons learn and exhibit sentience when embodied in a simulated game-world. Neuron\, 110(23)\, 3952-3969.\n2. Cai\, H.\, Ao\, Z.\, Tian\, C.\, Wu\, Z.\, Liu\, H.\, Tchieu\, J.\, ... & Guo\, F. (2023). Brain organoid reservoir computing for artificial intelligence.&nbsp\;Nature Electronics\,&nbsp\;6(12)\, 1032-1039.\n\n\n\nAcknowledgement\nThis work was funded by Cortical Labs Pty Ltd.
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:25d31938c15216703e7748273d104a7c
URL:http://cns2026.sched.com/event/25d31938c15216703e7748273d104a7c
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SUMMARY:P061: Learning Conduction Delay Distributions from Neural Activity to Study Stability in Delayed Nonlinear Neural Networks
DESCRIPTION:Introduction\n\n\nConduction delays between brain regions play a central role in regulating largescale neural dynamics. Plastic changes in white matter modify said delays\, altering network stability\, synchronization and gain in nonlinear neural systems [1\, 2\, 3]. However\, most theoretical studies assume either simplified or fixed delay structures\, while experimentally measured delay statistics remain difficult to estimate from neural recordings. In this present work\, we combine theoretical analysis to study the influence of distributed delays on the dynamics of delayed nonlinear neural networks (DNLNNs)\, and machine learning to infer delay structure directly from neural time-series data.\n\nMethods\n\nWe study DNLNNs using two complementary approaches. First\, linear stability analysis and random matrix theory are used to characterize how delay statistics influence a DNLNN’s eigenspectrum [4\, 5]\, from which we can infer the network stability. Second\, we develop artificial neural network models capable of learning effective delay distributions from neural activity data. Spiking neural networks (SNNs) [6\, 7] are trained to infer pairwise delay matrices from multivariate neuronal time series\, which are incorporated in simulations of delayed nonlinear networks in order to reproduce the observed dynamics. The full pipeline is illustrated in Figure 1.\n\nResults\n\nThe linear stability analysis shows how delay statistics modify the eigenspectra and thus the stability boundaries of delayed nonlinear neural networks\, linking the mean and variance of conduction delays to dynamical transitions of the system. In parallel\, the machine learning framework successfully recovers structured delay distributions from simulated neural data and accurately reproduces the resulting network dynamics when incorporated into delayed network simulations.\n\nDiscussion\n\nThe obtained results suggest that combining theoretical analysis with datacentered inference provides a promising approach for studying delayed neural systems. Learning delay distributions directly from neural recordings can help bridge the gap between experimentally measured neuronal activity and mathematical models of large-scale brain dynamics\, offering new tools to investigate the influence of conduction delays on the stability and collective dynamics of\nneural networks.\n\nFigure 1.&nbsp\;Pipeline for data-driven inference of conduction delays and stability analysis of delayed neural networks. Simulated neural recordings are used to train a spiking neural network that infers a pairwise delay matrix τ. The learned delays are incorporated into delayed neural network models whose spectral properties and stability are analyzed.\n\nReferences\n\n[1] Lefebvre\, J et. al\,&nbsp\; Myelin-induced gain control in nonlinear neural networks. Commun Phys (2025)\n[2] Sampaio-Baptista\, C. & Johansen-Berg\, H. White Matter Plasticity in the Adult Brain. Neuron (2017)\n[3] Scholz&nbsp\;et. al&nbsp\; Training induces changes in white-matter architecture. Nat Neurosci (2009)\n[4] Pigani\, E.&nbsp\;et. al\,&nbsp\;Delay effects on the stability of large ecosystems. PNAS\, (2022).&nbsp\;\n[5] Leishman\, Q. & Webb\, B. A New Approach to Stability of Delay Differential Equations with Time-Varying Delays via Isospectral Reduction (2025). \n[6] Sun\, P.\, Wu\, J.\, Zhang\, M.\, Devos\, P. & Botteldooren\, D. Delay learning based on temporal coding in spiking neural\nnetworks (2024)&nbsp\;\n[7] Nicola\, W. & Clopath\, C. Supervised learning in spiking neural networks with FORCE training. Nat Commun (2017)\n\n\nAcknowledgement\nThe authors thank members of the Neurophysics and Nonlinear Dynamics group at the University of Ottawa for helpful discussions.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:55fb54132bea6ba0c77515ef4a1c7dbe
URL:http://cns2026.sched.com/event/55fb54132bea6ba0c77515ef4a1c7dbe
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SUMMARY:P062: Mutual information of time sequences in working memory is maximized by parametric heterogeneity in response adaptation and threshold
DESCRIPTION:Introduction\nIt is crucial for an animal’s survival to remember the path taken to reach an important area of a new environment. In the weakly electric fish\, neurons in a thalamic-like region respond to encounters with new objects in bursts whose intensity depends on the time elapsed between events\, a viable strategy to encode spatial information [1]. It was recently shown that\, with such adaptive responses\, parametric heterogeneity is necessary to accurately recall a sequence of multiple encounters from the latest response [2]. Here\, we generalize the result to include working memory (WM) of past adaptive responses and show that variety in adaptation and threshold parameters maximizes mutual information (MI) between responses and time sequences.\n\nMethods\nTiming information is encoded in a resource variable x(n) = 1 - exp(-T(n)/τ)(1-βx(n-1))\, where T(n)&nbsp\;is the time interval between encounters n-1 and n\, τ is the resource recovery rate and β is its history dependence. The response during an encounter n has Poisson statistics with a firing rate proportional to the rectified value max(x-s\,0)\, s being a response threshold (RT) under which a cell does not respond (see Fig 1A). We model WM with a mixture of this distribution and a uniform distribution whose weight increases with the time since the original event (see Fig 1B). This is to represent the unavoidable – and necessary [3] – forgetting of past stimuli. We then use stochastic gradient ascent to optimize MI with respect to the parameters.\n\nResults\nTo quantify the effect of heterogeneity\, we look at the number of neurons N necessary to reach 80% of the theoretical MI limit for different numbers of optimized population P and sequence lengths (see Fig 1C). Without WM or RT\, the optimized parameters are effectively separated into n different populations. As such\, no significant efficiency gain is achieved by increasing P past n. The extra representational capacity of heterogeneous parameters becomes advantageous once WM or RT are available. With or without WM\, heterogeneous RT provides a more efficient method to encode time interval sequences\, requiring fewer neurons for the same information content. In all cases\, heterogeneity offers diminishing returns beyond some value of P.\n\nDiscussion\nThe cases without RT and WM are consistent with previous results which showed that 1 population is optimal when n=1 and that at least n different populations are necessary for n&gt\;1 [2]. WM allows for a middle ground between both cases\, as recollections of previous responses are essentially a noisier version of the single interval case with redundancy coming from later responses. Heterogeneous RT allows for division of labor: a population which does not activate indicates time intervals are in the area given by x&lt\;s. Such silent coding was shown to increase spatial information in the same fish [4]. This stratagem might also be indicative of the power of rectified nonlinearities commonly observed across neural systems in sequence encoding.\n\nFigure 1.&nbsp\;Encoding time sequences is more efficient with parametric heterogeneity. (A) Responses of multiple populations of adaptive cells with response threshold. (B) Recollection of past responses (working memory) during event n. (C) Number of cells N necessary to reach 80% of theoretical maximum information content about a sequence of length n for different number of optimized populations P.​\n\nReferences\n1. Wallach\, A. et al. 2018. A Time-Stamp Mechanism May Provide Temporal Information Necessary for Egocentric to Allocentric Spatial Transformations. eLife 7:e36769. doi:10.7554/eLife.3676\n2.&nbsp\;Lafond-Mercier\, R. et al. 2025. Neural Heterogeneity Enables Adaptive Encoding of Time Sequences. Communications Physics 8(1):504. doi:10.1038/s42005-025-02408-3\n3.&nbsp\;Georgiou\, A. et al. 2021. Retroactive Interference Model of Forgetting. The Journal of Mathematical Neuroscience 11(1):4. doi:10.1186/s13408-021-00102-6\n4.&nbsp\;Haggard\, M.\, & Chacron\, M. J. 2025. Nonresponsive Neurons Improve Population Coding of Object Location. Journal of Neuroscience 45(3). doi:10.1523/JNEUROSCI.1068-24.2024\n\nAcknowledgement\nThis work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) RGPIN/06204-2014 (A.L.) and by the Fonds de recherche du Québec – Nature FRQ B2X/328560 (R.L.M.)
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:362faaedb0c8b566be8ad254f0846a59
URL:http://cns2026.sched.com/event/362faaedb0c8b566be8ad254f0846a59
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SUMMARY:P063: Shared dynamics between active sensing movements and rate-based sensory sampling in electric fish
DESCRIPTION:Introduction\nMost forms of sensory sampling are performed actively [1\,2]. Uncovering the general principles that underly active sensing is thus important for fully understanding animal behaviour. In particular\, active sensing movements are an integral part of behavioural repertoires. These movements are often performed rhythmically\, and some hypothesize that their frequencies match the intrinsic neuronal oscillations of primary processing areas\, thereby enhancing information transfer [3]. Here\, we investigate the coordination between active sensing movement and sensory signals in a pulse-type weakly electric fish.\n\n\nMethods\nWe perform a reanalysis of previously published data [4] of freely-behaving fish in sensory isolation. Simultaneous video and electrical recordings allow for a joint analysis of postural and sensory acquisition dynamics. Behavioural classification is achieved by applying t-SNE to the wavelet spectra of the inter-pulse interval time series. During rhythmic behaviour\, this time series exhibit two dominant frequency bands\, around 0.5 and 1 Hz\, for which we extract narrowband signals. Postural modes during these rhythmic behaviour are extracted by principal component analysis.&nbsp\;\n\nResults\nBy analyzing the pair of narrowband sensory acquisition signals\, we find that they exhibit hallmark features of synchronization\, including phase slips\, where the generalized phase difference jumps by multiples of 2 pi\, limit cycles of the phase dynamics on the torus\, frequency locking\, and a preferred value of the relative cyclic phase. &nbsp\;\n\n\nDiscussion\nIn this work\, we analyze a rhythmic motor behaviour of electric fish where the rate at which sensory samples are acquired is itself modulated at frequencies also appearing in the motor pattern. Moreover\, we show that this modulation is comprised of several frequency bands that are coupled through synchronization dynamics. This suggest that a shared dynamical template is applied in both the sensory acquisition and movement dynamics.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nReferences\n1. Schroeder\, C. E.\, Wilson\, D. A.\, Radman\, T.\, Scharfman\, H.\, & Lakatos\, P. (2010). Dynamics of active sensing and perceptual selection.&nbsp\;Current Opinion in Neurobiology\, 20(2)\, 172–176.\n2. Wachowiak\, M. (2011). All in a sniff: Olfaction as a model for active sensing.&nbsp\;Neuron\, 71(6)\, 962–973.\n3. Haegens\, S.\, & Zion Golumbic\, E. (2018). Rhythmic facilitation of sensory processing: A critical review.&nbsp\;Neuroscience & Biobehavioral Reviews\, 86\, 150–165.\n4. Jun\, J. J.\, Longtin\, A.\, & Maler\, L. (2014). Enhanced sensory sampling precedes self-initiated locomotion in an electric fish.&nbsp\;Journal of Experimental Biology\, 217(20)\, 3615–3628.\n\nAcknowledgement\nThis work was funded by the Natural Sciences and Engineering Research Council of Canada under Grant No. RGPIN-2022-0 531 4.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:764d2ff5a45fac2a81b9dcee6568d7a2
URL:http://cns2026.sched.com/event/764d2ff5a45fac2a81b9dcee6568d7a2
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SUMMARY:P064: Complementary roles of neuronal and synaptic adaptation in regulating network stability
DESCRIPTION:Introduction\nRecurrent connectivity and nonlinearity make neural networks inherently susceptible to destabilization by fluctuating input\, yet the brain must maintain a consistent level of stability. Near the edge of chaos\, decodable information persists over extended timescales. However\, in sparse networks obeying Dale's law\, structural balance alone cannot constrain destabilizing eigenvalue outliers [1]. Furthermore\, external stimuli can alter stability\, especially in nonlinear networks [2]. We hypothesized that two complementary forms of adaptation\, spike frequency adaptation (SFA) and short-term synaptic depression (STD)\, together regulate network stability.\n\nMethods\n\nResults\nOnly networks with both SFA and STD consistently operated near the edge of chaos even as connectivity parameters varied and external stimulation changed. Networks without dual adaptation were much more likely to be overly stable or highly chaotic as connectivity parameters were varied. During excitatory stimulation\, networks with no adaptation or with SFA only became significantly more chaotic [2]. As a consequence of remaining near the edge of chaos\, networks with both SFA and STD had the greatest memory capacity.\n\nDiscussion\nSFA and STD provide complementary stabilizing mechanisms that together maintain near-edge-of-chaos dynamics and maximize memory capacity [2]. Multi-timescale SFA approximates fractional differentiation [3\,4]\, connecting our framework to fractional-order dynamical models. Fractional-order network models distinguish epileptic brain states\, and stabilizing their dynamics suppresses seizures in simulation [5]. EEG recordings near the seizure onset zone show power spectral density slope changes consistent with altered adaptation [6]\, suggesting that adaptation dysfunction may contribute to epilepsy.\n\nReferences\n\nAcknowledgement\nThis work was supported by funding from the NIH awarded to B.N.L. (NINDS R01NS129622 and K23NS112339).\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:eb632faa2c60a3fd3b6ee2fe57a8d9f0
URL:http://cns2026.sched.com/event/eb632faa2c60a3fd3b6ee2fe57a8d9f0
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SUMMARY:P065: Characterizing Epileptic Brain Dynamics Through Fractional-Order Modelling
DESCRIPTION:Introduction\nDrug-resistant focal epilepsy often requires identifying the seizure onset zone (SOZ) for resection or neuromodulation\, yet objective biomarkers of SOZ excitability remain limited. Neural adaptation across multiple timescales can reshape EEG power spectra and can be parameterized using fractional-order dynamics. We hypothesize that the fractional order (alpha) of a fractional neuronal network model provides an indirect\, mechanistically grounded measure of neuronal excitability that can be inferred from macroscopic recordings\, with potential utility for SOZ localization [2-4].\n\nMethods\nWe generated synthetic datasets by simulating a recurrent fractional-order neuronal network in which each neuron included a fractional-order filter implementing fractional differentiation consistent with cortical pyramidal-neuron adaptation [1\,2]. Networks were driven by white-noise current input\; alpha was varied while all other parameters were fixed. From inputs and network outputs we extracted phase shift\, phase-locking value\, power spectral density (PSD) slope and band powers\, spectral density\, and Hilbert-spectrum metrics. We fit regression models relating each feature to α and compared goodness of fit across features.\n\nResults\nAcross different α values\, the PSD slope of the network output showed the clearest and most consistent relationship with α. This trend was roughly monotonic. In contrast\, phase-based features and Hilbert-spectrum measures were weaker and more variable. Since a fractional differentiator changes the spectrum as a function of frequency\, the changes in PSD slope provide an interpretable link between signal properties and alpha. These results suggest that PSD slope could be a simple surrogate marker for fractional order and\, indirectly\, for neuronal excitability in EEG-like signals.\n\nDiscussion\nSpectral slope has been linked to synaptic excitation/inhibition balance and hyperexcitability\, and interictal EEG near the SOZ shows flattened PSD slopes consistent with reduced adaptation and increased excitability. Our simulation study suggests that estimating alpha from PSD slope could provide a mechanistically grounded\, low-complexity biomarker for SOZ identification. Next\, we will apply the α-estimation pipeline to clinical EEG recordings with electrode-level SOZ annotations\, evaluating SOZ vs non-SOZ classification performance and robustness across patients and recording states.\n\nReferences\n\n\n1.&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Lundstrom\, B. N.\, et al. (2008). Fractional differentiation by neocortical pyramidal neurons. Nat Neurosci. https://doi.org/10.1038/nn.2212\n2.&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Lundstrom\, B. N.\, & Richner\, T. J. (2023). Neural adaptation and fractional dynamics as a window to underlying neural excitability. PLoS Comput Biol. https://doi.org/10.1371/journal.pcbi.1010527\n3.&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Gao\, R.\, et al. (2017). Inferring synaptic excitation/inhibition balance from field potentials. NeuroImage. https://doi.org/10.1016/j.neuroimage.2017.06.078\n4.&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Lundstrom\, B. N.\, et al. (2021). Low frequency novel interictal EEG biomarker for localizing seizures and predicting outcomes. Brain Commun. https://doi.org/10.1093/braincomms/fcab231\n\nAcknowledgement\nNo funding was received for this work.\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:a2afcca3475e1811a109820078cec87b
URL:http://cns2026.sched.com/event/a2afcca3475e1811a109820078cec87b
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SUMMARY:P066: Variability and Degeneracy In Simulations of Primary Motor Cortex Pyramidal Tract Neurons
DESCRIPTION:Introduction\nThe layer 5B pyramidal neurons (PT5B) are the final output of the primary motor cortex (M1). They suffer from reduced intrinsic excitability in a Parkinsonian mouse model[1]. There is considerable heterogeneity within the population\, with evidence from both electrophysiology and single-cell RNA sequencing[2]. We also suspect there is considerable degeneracy\, where multiple configurations of ion channels can produce similar responses. Here\, we used data-driven multicompartment models to explore the variability and degeneracy of the PT5B neurons.\n\nMethods\n\nWe used BluePyOp to optimize parameters of conductance-based multicompartment neuron models for individual fits to in vitro somatic current clamp data from 133 PT5B healthy control neurons. To explore potential degeneracy\, we optimized parameters independently 20 times for the same experimental response to produce an ensemble of neuron models. As perturbations can reveal the underlying differences\, we modeled the effects of low dose (20nM) tetrodotoxin (TTX)\, which inhibits the persistent sodium channel (NaP).\n\nResults\nThere was substantial variability within electrophysiology (Fig. 1A). Simulations reproduced many features of the voltage traces (Fig. 1B)\, capturing the excitability of individual neurons and the population\, e.g.\, maximum frequency range in experiments 20.5 – 80.4Hz\, and simulation 20.0 – 94.4Hz. Similar voltage traces could be produced with a wide variety of model parameters\, with substantial variability in the contribution of calcium and dendritic currents. Simulations of low-dose TTX reductions in NaP conductance revealed a subpopulation of neurons where there was little or no change in excitability\, with similar maximum frequencies (33% with less than 1% decrease)\, and no change in rheobase (77% of models).\n\n\n\nDiscussion\nThere was substantial variability in the electrophysiology of PT5B neurons\, which can be captured by computational models. The diversity of ion channel genes in single-cell RNA counts suggests the degeneracy seen in computational models is not only the result of the ill-posed inverse problem\, but also a biologically relevant feature of the neurons. Degeneracy has been shown to play a role in the survival of invertebrates[3] and may be relevant to varying resistance to Parkinsonism.\n\nFigure 1.&nbsp\;Variability of PT5B excitability captured by parameter optimization. Voltage traces of PT5B neurons to current clamp at (160 pA\, 320 pA\, and 480 pA) (A) Experiments\, (B) Simulation. (C) Excitability measures demonstrate the variability seen between cells and the ability of parameter optimization to capture both individual responses (highlighted) and the population distribution.​\n\nReferences\n\n1. Chen\, L.\, Daniels\, S.\, Kim\, Y.\, & Chu\, H.-Y. (2021). Cell Type-Specific Decrease of the Intrinsic Excitability of Motor Cortical Pyramidal Neurons in Parkinsonism. The Journal of Neuroscience 41(25)\, 5553–5565.\n2. Yao\, Z.\, Liu\, H.\, Xie\, F.\, Fischer\, S.\, Adkins\, R. S.\, Aldridge\, A. I.\, Ament\, S. A.\, Bartlett\, A.\, Behrens\, M. M.\, Van den Berge\, K.\, Bertagnolli\, D.\, de Bézieux\, H. R.\, Biancalani\, T.\, Booeshaghi\, A. S.\, Bravo\, H. C.\, Casper\, T.\, Colantuoni\, C.\, Crabtree\, J.\, Creasy\, H.\, … Mukamel\, E. A. (2021). A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex. Nature\, 598(7879)\, 103–110.\n3. Goaillard\, J.-M.\, & Marder\, E. (2021). Ion channel degeneracy\, variability\, and covariation in neuron and circuit resilience. Annual Review of Neuroscience\, 44(1)\, 335–357.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:86f1845fff45d53b1ae9294bbae262e2
URL:http://cns2026.sched.com/event/86f1845fff45d53b1ae9294bbae262e2
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SUMMARY:P067: Modeling spreading depolarization in neocortical microcircuits
DESCRIPTION:Introduction\n\nSpreading depolarizations are waves of brief neuronal hyperexcitability followed by prolonged depolarization that propagates through grey matter at a rate of 1–9mm/min. Such waves are associated with multiple neurological disorders\, including epilepsy\, ischemic stroke\, and migraine aura. Neurons are susceptible to SD due to their high energy demand\, particularly for restoring ion concentrations after action potentials (APs). Here\, we model SD to identify different factors that influence neurons and neuronal populations' vulnerability.\n\n\nMethods\nWe build on our prior in vitro model of spreading depolarization[1]\, with connectivity from Potjans-Diesmann cortical microcircuit (PDCM)[2\,3] and O2 sources based on capillaries in human V1. The model was developed in NetPyNE using NEURON/RxD to account for ion concentrations and homeostatic mechanisms\, including Na+/K+-ATPase\, NKCC1\, KCC2\, and dynamic volume changes. A 2.0 x 2.3 cm cross-section of the human cortical plate in V1 with immunostaining for CD34 was used to determine capillary locations. Optuna was used to determine both single-cell parameters and synaptic weights to achieve firing rates\, synchrony\, and irregularity comparable to those of the original PDCM.\n\n\nResults\nWe simulated 13\,000 neurons representing ~1 mm3 of cortex (layers 2-6). We monitored intracellular and extracellular ion concentrations (Na+\, K+\, Cl-) and O2. O2 was supplied by 918 capillaries (density: 199.6/cm2\; cross-sectional area: 16.7±11.9μm2) identified by immunohistochemistry. SD could occur spontaneously when reducing available O2 to simulate hypoxic SD\, or by elevating extracellular K+. Preliminary results suggest that susceptibility to SD varied with layer\, with layer IV being the most vulnerable and layer II/III the most resilient. Network connectivity did not directly relate to a neuron’s vulnerability to SD\, but those that fired at higher frequencies were more vulnerable.\n\nDiscussion\n\nThis model explores the roles of network connectivity\, neuronal density\, neuronal activity\, and heterogeneity of O2&nbsp\;supply that affect the susceptibility of SD of individual neurons and cortical layers. Our model also suggests that the distribution of capillaries affects neurons' ability to maintain homeostasis and physiological firing. Neurons closer to capillaries are better able to sustain their activity when O2&nbsp\;is reduced.\n\n\nReferences\n1. Kelley\, C.\, Newton\, A. J. H.\, Hrabetova\, S.\, McDougal\, R. A.\, & Lytton\, W. W. (2022). Multiscale Computer Modeling of Spreading Depolarization in Brain Slices. eNeuro\, 9(4). https://doi.org/10.1523/ENEURO.0082-22.2022\n2. Potjans\, T. C.\, & Diesmann\, M. (2012). The Cell-Type Specific Cortical Microcircuit: Relating Structure and Activity in a Full-Scale Spiking Network Model. Cerebral Cortex \, 24(3)\, 785–806. https://doi.org/10.1093/cercor/bhs358\n3. Romaro\, C.\, Najman\, F. A.\, Lytton\, W. W.\, Roque\, A. C.\, & Dura-Bernal\, S. (2021). NetPyNE Implementation and Scaling of the Potjans-Diesmann Cortical Microcircuit Model. Neural Computation\, 33(7)\, 1993–2032. https://doi.org/10.1162/neco_a_01400\n\n\n\n\n\nAcknowledgement\nThis research was funded by the National Institute of Mental Health\, National Institutes of Health\, grant number R01 MH086638\, with HPC time from NIH S10 award\, 1S10OD032417-01\, and the Yale Center for Research Computing McClearly cluster. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.\n\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:3a545e378069c199d7a18e83a0b3f993
URL:http://cns2026.sched.com/event/3a545e378069c199d7a18e83a0b3f993
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SUMMARY:P068: A novel method to characterize spatiotemporal propagation
DESCRIPTION:Introduction\nIn a recent study on wide-field calcium images in mice before and after stroke [1]&nbsp\;singular value decomposition (SVD) was used to perform a spatiotemporal analysis of movement-evoked global cortical events. Indicators such as angle and smoothness of the propagation were defined that allowed to compare different stroke rehabilitation therapies. This method worked because the events were truly global (e.g.\, basically all pixels in the field of view were participating). By contrast\, in a follow-up study on a mouse model of autism we now also include non-complete events but for such events SVD does not work. Thus\, novel and more general approaches are needed and this need is addressed here.\n\nMethods\nAdaptive coincidence detection and the SPIKE-synchronizarion and SPIKE-order framework&nbsp\;[2]&nbsp\;are used to identify (global or non-global) events and to sort the participating pixels of each event from leader to follower. From the resulting two-dimensional propagation patterns\, we define various indicators such as completeness (fraction of participating pixels)\, connectedness (clustering of these pixels) and correlation (similarity of rank order among neighboring pixels). The angel of propagation is defined from the resultant length&nbsp\;[3]&nbsp\;of the direction vectors of all pairs of participating pixels and its carefully renormalized amplitude is an indicator of the strength of the propagation.\n\n\nResults\nWe illustrate the new methods using both simulated data for verification and experimental data for exploration. These are typically non-global events\nrecorded using wide-field calcium imaging to monitor cortical activity in a Shank3b mouse model of autism from late development through adulthood\, and under isoflurane anesthesia to manipulate the brain state&nbsp\;[4].\nWe here show that these new methods\, and in particular the angle and the strength of the propagation\, generalise and improve on the original SVD method in terms of both accuracy and speed.\n\nDiscussion\nTogether\, the five indicators completeness\, connectedness\, correlation\, angle and amplitude provide a full characterization of the spatiotemporal activity. Importantly\, this new approach is so far the only method that allows calculating the angle and the strength of the propagation for non-complete global events.\u2028\nThe corresponding scientific article is currently under preparation.\n\nReferences\n[1] Cecchini\, G.\, ... Kreuz\, T. (2021). Cortical propagation tracks functional recovery after stroke. PLoS Comput Biol&nbsp\;17: e1008963. https://doi.org/10.1371/journal.pcbi.1008963&nbsp\;\n[2] Kreuz\, T.\, ... Mulansky\, M. (2017). Leaders and followers: Quantifying consistency in spatio-temporal propagation patterns. New Journal of Physics&nbsp\;19\, 043028. https://doi.org/10.1088/1367-2630/aa68c3\n[3] Andrzejak\, R. G.\, ... Schindler\, K. (2023). High expectations on phase locking: Better quantifying the concentration of circular data. Chaos 33\, 091106.&nbsp\;https://doi.org/10.1063/5.0166468\n[4] Montagni\, E.\, ... Allegra Mascaro\, A. L. (2025). Age-dependent cortical overconnectivity in shank3 mice is reversed by anesthesia. Translational Psychiatry 15 (1)\, 154. https://doi.org/10.1038/s41398-025-03377-5\n\nAcknowledgement\nThis work has been funded by Telethon Seed Grant Spring Renewal 2025 PHEM (GSA25E002)\, the Italian Ministry of Universities and Research on the&nbsp\;project THE Tuscany Health Ecosystem (ECS_00000017)\, MUR_ PNRR.&nbsp\;\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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URL:http://cns2026.sched.com/event/7dce1f8cd0c8b22bd8455d5692ba6e5a
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SUMMARY:P069: EFFECT OF Ca2+ BUFFERS WITH MULTIPLE BINDING SITES ON SHORT-TERM SYNAPTIC PLASTICITY
DESCRIPTION:Introduction\n\n\nCa2+ions are essential for triggering and modulating synaptic neurotransmitter release. Since most Ca2+\nentering the cell is quickly bound by Ca2+ buffers\, these molecules strongly shape the synaptic dynamics.\nTwo mechanisms have been proposed for buffer-driven short-term synaptic changes: (1) facilitation by\nbuffer saturation [1–3]\, and (2) facilitation by translocation of membrane-bound buffers into the synaptic\nterminal [4].\nHere\, we systematically examine the impact of Ca2+buffers on the dynamics of Ca2+transients. We\nfocus on buffers with two Ca2+binding sites with distinct binding kinetics\, characterizing buffers such as\ncalmodulin and calretinin\, and we explore the effects of changes in buffer diffusivity upon Ca2+binding.\n\nMethods\nTo explore the impact of Ca2+buffering properties on Ca2+transient dynamics\, we numerically solve\nreaction–diffusion equations describing the influx\, diffusion\, and mutual binding of Ca2+and buffer con-\ncentration fields in an enclosed volume simulating a single synaptic terminal. Vesicle pool dynamics are\nnot modeled\, as we focus on synaptic plasticity effects arising solely from changes in local Ca2+transients\nduring a train of action potentials\, upstream of additional plasticity effects due to vesicle pool depletion\nand recovery. Equations are solved using the CalC (Calcium Calculator) software (GitHub: mvvik)\, with\nwrapper code written in MATLAB (MathWorks\, Inc.).\n\nResults\nBeyond facilitation via buffer saturation and dislocation\, we find that strong depression of Ca2+transients\ncan occur in the presence of Ca2+-buffers with two binding sites\, provided the second binding event is\nmuch faster than the first. We refer to this effect as buffer priming\, previously hypothesized in response\nto calretinin overexpression [5]. We also demonstrate that certain buffering regimes produce complex\nCa2+dynamics\, with facilitation followed by depression or vice versa. Finally\, we systematically analyze\nhow these effects depend on binding properties and changes in buffer diffusivity through parameter sweeps.\n\nDiscussion\nAlthough our results are based purely on computational modeling\, it is valuable to systematically explore\nhow facilitation and depression of Ca2+transients depend on the kinetics\, affinities\, and mobilities of distinct\nCa2+-bound states of buffers with multiple binding sites. Such buffers are widely expressed in neurons\,\nyet their properties are difficult to measure. Buffer expression profiles differ across neuron classes\, shaping\nthe synaptic dynamics. We believe this work helps elucidate the interplay between Ca2+homeostasis and\nshort-term synaptic dynamics\, revealing the broader impact of complex buffer binding dynamics on cellular\nCa2+signaling.\n\nReferences\n[1] Klingauf\, J.\, & Neher\, E. (1997). Modeling buffered ca2+ diffusion near the membrane(...) Biophysical\nJournal\, 72 (2)\, 674–690.\n[2] Blatow\, M.\, Caputi\, A.\, Burnashev\, N.\, Monyer\, H.\, & Rozov\, A. (2003). Ca2+ buffer saturation\nunderlies paired pulse facilitation in calbindin(...) Neuron\, 38 (1)\, 79–88.\n[3] Matveev\, V.\, Zucker\, R.\, & Sherman\, A. (2004). Facilitation through buffer saturation(...) Biophysical\njournal\, 86 (5)\, 2691–2709.\n[4] Burnashev\, N.\, & Rozov\, A. (2005). Presynaptic ca2+ dynamics\, ca2+ buffers(...) Cell calcium\, 37 (5)\,\n489–495.\n[5] Bolshakov\, A.\, Kolleker\, A.\, Volkova\, E.\, Valiullina-Rakhmatullina\, F.\, Kolosov\, P.\, & Rozov\, A.\n(2019). Overexpression of calretinin(...) Frontiers in Cellular Neuroscience\, 13\, 91.\n\nAcknowledgement\nI would like to sincerely thank my advisor\, Victor Matveev\, for his invaluable guidance\, support\, and mentorship throughout this work. It has\, and will continue to be\, a privilege to work with him.\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:76671f64708488ae3e9071237628e844
URL:http://cns2026.sched.com/event/76671f64708488ae3e9071237628e844
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SUMMARY:P070: Modeling of Cross-Frequency Brain Dynamics in Mice Cortical Region
DESCRIPTION:Introduction\nLarge-scale brain activity shows complex dynamics that can be better interpreted through mathematical modeling. The very nature of neural activity - multi-scale\, noisy\, nonstationary\, and highly variable across space and subjects - makes it difficult to define a model that is both computationally feasible and biologically meaningful. Here\, we focus on the oscillatory components of this activity and propose a model of cross-frequency band interactions. Couplings between these bands can facilitate network communications and modulate information transfer [1]. We develop a prediction-based linear model estimating effective connectivity and characterize such couplings in cortical recordings of mice under anesthesia.\n\n\nMethods\nIn terms of data processing\, we extracted time-resolved band power using a continuous Morlet wavelet filter bank\, downsampled\, applied log-amplitude scaling\, and performed per-band\, per-channel z-score normalization.\nWe then modeled effective connectivity with a linear time-delayed model to track how band power evolves across channels over time. The model is estimated by minimizing a cost function combining the sum of squared residuals between predicted and observed states with an L1 penalty on the transition matrix [2]\, which promotes sparsity by shrinking weak connections toward zero\, improving generalization and interpretability. A regularization parameter controls the trade-off between data fit and sparsity.\n\nResults\nWe evaluated our model on ECoG data from a 32-electrode array evenly covering a large portion of the cortical dorsal surface&nbsp\;(Fig.1 E) in anesthetized mice [3]. From channel-wise Morlet wavelets\, we defined physiologically relevant bands (delta\, theta\, spindles\, low gamma) and used the model to predict band-power dynamics from cross-frequency and cross-channel couplings. Preliminary results from this approach revealed key properties of cortical activity: certain frequency bands\, such as delta and low gamma\, show more predictive influence than others on the evolution of the cortical network (Fig.1 D). We also identified cross-channel and cross-frequency interactions\, as well as the predictive influence of each band per channel (Fig.1 A-C).\n\n\nDiscussion\nThis framework captures how neural activity spreads across cortical regions and frequency bands. In this formulation\, effective connectivity describes how activity in one frequency band shapes future activity in another by including past temporal information in the model formulation. By representing each frequency’s power as a separate state\, the model naturally integrates these influences\, allowing it to capture both cross-channel and&nbsp\;cross-frequency interactions\, as seen in Fig.1 A-B\, respectively. Thus\, preliminary results highlight the model’s promise for describing complex multi-scale brain dynamics.\n\nFigure 1.&nbsp\;A) Cross-channel interactions summed over all frequency bands. B) Cross-frequency interactions summed over all channels. C) Summed band activation per channel. D) Frequency band predictive influence in future states. E) Illustration of the mouse dorsal portion of the cortex with the 32-electrode ECoG grid.​\n\nReferences\n1. Canolty\, R. T.\, & Knight\, R. T. (2010). The functional role of cross-frequency coupling. Trends in cognitive sciences\, 14(11)\, 506–515.&nbsp\;https://doi.org/10.1016/j.tics.2010.09.001\n2. Tibshirani\, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology\, 58(1)\, 267–288.&nbsp\;https://doi.org/10.1111/j.2517-6161.1996.tb02080.x\n3. Pedrosa\, R.\, Nazari\, M.\, Mohajerani\, M. H.\, Knöpfel\, T.\, Stella\, F.\, & Battaglia\, F. P. (2022). Hippocampal gamma and sharp wave/ripples mediate bidirectional interactions with cortical networks during sleep. Proceedings of the National Academy of Sciences\, 119(44)\, e2204959119. https://doi.org/10.1073/pnas.2204959119\n\n\nAcknowledgement\nThe authors thank Rafael Pedrosa for the dataset. This work is supported by the Project Dutch Brain Interface Initiative (DBI2) with Project number 024.005.022 of the Research Programme Gravitation\, which is financed by the Dutch Ministry of Education\, Culture and Science (OCW) via the Dutch Research Council (NWO). The authors declare no conflict of interest.
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:c07dcccf829a9475698d882339c6d8c7
URL:http://cns2026.sched.com/event/c07dcccf829a9475698d882339c6d8c7
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SUMMARY:P071: Balanced E–I gain sets spike predictability from synaptic history
DESCRIPTION:Introduction\nNeurons encode\, compute\, and transmit information through spikes\, yet the functional meaning of a spike depends on how reliably it reflects the recent synaptic events that generated it. In many neural circuits\, excitation and inhibition are co-active and approximately balanced\, placing neurons in a conductance-driven regime where spike timing emerges from the interaction between fast excitatory–inhibitory (E–I) competition and intrinsic membrane nonlinearities such as thresholding\, refractoriness\, and adaptation. Here we ask how the predictability of near-future spiking from recent local E–I history depends on the magnitude and statistics of balanced synaptic drive.\n\n\nMethods\nWe simulated a biophysical neuron receiving balanced excitatory and inhibitory synaptic inputs. Three parameters were systematically varied: (i) balanced synaptic gain\, implemented by increasing excitatory and inhibitory conductance per event together in matched proportion\; (ii) presynaptic input rate\, shaping the temporal statistics of synaptic events\; and (iii) the balance point of mean drive (balanced voltage). For each condition\, we evaluated how well recent E–I history predicted imminent spikes. Predictability was quantified using two decoders: a linear generalized linear model (GLM) and a nonlinear multilayer perceptron (MLP). (Fig. 1). Model performance was evaluated using precision–recall area under the curve (PR-AUC).\n\nResults\nPredictability increased steeply with balanced E–I gain across the explored parameter space. Standardized regression analysis showed that balanced synaptic gain was the dominant determinant of PR-AUC\, exerting a substantially larger effect than other parameters. Firing rate provided the second strongest contribution\, whereas variations in presynaptic input frequency and balanced mean voltage produced comparatively minor effects. Increasing input frequency modestly improved predictability at low firing regimes but showed rapid saturation once firing rates plateaued. Across all parameter regimes\, the MLP decoder consistently outperformed the GLM decoder.\n\n\nDiscussion\nThese findings reveal a regime-dependent predictability landscape: Strengthening excitation and inhibition together increases the reliability with which recent local synaptic competition can be decoded from spikes. In contrast\, input frequency and mean voltage balance exert limited direct influence. Together\, these findings indicate that synaptic gain modulation can tune neuronal computation between stochastic spiking and history-dependent gating without requiring shifts in E–I ratio or intrinsic neuronal properties.\n\nFigure 1.&nbsp\;Schematic overview of the spike prediction pipeline. A biophysical neuron model receives balanced excitatory and inhibitory synaptic inputs. The recent temporal history of these inputs\, along with the neuron’s own past output spikes\, is extracted and fed into a deep neural network (DNN) to predict imminent spikes.​\n\nAcknowledgement\nWork supported in part by graduate summer funding to HL from the Program in Computational Biology and Biomedical Informatics\, and by NIH R01 NS011613 to RAM.&nbsp\;The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:466f2864b604c0c50d4a8808ac934fa9
URL:http://cns2026.sched.com/event/466f2864b604c0c50d4a8808ac934fa9
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SUMMARY:P072: Event-based machine-learned reduction of biophysically detailed neuron models
DESCRIPTION:Introduction\nBiophysical models impose substantial computational burdens\, limiting large-scale simulation of complex neuronal dynamics\, particularly with morphologically detailed neuron models. Previous evidence [1] suggests that the neuronal spiking behavior is primarily constrained by recent causal stimulus events rather than continuous full-timescale integration\, which makes event-driven dynamical computation possible. We developed a machine-learning framework with a recurrent architecture for sustained spike prediction. The framework replaces computationally expensive continuous differential equation solving with an event-based mechanism\, enabling temporal computation without requiring timestep-level simulation.\n\nMethods\nThe&nbsp\;framework uses recent excitatory and inhibitory events as input to recurrent neural architectures (LSTM/GRU) to encode temporal neuronal dynamics and learn a&nbsp\;reduced&nbsp\;representation of the latent state.&nbsp\;Then we utilize a downstream multilayer perceptron to predict whether or not the neuron will spike\, and if it does\, the next-spike time (NST).&nbsp\;Following individual spike evaluation\, the trained framework was further tested under&nbsp\;40000&nbsp\;ms&nbsp\;sustained neuronal activity&nbsp\;driven by excitatory and inhibitory event streams at 200 Hz and 67 Hz\, respectively\, where&nbsp\;predicted spikes constantly influenced&nbsp\;subsequent&nbsp\;neuronal activity\,&nbsp\;to&nbsp\;estimate long-term temporal stability and dynamic spike prediction performance.\n\n\nResults\nTrained on a dataset of over one million stimulation trials and tested on 38k trials\, the proposed event-driven framework achieved an F1 score and AUC of &gt\; 0.99\, with a next-spike timing (NST) mean absolute error (MAE) of 0.07 ms\, approaching the intrinsic temporal resolution of the NEURON simulation environment. Under sustained neuronal activity over a 40\,000 ms simulation window\, the framework reproduced 929 of 947 ground-truth spikes with only 51 missed spikes and 33 false-positive predictions\, exceeding the performance of the baseline event-based model\, which reproduced 135 false positives and missed 76 spikes under the same conditions.\n\nDiscussion\nThe stable performance&nbsp\;observed&nbsp\;under sustained neuronal activity&nbsp\;suggests&nbsp\;that the framework can preserve long-term temporal consistency beyond isolated spike prediction&nbsp\;tasks\, potentially including in network models\, with potential run-time improvements for large cell models.&nbsp\;However\, the resulting errors differ from those of traditional&nbsp\;biophysical&nbsp\;models\,&nbsp\;so further work is needed to understand their effects on system behavior.&nbsp\;Studying parameter sweeps or heterogeneous models would require incorporating parameters of interest into the machine learned model\, re-introducing complexity.&nbsp\;Extending the framework to generalize across broader biophysical conditions&nbsp\;remains&nbsp\;an important direction for future work.\n\nReferences\nCudone\, E.\, Lower\, A. M.\, & McDougal\, R. A. (2023). Reproducibility of biophysical in silico neuron states and spikes from event-based partial histories. PLOS Computational Biology\, 19(10)\, e1011548.\n\n\nAcknowledgement\nResearch was supported by the National Institute of Neurological Disease and Stroke of the National Institutes of Health under award number R01NS011613.&nbsp\;The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:1ca604267eb6d2caa229bb099fb41d85
URL:http://cns2026.sched.com/event/1ca604267eb6d2caa229bb099fb41d85
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SUMMARY:P073: Evaluating parameter space stiffness of maximum entropy models and departure from criticality
DESCRIPTION:Introduction\nThe emergence of collective behaviour in biological systems remains to be fully understood. Models inspired by statistical physics\, where neurons are treated as Ising-like variables\, provide an appealing approach to fill this conceptual gap. They offer\, notably\, a principled framework for inferring the effective interactions and constraints that shape the collective activity\, as well a method for detecting criticality without relying on avalanche calculations [1].\n\n\nMethods\nWe focus here on Maximum Entropy Models (MEM)\, where a probability distribution over states is inferred by maximizing its entropy while enforcing a match between the expectation values of a given set of observables and their empirical averages. This procedure results in a Boltzmann-like distribution with temperature &nbsp\;and a Hamiltonian constrained by the chosen observables\, parameterized by a set of Lagrange multipliers. While this approach is typically applied to experimental data\, here we apply it to simulation results in order to systematically study how variations of the structural and dynamical model parameters map to changes in the effective parameters of the inferred MEM.\n\n\nResults\nWe implement an integrate-and-fire (IF) model known to be poised at criticality [2] and train a MEM consistent with the chosen observables\, namely the mean activity and pairwise correlations. The inferred Boltzmann-like distribution is parameterized by so-called local fields and effective couplings. It can be used to calculate the covariance matrix between the observables\, which\, in turn\, is equivalent to the Fisher Information Matrix (FIM). We characterize the stiffness of the model based on the eigenvalue spectrum\, and a preliminary analysis consists of imposing incremental parameter changes in the direction of the leading eigenvalue. This leads to a new model whose departure from criticality can then be evaluated.\n\n\nDiscussion\nFuture work includes the study of the covariance between structural parameters and MEM parameters\, with the goal to identify how structural features of the IF model are associated with criticality. Further investigation will also evaluate whether a two-compartiment model endowed with an intrinsic bursting mechanism can be tackled with MEMs. &nbsp\;\n\n\nReferences\n1. Meshulam\, L.\, & Bialek\, W. (2025). Statistical mechanics for networks of real neurons.&nbsp\;Reviews of Modern Physics\,&nbsp\;97(4) 045002. doi:10.1103/jcrn-3nrc\n2. Simões\, T. S. A. N.\, Filho\, C. I. N. S.\, Herrmann\, H. J.\, Andrade\, J. S.\, Jr\, & de Arcangelis\, L. (2024). Thermodynamic analog of integrate-and-fire neuronal networks by maximum entropy modelling.&nbsp\;Scientific Reports\,&nbsp\;14(1)\, 9480. doi:10.1038/s41598-024-60117-3\n\nAcknowledgement\nThis work was funded by the Natural Sciences and Engineering Research Council of Canada and the New-Brunswick Innovation Foundation.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:cff54b5c3ed6c644c5ce3cb08bb79c56
URL:http://cns2026.sched.com/event/cff54b5c3ed6c644c5ce3cb08bb79c56
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SUMMARY:P074: Dynamic Functional Connectivity Resolves Brain Integration-Segregation Trade-off Under Costly Links
DESCRIPTION:Introduction\nDynamic functional connectivity (dFC) is a pervasive feature of brain activity\, even at rest\, but its functional role remains debated. We ask whether temporal reconfiguration of functional links can be advantageous when maintaining links is costly. We hypothesize that dFC helps resolve the trade-off between large-scale integration and transient local segregation by reusing a limited communication budget over time.\n\n\nMethods\nResting-state fMRI dFC was modeled as a cost-constrained temporal communication network. Sliding-window functional-connectivity frames were binarized at different densities and compared with equal-cost static architectures and temporal null models. Information dispatch was quantified using smart and random walkers\, measuring irrigation reach\, penalized latency\, temporal clustering\, return latency and neighborhood retention. A connectome-based mean-field model was used as a mechanistic benchmark.\n\n\nResults\nEmpirical dFC outperformed equal-cost static networks in sparse\, high-cost regimes\, allowing information to reach more nodes and reducing penalized latency. However\, more randomized temporal nulls often spread information even more efficiently\, showing that empirical dFC is not optimized for diffusion alone. Empirical networks also preserved strong spatial and temporal clustering\, rapid return to source nodes and high neighborhood retention\, supporting transient local segregation.\n\n\nDiscussion\nThese findings suggest that resting-state dFC is neither a mere by-product of neural dynamics nor a simple maximizer of global spreading. Instead\, it reflects a structured regime of controlled persistence and renewal: local neighborhoods remain stable long enough for transient recirculation\, before broader network-wide spreading occurs. dFC may therefore be a resource-efficient solution to competing demands for integration and segregation in brain communication.\n\n\nReferences\nMengiste\, S.A.\, and Battaglia\, D. (2026). Dynamic Functional Connectivity Resolves Brain Integration-Segregation Trade-off Under Costly Links. arXiv. https://doi.org/10.48550/arXiv.2604.11608.\n\nAcknowledgement\nThis work was supported by the PEPR Sané Numérique program (France 2030)\, project “Brain Health Trajectories (BHT)”\, implemented by the Agence Nationale de la Recherche (ANR) under grant number ANR-22-PESN-0012-BHT. We wish to thank Alain Barrat\, Caio Seguin and Sinisa Pajevic for inspiring discussions and Anagh Pathak for sharing time-series from connectome-based simulations.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:e2fc3a911db928b0a741762e6619f818
URL:http://cns2026.sched.com/event/e2fc3a911db928b0a741762e6619f818
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SUMMARY:P075: Simulating bilingual naming in laterally-connected self-organizing maps
DESCRIPTION:Introduction\nThe BiLex model simulates the bilingual language system: two phonetic self-organizing maps (SOMs) represent English and Spanish words\, linked to a shared semantic map via bidirectional associative connections (Fig.1a) [1]. Traditional SOMs use neighborhood activations\; the laterally-connected BiLex uses short-range excitation and long-range inhibition\, enabling mechanistic examination of within- and between-map lexical-semantic interactions. Semantic effects — familiarity\, typicality\, and specificity — can emerge from lateral interactions. Lateral excitation-inhibition focuses activation over settling steps\, providing a novel response time measure. Naming accuracy and response time were examined in English and Spanish.\n\n\nMethods\nPhonetic representations used IPA-based feature encodings. Semantic representations\, superordinate membership\, and typicality were defined using GPT-4o [2] (&gt\;90% MTurk agreement): feature semantics and superordinate-subordinate pairs (yes/no word-feature queries e.g.\, "is&nbsp\;apple&nbsp\;a&nbsp\;fruit?"\; typicality as a 0-1 category-membership rating. SOMs were trained by word-frequency sampling (a familiarity proxy)\, learning boosted or penalized based on expected representation. Learned connections linked all maps\, superordinates on the phonetic map via subordinates. Naming was simulated by presenting a word to the semantic map\, driving lateral interactions within and between maps until activation settled\; response time was settling steps to certainty.\n\n\nResults\nOverall accuracy was 73.6% (English) and 70.5% (Spanish)\, within the range for healthy bilingual adults on naming tasks [3]. Most errors were semantically related (49.1%)\, superordinate responses (28.0%)\, or phonological (9.2%)\; 8.6% were unrelated and 3.0% failed to converge. Typicality and word frequency were positively correlated\, rs(664) = .115\, p = .003. Higher frequency was correlated with faster (English rs(664) = −.261\, Spanish rs = −.180) and more accurate naming (English rs(664) = .157\, Spanish rs(664) = .225\; all p &lt\; 0.001). See Fig. 1b\, c. Excitation-inhibition dynamics enabled response time measurements via map activation settling steps. The laterally-connected BiLex model successfully simulated naming in both languages.\n\nDiscussion\nA biologically plausible model should produce human-like errors rather than random failures. Error types matched those documented in the naming literature on semantic cognition and lexical access [4]: within-category coordinate errors\, phonological similarity errors\, or superordinate responses to subordinate category members. A positive correlation between typicality and word frequency suggested that higher-frequency words tend to be more typical category members\, and atypicality in low-frequency words may contribute to naming difficulty. Structured errors validate the model and provided insight into underlying semantic and lexical mechanisms. Future work will examine lexical-semantic impairments and potential treatments.\n\nFigure 1.&nbsp\;Left: BiLex model with lateral connections\, adapted from [1]. Naming is simulated by presenting an input to the semantic map\, propagating activation through bidirectional associative connections\, and producing a response from a phonetic map\, in English and Spanish. Right: accuracy significantly increased with word frequency (p &lt\; 0.001) while response times were significantly faster (p &lt\; 0.001).\n\nReferences\n[1] Peñaloza\, C.\, et al. (2019). BiLex: A computational approach to the effects of age of acquisition and language exposure on bilingual lexical access. Brain and Language\, 195\, Article 104643. https://doi.org/10.1016/j.bandl.2019.104643\n[2] OpenAI. (2024). Hello GPT-4o. https://openai.com/index/hello-gpt-4o/\n[3] Kohnert\, K. J.\, Hernandez\, A. E.\, & Bates\, E. (1998). Bilingual performance on the Boston Naming Test: Preliminary norms in Spanish and English. Brain and Language\, 65(3)\, 422–440. https://doi.org/10.1006/brln.1998.2001\n[4] Rogers\, T. T.\, et al. (2015). Disorders of representation and control in semantic cognition: Effects of familiarity\, typicality\, and specificity. Neuropsychologia\, 76\, 220–239. https://doi.org/10.1016/j.neuropsychologia.2015.04.015\n\n\n\nAcknowledgement\nThis research was supported through the NIH under grant 5R01DC020653-02.\n\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:dc432a62e6c9ec7cdfbd0dfffe00f365
URL:http://cns2026.sched.com/event/dc432a62e6c9ec7cdfbd0dfffe00f365
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SUMMARY:P076: A Recurrent Circuit for Two Streams of Evidence Accumulation As a Decision-Making Model
DESCRIPTION:Introduction\n\nMany decisions require combining multiple evidence streams into a single action. In the human double-decision random-dot task\, participants view moving colored dots and report a single choice among four spatial targets that jointly encode motion direction (left/right) and dominant color (blue/yellow) [1].&nbsp\; Motion and color coherences jointly determine the correct target. We show that a single LIP-inspired recurrent circuit reproduces the key error-rate (ER) and reaction-time (RT) signatures of both 2T trials (two targets\; motion only) and 4T trials (four targets\; motion+color)\, including similar ERs but longer RTs in 4T compared with 2T. We then ask how adding a second stream reshapes the decision-manifold geometry of population dynamics.\n\n\n\nMethods\nWe used an E/I neural-field model of LIP with distance-dependent connectivity\, extending the 2-target circuit (2T) of Monsalve-Mercado et al. [2] to four targets (4T) with four target-in (Tin) populations. Each target received a Gaussian input bump whose amplitude scaled with stimulus coherence. Tin activity bumps competed via shared broad inhibition (winner-take-all). Motion and color were independent noisy evidence streams with separate gains. Stimulus drive was maintained throughout the entire decision process. ER was the fraction of trials in which the correct Tin won. RT was defined as the first time the gap between the largest and second-largest Tin activities exceeded a fixed value. The decision manifold is reproduced via PCA.\n\nResults\n\nOur 4T network qualitatively captured the dependence of behavioral error rate (ER) and reaction time (RT) on motion and color coherence in the double-decision task [1]: low coherences yielded higher ERs and longer RTs\, whereas high coherences produced lower ERs and shorter RTs. Motion outperformed color in the data and was captured by a higher motion-input gain. Model RTs required an additive 0.4 s offset consistent with non-decision time. Comparing matched 2T and 4T conditions\, ERs were similar but RTs were consistently longer in 4T\, consistent with behavioral results. Population activity showed a richer decision-manifold geometry in 4T\, with participation ratio increasing nonlinearly from just above 2 in 2T to around 6 in 4T.\n\n\n\nDiscussion\n\nA single LIP-inspired E/I neural field with local excitation and broad inhibition can account for core signatures of human double decisions [1]. In the model\, four Tin activity bumps compete within a shared inhibitory pool\, naturally producing a reaction-time cost in 4T without a comparable loss in accuracy\, consistent with parallel evidence acquisition but a serial bottleneck in commitment. Adding a second evidence stream also reshapes state-space structure: effective dimensionality increases markedly from 2T to 4T\, and low-coherence trials dwell longer near a quasi-indecision region before diverging toward the eventual choice state. This circuit provides a mechanistic bridge between multi-attribute behavior and decision-manifold geometry.\n\n\nReferences\n\n1 - Kang\, Y. H.\, Löffler\, A.\, Jeurissen\, D.\, Zylberberg\, A.\, Wolpert\, D. M.\, & Shadlen\, M. N. (2021). Multiple decisions about one object involve parallel sensory acquisition but time-multiplexed evidence incorporation. eLife\, 10\, e63721. https://doi.org/10.7554/eLife.63721\n2 - Monsalve-Mercado\, M. M.\, Stine\, G. M.\, Shadlen\, M. N.\, & Miller\, K. D. (2025). The geometry of the neural state space of decisions. bioRxiv. https://doi.org/10.1101/2025.01.24.634806\n\n\nAcknowledgement\nI thank Prof. Kenneth D. Miller and his lab members for support and discussions. I was supported by the M.Sc. Neuroscience program at the Bernstein Center for Computational Neuroscience at the University of Freiburg\, Germany and by an external research internship at Columbia University at the City of New York\, USA.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:65fbc65e0c670187ae3848bdad70d496
URL:http://cns2026.sched.com/event/65fbc65e0c670187ae3848bdad70d496
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SUMMARY:P077: Homophily-informed generative models of brain maps
DESCRIPTION:Introduction\nWhole-brain maps of structural and functional features provide complementary views of cortical organization [1]. Despite their diversity\, these maps exhibit structured spatial patterns\, suggesting that common organizing principles shape the topographic distribution of biological features across the cortex. To better understand the underlying forces shaping brain organization\, we quantified the homophily - the propensity for brain regions proximal in physical\, connectivity\, or biological spaces to exhibit similar properties – of 43 brain maps [2]\, introduce a generative framework that preserves empirical homophilic structures\, then use it to identify patterns of unexplained variation and build biologically rich null models.&nbsp\;\n\nMethods\nHomophily was quantified using Moran’s I [3]\, with respect to six inter-regional relationship matrices capturing geodesic proximity\, structural and functional connectivity\, as well as laminar\, receptor\, and genetic similarity. We then developed a generative model preserving the multimodal homophilic structure of empirical maps. Starting from random initial conditions\, simulated annealing iteratively permuted regional values to minimize differences in Moran’s I across all modalities simultaneously (Fig. 1a). We generated 500 surrogate maps for each empirical map to quantify reconstruction accuracy and estimate the unique contribution of each modality. Residuals were also analyzed to identify patterns of unexplained variation.\n\nResults\nHomophily varied markedly across brain maps. Most maps were more strongly aligned with receptor and transcriptomic similarity than with geodesic proximity. Surrogate maps generated by preserving multimodal homophily accurately reproduced empirical topographies (Fig. 1b)\, with reconstruction accuracy strongly related to overall homophily (r=0.94). Leave-one-out analyses identified receptor similarity as the largest unique contributor\, followed by gene similarity and functional connectivity. Residual analyses revealed four reproducible axes of unexplained variation\, suggesting the existence of additional biological and methodological influences not captured by the modeled constraints.\n\nDiscussion\nWe show that homophily provides a unifying framework for understanding whole-brain topographies. Brain maps were more strongly aligned with receptor and transcriptomic similarity than with geodesic proximity\, indicating that biological similarity capture aspects of cortical organization that cannot be accounted for by geometry alone. By preserving multimodal homophilic structures\, our generative model accurately reconstructed empirical maps and exposed reproducible residual patterns that may reflect additional organizational principles or methodological influences. More broadly\, this framework enables the creation of biologically-informed surrogate models\, providing a powerful tool for hypothesis testing in neuroscience.\n\nFigure 1.&nbsp\;(a) The generative model relies on simulated annealing to randomly permute values while minimizing the difference in autocorrelation between empirical and simulated maps. (b) Morphospace summarizing the topographic properties of the empirical and simulated maps. (c) We identified the four main axes of variance in a matrix of regional difference between simulated and empirical values.​References\n[1]&nbsp\;Hansen\, J. Y.\, & Misic\, B. (2025). Integrating and interpreting brain maps. Trends in Neurosciences\, 48&nbsp\;(8): 594–607.\n[2] Markello\, R. D.\, Hansen\, J. Y.\, Liu\, Z.-Q.\, Bazinet\, V.\, Shafiei\, G.\, Suárez\, L. E.\, Blostein\, N.\, Seidlitz\, J.\, Baillet\, S.\, Satterthwaite\, T. D.\, Chakravarty\, M. M.\, Raznahan\, A.\, & Misic\, B. (2022). neuromaps: structural and functional interpretation of brain maps. Nature Methods\, 19&nbsp\;(11): 1472–1479.\n[3]&nbsp\;Moran\, P. A. P.&nbsp\;(1950). Notes on Continuous Stochastic Phenomena.&nbsp\;Biometrika.&nbsp\;37&nbsp\;(1):&nbsp\;17–23.\n\nAcknowledgement\nWe thank Justine Y. Hansen\, Eric. G. Ceballos\, Yigu Zhou\, Asa Farahani\, Tahmineh Taheri and Moohebat Pourmajidian for their comments and suggestions.\n\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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URL:http://cns2026.sched.com/event/854c6add0e1fe2cf8acb7a1f3cc5ea6d
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SUMMARY:P078: Identifying Neural Markers of Chronic Pain in Children with Cerebral Palsy Using Electroencephalography and Machine Learning
DESCRIPTION:Introduction\nCerebral palsy (CP) is the most common childhood motor disability\, with a prevalence of 1.6 per 1000 births worldwide [1]. A common symptom of CP is chronic pain\, with 76% of children experiencing pain\, and 33% experiencing chronic pain [2]. Existing pain assessment tools rely on self- or proxy-reporting\, limiting their utility for children with communication or cognitive impairments [3]. Electroencephalography (EEG) offers a non-invasive and objective alternative by identifying neural biomarkers associated with pain [4]\, [5]\, [6]. This study aims to develop and evaluate machine learning models for classifying pain intensity in children with CP using EEG data.\n\n\nMethods\nTen children with cerebral palsy and chronic pain\, along with ten age-matched healthy controls\, will undergo EEG recording during a hamstring stretching protocol administered by a research physiotherapist [6]. Measurements will occur in three conditions including rest\, non-painful\, and painful stretching. The intensity of pain will be continuously monitored and measured by either the Visual Analog Scale (VAS) for verbal participants\, or the Faces Pain Scale – Revised (FPS-R) for non-verbal participants. The EEG data will then be processed\, and power spectral density will be computed across all frequency bands. A support vector machine\, and two deep-learning models will be evaluated on their ability to accurately classify pain EEG signals.\n\nResults\nWe hypothesize that children with CP will exhibit increased theta and alpha power in the somatosensory and frontal cortices during painful stretching\, in accordance with previous literature\, while controls will exhibit the opposite pattern [7]. Chronic pain may also alter ERP components such as N100 and P300\, reflecting abnormal cognitive processing\, with greater modulations in individuals with CP due to sensorimotor impairments [7]. For classification\, SVM is expected to provide strong baseline performance\, while deep learning models are anticipated to outperform SVM across accuracy\, sensitivity\, specificity\, and F1 score by capturing more nuanced frequency-specific pain patterns.\n\n\nDiscussion\nThis research holds important clinical relevance\, particularly for children with CP who have been historically underrepresented in pain assessment research due to communication challenges and the subjective nature of traditional pain assessment methods. By developing objective\, EEG-based biomarkers for pain detection and intensity classification\, this research will fill a critical gap in pediatric pain management. Accurate identification of pain could lead to more personalized and effective treatment strategies. Ultimately\, this research may inform the development of tools that could be integrated into clinical settings to support clinicians in making faster data-driven decisions about pain interventions\, thus improving quality of life.\n\n\nReferences\nRosenbaum\, P.\, et al. (2007). Developmental Medicine and Child Neurology. Supplement\, 109\, 8–14.Harvey\, A.\, et al. (2024). BMC Medicine\, 22(1)\, 238. https://doi.org/10.1186/s12916-024-03458-0Shauna Kingsnorth\, et al. (2018). https://hollandbloorview.ca/research-education/knowledge-translation-products/chronic-pain-assessment-toolbox-childrenRockholt\, M. M.\, et al. (2023). Frontiers in Neuroscience\, 17\, 1186418. https://doi.org/10.3389/fnins.2023.1186418\nChmiel\, J.\, et al. (2025).&nbsp\;Journal of Clinical Medicine\, 14(16)\, 5902. https://doi.org/10.3390/jcm14165902Sabater-Gárriz\, Á.\, et al. (2024).&nbsp\;Research in Developmental Disabilities\, 150\, 104760. https://doi.org/10.1016/j.ridd.2024.104760dos Santos Pinheiro\, E. S.\, et al. (2016). http://hdl.handle.net/20.500.12105/20252\n\nAcknowledgement\nN/A\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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URL:http://cns2026.sched.com/event/2153abb2e67809d09a10126b85dda615
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SUMMARY:P079: Competitive dynamics in a biophysical model of rat somatosensory cortex
DESCRIPTION:Introduction\nThe neocortex not only has the ability to represent stimuli\, but it also needs to be able to categorize them for fast and efficient processing. Research has shown discrete representation in the primary auditory cortex when presented with ambiguous stimuli [1]. We hypothesize that this mutually exclusive dynamic is possible through competitive interaction between different neuronal assemblies representing the stimuli\, mediated by inhibition to opposing assemblies via Martinotti cells (MC).\n\n\nMethods\nThis study employs an existing\, experimentally-valided\, large-scale biophysical model of the non barrel primary somatosensory cortex (nbS1) of juvenile rates [2\,3]. This level of detail allows for a manipulation of the connectome to mimic different hypotheses for how learning could affect the connectivity between different neuronal populations. Based on a previous method\, the circuit presented with “pure” patterns to identify assemblies then ambiguous patterns generated from interpolation [4]. Different modifications are done to the circuit like the removal of connections between different populations of neurons\, allowing for a study into how these modifications change MC’s ability to inhibit different excitatory populations.\n\n\nResults\nWhen presented with the interpolated patterns\, the unmodified and naïve circuit followed it while displaying a transitional representation. However\, the modified circuit with changes to connection of MC also exhibited the same behavior. This is unexpected behavior which prompted further experiments to see how competitive dynamics can be achieved in this circuit.\n\n\nDiscussion\nOur results suggest further experimentation and a possible revision of our hypothesis. We would like to further analyze the role of top-down projections\, VIP+ neurons\, and hypothetical changes to synapses due to plasticity in process of learning to categorize.\n\n\nReferences\n\n\nBathellier\, B.\, Ushakova\, L.\, & Rumpel\, S. (2012). Discrete neocortical dynamics predict behavioral categorization of sounds. Neuron\, 76(2)\, 435–449. https://doi.org/10.1016/j.neuron.2012.07.00\nReimann\, M. W.\, … Ramaswamy\, S. (2026). Modeling and simulation of neocortical micro- and mesocircuitry (Part I\, anatomy). eLife\, 13\, RP99688. https://doi.org/10.7554/eLife.99688\nIsbister\, J. B.\, … Reimann\, M. W. (2026). Modeling and simulation of neocortical micro- and mesocircuitry (Part II\, Physiology and experimentation). eLife\, 13\, RP99693. https://doi.org/10.7554/eLife.99693\nEcker\, A.\, ...\, Reimann\, M. W. (2024). Cortical cell assemblies and their underlying connectivity: An in silico study. PLoS Computational Biology\, 20(3)\, e1011891. https://doi.org/10.1371/journal.pcbi.1011891\n\n\n\nAcknowledgement\nThis research project is supported by funding from the Fondation Courtois\, NSERC\, IVADO\, the CHU Sainte-Justine Research Center\,&nbsp\; FRQS\, the Canada CIFAR AI Chairs Program\, Mila\, and Google. Their compute infrastructure was supported through a grant from the Canada Foundation for Innovation (John Evans Leader Fund)\, and a grant of computing time awarded from the Digital Research Alliance of Canada.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:67ffcbda7afa786a1a10716df8f5f340
URL:http://cns2026.sched.com/event/67ffcbda7afa786a1a10716df8f5f340
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SUMMARY:P080: The Synapse-Pairing Tradeoff: How Clustering\, Bursts\, and Dendritic Location Enable Robust Plasticity In-Vivo
DESCRIPTION:Introduction\nCortical representations are thought to arise from stable network motifs formed by neuronal assemblies\, with synaptic plasticity between pyramidal cells (PCs) playing a central role in their formation\, competition\, and maintenance. While rules governing such synaptic changes have been well characterized in slice conditions\, their implications for learning in awake behaving animals remain an active area of research. Here we use biophysically detailed simulations with calibrated ion channels\, background synaptic activity\, and calcium-based plasticity rules to investigate mechanisms enabling reliable plasticity in-vivo. We find that spatially clustered activation and burst firing offer robust pathways for LTP under physiological conditions.\n\n\nMethods\nWe used biophysically detailed simulations of a large-scale in-silico cortical microcircuit of rat somatosensory cortex with a calcium-based plasticity model capturing LTP and Long-Term Depression (LTD) as a function of integrated postsynaptic calcium. We parameterized voltage-gated Na⁺\, K⁺\, Ca²⁺\, and Bk channels throughout the dendritic tree based on experimental data. To reproduce the high-conductance state of awake cortex\, we incorporated stochastic background activity using Dendritic Extra-Excitatory Synapses (DEES) at 1.1 synapses/μm. We investigated clustered plasticity in L2/3 PC and L5-TTPC basal and apical dendrites under both in-vitro and in-vivo-like extracellular calcium concentrations.\n\n\nResults\nSynchronous activation of ≥11 clustered synapses generates dendritic plateau potentials (≥100 ms) that induce LTP in distal basal dendrites (Fig. 1). We identify a synapse-pairing tradeoff\, where dendrites effectively trade the number of co-activated synapses for pairing repetitions: 16-synapse clusters achieve one-shot learning\, while 8-synapse clusters require 3+ pairings. This tradeoff exhibits spatial gradients: distal dendrites achieve LTP independent of backpropagating action potentials\, while proximal clusters require spike-timing coincidence. When multiple basal clusters coactivate\, summated depolarizations trigger somatic bursts\; both presynaptic and postsynaptic bursts drive robust LTP with all-or-none threshold dynamics.\n \n\nDiscussion\nThese findings establish multiple plasticity mechanisms within a single neuron—spatial clustering\, location-dependent learning modes\, and dual burst pathways—providing testable predictions for how cortical circuits implement flexible\, hierarchical learning. Distal dendrites enable unsupervised learning via cluster-based LTP independent of bAPs\, while proximal regions implement supervised learning requiring spike-timing coincidence. Apical dendrites receiving top-down signals can generate bursts or couple with somatic spikes via backpropagation-activated calcium (BAC) firing\, a substrate for top-down plasticity modulation. These mechanisms reveal how dendrites trade synapse number for pairing repetitions under noisy physiological conditions.\n\n\nReferences\n1.&nbsp\;Chindemi\, G.\, Abdellah\, M.\, Amsalem\, O.\, Benavides-Piccione\, R.\, Delattre\, V.\, Doron\, M.\, Ecker\, A.\, Jaquier\, A. T.\, King\, J.\, Kumbhar\, P.\, Monney\, C.\, Perin\, R.\, Rössert\, C.\, Tuncel\, A. M.\, Van Geit\, W.\, DeFelipe\, J.\, Graupner\, M.\, Segev\, I.\, Markram\, H.\, & Muller\, E. B. (2022). A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex.\n2.&nbsp\;Ecker\, A.\, Egas Santander\, D.\, Abdellah\, M.\, Alonso\, J. B.\, Bolaños-Puchet\, S.\, Chindemi\, G.\, Gowri Mariyappan\, D. P.\, Isbister\, J. B.\, King\, J.\, Kumbhar\, P.\, Magkanaris\, I.\, Muller\, E. B.\, & Reimann\, M. W. (2025). Assemblies\, synapse clustering\, and network topology interact with plasticity to explain structure-function relationships of the cortical connectome.\n\nAcknowledgement\nThis research project is supported by funding from the Fondation Courtois\, NSERC\, IVADO\, the CHU Sainte-Justine Research Center\, FRQS\, the Canada CIFAR AI Chairs Program\, Mila\, and Google. Their compute infrastructure was supported through a grant from the Canada Foundation for Innovation (John Evans Leader Fund)\, and a grant of computing time awarded from the Digital Research Alliance of Canada.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:f897fc04c30a994da8978bcb680737b9
URL:http://cns2026.sched.com/event/f897fc04c30a994da8978bcb680737b9
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SUMMARY:P081: Estimating spiking activity of cerebellar projections to the substantia nigra dopaminergic neurons during a Pavlovian task
DESCRIPTION:Introduction\nImaging using GECIs is a common technique utilized to measure neuronal activity [1]. This method\, however\, provides a proxy for neuronal activity and extracting the spiking activity from the observed fluorescence remains an open problem [2]. We utilize a set of differential equations to infer the underlying spike train from fluorescence recordings done in mice.\n\n\nMethods\nWe started from the model for the calcium concentration (c) and fluorescence (p) to estimate the underlying spike trains [3]. We focused on signals averaged over many trials and ignored Brownian noise and baseline fluorescence. The model assumed linear dynamics for c that decays exponentially\, but each spike increases c by a fixed fraction. The dynamics of p is a nonlinear function of c with parameters specific to the indicator used. A key parameter is the fluorescent saturation ɣ. It is possible to calculate c(t) by inverting the equations of p. To do so requires that the saturation parameter ɣ is small enough that 1- ɣp is bounded away from 0. From c\, the spike train S(t) can be calculated by inverting the linear differential equation.\n\n\nResults\nThe spike train estimation method was first optimized with ground-truth data simulated across trials and averaged. This method successfully reconstructed c and S(t) from p. We then applied the method to simultaneous recordings\, using fiber photometry\, of neurons of the deep cerebellar nuclei (DCN) projecting to the substantia nigra pars compacta (SNc) and dopamine neurons in the SNc\, in mice performing a simple Pavlovian task [4]. The obtained spike rates were compared to signals obtained from the licking rate of the animals and a rate model of the DCN neurons\, in order to estimate how the firing rates are modulated by reward value and sensory stimuli.\n\n\nDiscussion\nGECI signals provide a proxy for neural activity\, but building mechanistic models that represent these signals requires neural activity rates underlying the fluorescent signals to be properly estimated. This is especially useful when the baseline and maximum rates of neural activity in the recorded regions is known and\, therefore\, the changes in activity due to sensory inputs\, movement and extrinsic modulatory signals can be explored using meso-scale mechanistic models. We will use these results to compare different circuit motifs that include distinct feedforward and feedback connections in order to test hypotheses for the role of cerebellum inputs to the midbrain dopamine centers.\n\n\nReferences\n1. Dana H\, et al. (2019). High-performance calcium sensors for imaging activity in neuronal populations and microcompartments. Nature methods\, 16(7)\, 649–657. doi:10.1038/s41592-019-0435-6\n2. Rupprecht P\, et al. (2025). Spike rate inference from mouse spinal cord calcium imaging data. bioRxiv : the preprint server for biology\, 2024.07.17.603957. doi:10.1101/2024.07.17.603957\n3. Deneux T\, et al. (2016). Accurate spike estimation from noisy calcium signals for ultrafast three-dimensional imaging of large neuronal populations in vivo. Nature communications\, 7\, 12190. doi:10.1038/ncomms12190\n4. Washburn S\, et al. (2024). The cerebellum directly modulates the substantia nigra dopaminergic activity. Nature neuroscience\, 27(3)\, 497–513. doi:10.1038/s41593-023-01560-9 \n\nAcknowledgement\nThis work was conducted at New Jersey Institute of Technology using data collected at Albert Einstein College of Medicine. Financial support was provided by NIH MH060605 (FN) and NSF IOS-2002863 (HGR).\n\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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URL:http://cns2026.sched.com/event/ca793de1cdeea12f02bf0799dd4271ae
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SUMMARY:P082: Balancing stability and flexibility: a meta-learning algorithm for behavioral adaptation in mice
DESCRIPTION:Introduction\nLearning through trial and error (reinforcement learning\, RL) enables animals to adapt their behavior\nin dynamic environments. Updating behavior based on reward prediction errors requires balancing stability\nand flexibility: learners should avoid overreacting to noise while remaining sensitive to genuine environ-\nmental changes [1]. Here\, we investigated how mice adjust their learning parameters under uncertainty and\ndeveloped a meta-reinforcement learning framework to account for this adaptation [2].\n\nMethods\nWe manipulated two sources of environmental uncertainty: reward probabilities\, which determine out-\ncome stochasticity\, and the frequency of contingency changes\, which determines volatility [3]. We first de-\nrived theoretical predictions from a standard RL model by systematically varying stochasticity and volatility\nto identify reward-maximizing parameter values. We then compared these predictions with mouse behavior\nin a binary operant task using intracranial self-stimulation [4]\, ensuring stable motivation across animals\,\nsessions\, and thousands of trials. Behavioral data were fitted with a classical RL model and used to constrain\na meta-learning procedure.\n\nResults\nSimulations predicted that the optimal learning rate should increase with environmental volatility but\ndecrease with stochasticity\, while the optimal decision parameter (exploitation/exploration trade-off) should\ndecrease with both factors. Consistent with these predictions\, fitted learning rates in mice varied with both\nvolatility and stochasticity. In contrast\, decision parameter remained stable across conditions.\n\n\nTo account for these results\, we developed a meta-RL model in which mice estimate stochasticity from\nreward prediction errors and track volatility using a simple heuristic inspired by inference models. This\nmodel provided the best explanation of behavioral data.\n\nDiscussion\nTogether\, these results indicate that mice dynamically adjust learning rates in response to environmental\nuncertainty using computationally simple estimates of volatility and stochasticity. This framework provides\na tractable approach for investigating the neural mechanisms underlying adaptive learning\n\nReferences\n[1] Kenji Doya. Modulators of decision making. Nature neuroscience\, 11(4):410–416\, 2008.\n[2] Nathaniel D Daw\, Yael Niv\, and Peter Dayan. Uncertainty-based competition between prefrontal and\ndorsolateral striatal systems for behavioral control. Nature neuroscience\, 8(12):1704–1711\, 2005.\n[3] P Piray and ND Daw. A model for learning based on the joint estimation of stochasticity and volatility.\nNature Communication\, 1(12):6587\, 2021.\n[4] William A Carlezon Jr and Elena H Chartoff. Intracranial self-stimulation (icss) in rodents to study the\nneurobiology of motivation. Nature protocols\, 2(11):2987–2995\, 2007\n\nAcknowledgement\nThe authors acknowledge the support of La Fondation pour la Recherche Médicale. We also&nbsp\;thank Jacques Gautrais (CBI\, Toulouse) for his valuable advice and discussions regarding analysis codes.\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:03cb0978315458c741a38f9e6a984db6
URL:http://cns2026.sched.com/event/03cb0978315458c741a38f9e6a984db6
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SUMMARY:P083: Biophysical model of auditory thalamocortical circuit reveals GABAB-dependent control of N1 deficits in Schizophrenia
DESCRIPTION:Introduction\nAuditory processing deficits are a core feature of schizophrenia (SZ). The N1 component of the auditory evoked potential (AEP) is reduced in SZ. N1 refractory curves describe increasing N1 amplitudes with longer inter-stimulus intervals (ISIs)\, suggesting dependence on slow synaptic mechanisms\, including GABAB and NMDA receptors. Using a biophysical model of macaque primary auditory cortex (A1) and thalamus [1]\, we examine how GABAB and NMDA modulation shape N1 dynamics. We further test whether GABAB modulation can counteract N1 amplitude reductions under NMDA hypofunction. Our goal is to reproduce in-vivo N1 deficits and identify circuit mechanisms relevant to SZ.\n\n\nMethods\nSimulations were performed using the NEURON simulation environment and NetPyNE multiscale modeling package [2\,3]. The model includes medial geniculate nucleus (MGN)\, thalamic reticular nucleus (TRN)\, and primary auditory cortex (A1). A1 is represented as a cortical column with over 12\,000 neurons and ~25 million synapses. The model captures multiscale activity\, including laminar local field potentials (LFPs)\, current source density (CSD\; second spatial derivative of LFP)\, and neuronal firing rates. Auditory stimuli are modeled as punctate thalamic inputs to core and matrix pathways. Simulated LFPs are used to derive CSD\, and resulting patterns are compared with in-vivo macaque data for validation.\n\n\nResults\nBrief thalamic stimulation evoked CSD sink events in A1 granular layers that closely matched in-vivo macaque responses. The model reproduced the N1 refractory curve\, with event-related CSD amplitude and multi-unit activity increasing with longer inter-stimulus intervals (ISI). This relationship was strongly governed by GABAB conductance: increasing GABAB (+25%) reduced N1 amplitude across layers\, most prominently in supragranular and infragranular groups\, while decreasing GABAB (-25%) enhanced N1 responses. In contrast\, NMDA conductance modulation (+/-25%) produced comparatively modest effects\, suggesting weaker sensitivity under current conditions.\n\n\nDiscussion\nWe investigated N1 refractory dynamics by examining inter-stimulus interval dependence of GABAB conductance in the A1 model. Increased GABAB reduced N1 amplitude across layers\, suggesting a potential contributory role in auditory processing deficits observed in SZ. NMDA conductance modulation produced comparatively modest effects on N1 under current conditions. Notably\, reducing GABAB-enhanced N1 responses\, indicating a potential compensatory mechanism for N1 reductions associated with NMDA channel hypofunction. The model reproduces key in-vivo N1 dynamics and provides a framework for probing circuit mechanisms. Future work will extend this approach to more complex responses\, including mismatch negativity\, which is disrupted in SZ.\n\n\nReferences\n\n1. Dura-Bernal\, S.\, et al. (2023). Data-driven multiscale model of macaque auditory thalamocortical circuits.&nbsp\;Cell Reports\, 42(11)\, 113378.\n\n2. Dura-Bernal\, S.\, et al. (2019). NetPyNE\, a tool for data-driven multiscale modeling of brain circuits.&nbsp\;eLife\, 8\, e44494.3. Hines\, M. L.\, & Carnevale\, N. T. (2001). NEURON: A tool for neuroscientists.&nbsp\;Neuroscientist\, 7(2)\, 123–135.\n\nAcknowledgement\nResearch supported by NIH R01DC019979\,&nbsp\; NIH R01DC012947\,&nbsp\; NIH R01NS128924-01\, NIH R01MH134118-01\, NIH P50MH109429\, ARL Cooperative Agreement W911NF2220143\n\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:72445d6dd55d910bdc27cc97b37f5dbd
URL:http://cns2026.sched.com/event/72445d6dd55d910bdc27cc97b37f5dbd
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SUMMARY:P084: Mapping excitation-inhibition balance in schizophrenia with white-matter-microstructure informed modeling
DESCRIPTION:Introduction\nProviding early diagnosis and personalized treatment for psychiatric disorders like schizophrenia remains challenging\, due to important interpersonal differences and still elusive neuronal mechanisms. Whole-brain network models show promising results with clinical relevance for individualized treatment recommendations in neurological disorders. However\, their applicability to psychiatry is still limited as models fail to account for inter-individual differences in the correlation structure of brain dynamics among psychiatric patients.\n\n\nMethods\nWhat physiological mechanisms should models incorporate to better account for individual profiles of brain dynamics in schizophrenia patients and healthy controls? Our study compares various metrics of white matter structure and microstructure to inform connection weights between regions. To do so\, we inferred regional parameters of whole-brain mean-field models with The Virtual Brain simulator (Pille et al\, 2025 bioRxiv) to account for empirical functional connectivity from resting-state functional magnetic resonance imaging of schizophrenia patients and healthy controls (2).\n\n\nResults\nWe found that using global fractional anisotropy or apparent diffusion coefficient of white matter fibers to inform the weights in neural mass models can drastically improve model performance. The data-model correlations of simulated and empirical data were significantly improved (from 0.2 to 0.7) over state-of-the-art methods. This approach allows us to uncover personalized maps of excitation-inhibition imbalance\, hypothesized to take place in schizophrenia. These maps prove meaningful in that they can predict diagnosis better than model-independent neuroimaging benchmarks.\n\n\nDiscussion\nOur findings highlight the importance of white matter microstructure in whole-brain modeling. The findings provide a fundamentally novel bridge between cellular-scale E/I imbalance mechanisms hypothesized in schizophrenia and large-scale brain network dynamics associated with well-established biomarkers of the disorder. Personalized white-matter microstructure informed whole-brain models could therefore be relevant as platforms to simulate disorder progression for early diagnosis and to test and optimize intervention protocols toward individualized treatment recommendations.\n\n\nReferences\nPille\, M.\, Martin\, L.\, Richter\, E.\, Perdikis\, D.\, Schirner\, M.\, & Ritter\, P. (2025). Fast and easy whole-brain network model parameter estimation with automatic differentiation.&nbsp\;bioRxiv\, 2025-11.\nVohryzek\, J.\, Aleman-Gomez\, Y.\, Griffa\, A.\, Raoul\, J.\, Cleusix\, M.\, Baumann\, P. S.\, ... & Hagmann\, P. (2020). Structural and functional connectomes from 27 schizophrenic patients and 27 matched healthy adults [Data set]. Zenodo.&nbsp\;https://doi.org/10.5281/zenodo.3758534\n\nAcknowledgement\nThis project was supported by the Hertie Foundation.\n\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:2f86bc2da7dcf7374b0cd342e1298f51
URL:http://cns2026.sched.com/event/2f86bc2da7dcf7374b0cd342e1298f51
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SUMMARY:P085: Effect of visual distortion on the perception of straight lines and reaching trajectories
DESCRIPTION:Introduction\nThe crystalline lens or eyeglasses induce spatial distortion of images on the retina\, and the nervous system itself can introduce perceptual distortion. A previous study showed a correlation between perceptual distortion and the curvature of hand trajectories during reaching movements [1]. This suggests that perceptual distortion affects motor planning. On the other hand\, adaptation to image skew changes the perception of unskewed geometrical patterns [2]. Similarly\, barrel distortion\, a type of lens distortion\, might change the internal representation to perceive external straight lines as straight. We tested whether this distortion changes line perception and whether the internal representation affects motor planning of hand trajectories.\n\nMethods\nSeven participants performed an adaptation task in which an image of a grid with barrel distortion was displayed on a head-mounted display. During the task\, participants were required to move their heads and gaze in multiple directions. Pre- and post-adaptation changes were evaluated using two tasks: (1) a curvature discrimination task to measure the point of subjective equality (PSE) by having participants judge the convexity (upward or downward) of lines presented in the upper or lower visual field\, and (2) a reaching task measuring hand trajectory curvature during straight hand movements toward targets in either visual field. The study protocol was approved by the Institutional Review Board at Yamaguchi University.\n\nResults\nCompared with the pre-adaptation test\, the mean PSE in the post-adaptation test shifted in a direction consistent with the hypothesis that adaptation to barrel distortion leads to the perception of more outwardly curved lines as straight\, in both the upper and lower visual fields. This shift was statistically significant in the upper visual field (t(X) = −2.99\, p = 0.024\, Fig. 1)\, and below the significance level in the lower visual field (t(X) = 0.59\, p = 0.58). Regarding the reaching task\, the initial movement direction did not change significantly across any conditions\, regardless of the movement direction (leftward or rightward) or visual field (upper or lower).\n\nDiscussion\nThe PSE shift supports the hypothesis that the internal representation of line is acquired through perceptual learning. This implies that the concept of a straight lines might also be acquired as the brain constructs a consistent representation from images that appear in various shapes depending on their retinal positions.\n\nNo observed change in the initial hand direction in reaching movements indicates that the internal spatial representation based on visual stimuli does not directly affect motor control\, implying that perceptual spatial representations differ from those used in motor planning of hand trajectories.\n\nFigure 1.&nbsp\;Mean PSE curvature across participants in pre- and post-adaptation curvature discrimination tests. Error bars represent the standard deviation. Positive and negative values indicate an upward- and downward-convex curves\, respectively. Gray dots represent individual participant data.\n\nReferences\nWolpert\, D. M.\, Ghahramani\, Z.\, & Jordan\, M. I. (1995). Are arm trajectories planned in kinematic or dynamic coordinates? An adaptation study.&nbsp\;Experimental brain research\,&nbsp\;103(3)\, 460-470.&nbsp\;https://doi.org/10.1007/BF00241505Habtegiorgis\, S. W.\, Rifai\, K.\, Lappe\, M.\, & Wahl\, S. (2017). Adaptation to skew distortions of natural scenes and retinal specificity of its aftereffects.&nbsp\;Frontiers in Psychology\,&nbsp\;8\, 1158. https://doi.org/10.3389/fpsyg.2017.01158\nAcknowledgement\nThis work was supported by the Sasakawa Scientific Research Grant from The Japan Science Society and by AMED under Grant Number JP26wm0625418h0002.\n\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:3f2c68502e43111a7b6d688a5088adfb
URL:http://cns2026.sched.com/event/3f2c68502e43111a7b6d688a5088adfb
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SUMMARY:P086: Examining mnemonic discrimination performance in a hippocampus model using the mnemonic similarity task
DESCRIPTION:Introduction\nMnemonic Discrimination (MD) refers to the ability to distinguish novel stimuli from similar memories [1]. It is hypothesized to involve dentate gyrus (DG) pattern separation (PS) [1]\, which is impaired by the hyperactivity of DG granule cells (DGGCs). DGGC hyperactivity has been found in bipolar disorder [2]\; such hyperactivity may subsequently impair MD. It is unclear whether DG PS is involved in both the encoding and retrieval phases of MD\, or solely during encoding [3]. This distinction is important because it may clarify how changes in DGGC activity affects MD performance.\n\n\nMethods\nTo address this gap\, we developed a computational model of the hippocampus capable of executing the gold standard MD task for humans\, the Mnemonic Similarity Task (MST) [1]. Our model simulates the ventral visual stream and entorhinal cortex via pre-trained ResNet-derived representations. It simulates DG pattern separation via k-winner-take-all dynamics\, and a continuous log-sum-exp modern Hopfield network simulates the CA3’s autoassociative behaviour. Subsequently\, our model’s MD performance was compared between retrieval conditions where the DG was active vs. inactive. Finally\, a mediation analysis was conducted to examine if the relationship between DG excitability and MD is mediated via DG pattern separation.\n\n\nResults\nOur preliminary findings suggest that the DG is active during both encoding and retrieval\, as these models exhibited better MD performance than those with the DG only active during encoding. Additionally\, the mediation analysis indicated that MD performance is significantly partially mediated by DG PS. The proportion mediated ranged from 0.42 to 0.46. Total effects were also statistically significant\, with coefficients between -0.80 and -0.87\, indicating that\, in our model\, DG hyperexcitability impairs MD performance.\n\n\nDiscussion\nWe present a computational model of the hippocampus capable of simulating the MST. By demonstrating that PS partially mediates the relationship between DG excitability and MD performance\, we therefore present a candidate mechanistic explanation for memory impairments seen in people with BD. A potential direction for future research is to explore why PS did not fully mediate MD performance. Alternatively\, future studies can fit model parameters to behavioural data at the individual level\, deepening our understanding of individual differences in hippocampal functioning.\n\n\nReferences\n1.\tStark\, S. M.\, Kirwan\, C. B.\, & Stark\, C. E. L. (2019). Mnemonic Similarity Task: A Tool for Assessing Hippocampal Integrity. Trends in Cognitive Sciences\, 23(11)\, 938–951.\n2.\tBakker\, A.\, Krauss\, G. L.\, Albert\, M. S.\, Speck\, C. L.\, Jones\, L. R.\, Stark\, C. E.\, Yassa\, M. A.\, Bassett\, S. S.\, Shelton\, A. L.\, & Gallagher\, M. (2012). Reduction of hippocampal hyperactivity improves cognition in amnestic mild cognitive impairment. Neuron\, 74(3)\, 467–474.\n3.\tBernier\, B. E.\, Lacagnina\, A. F.\, Ayoub\, A.\, Shue\, F.\, Zemelman\, B. V.\, Krasne\, F. B.\, & Drew\, M. R. (2017). Dentate Gyrus Contributes to Retrieval as well as Encoding: Evidence from Context Fear Conditioning\, Recall\, and Extinction. The Journal of Neuroscience\, 37(26)\, 6359–6371.\n\nAcknowledgement\n&nbsp\;\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:722d20a80ebe12d10354386873439918
URL:http://cns2026.sched.com/event/722d20a80ebe12d10354386873439918
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SUMMARY:P087: High‑Order Interactions Predict the Dimensionality of Recurrent Hidden Dynamics Across Cognitive Tasks
DESCRIPTION:Introduction\nA central challenge in neuroscience is to understand how collective computations arise from the coordinated activity of many interacting units. High order interactions (HOIs)—statistical dependencies not reducible to pairwise relations—offer a principled way to quantify such emergent structure. Yet\, the mechanisms that generate HOIs and their relationship to the geometry of population dynamics remain poorly understood. Here\, we study how HOIs self organize in recurrent neural networks (RNNs) trained on cognitive tasks of varying complexity\, and we identify a general link between informational structure and the dimensionality of the underlying dynamical trajectories.\n\n\nMethods\nContinuous‑time RNNs were trained on four tasks spanning a range of cognitive demands: Go/NoGo\, Negative Patterning\, Temporal Discrimination\, and Context‑dependent Decision Making. After training\, networks were probed with long sequences of noise or task‑related inputs to characterize their intrinsic dynamics. HOIs were quantified using O‑information and S‑information (KSG estimator\, JIDT implementation (1)) across all combinations of 3–8 hidden units. The nonlinear dimensionality of the hidden‑state trajectory was quantified using correlation dimension and complemented by PCA‑based variance analyses.\n\n\nResults\nTraining induced robust HOIs across tasks\, with simpler tasks producing predominantly redundant interactions and more complex tasks eliciting stronger synergistic structure. Crucially\, we found a systematic negative correlation between O‑information and the dimensionality of hidden‑state trajectories: networks with more synergy explored higher‑dimensional dynamical manifolds\, whereas networks dominated by redundancy collapsed onto lower‑dimensional regimes. This relationship was consistent across tasks\, input conditions\, and network realizations. Pruning procedures that sparsified the weight matrix did not disrupt the HOI–dimensionality link\n\n\nDiscussion\nOur results reveal a mechanistic coupling between informational structure and dynamical geometry in recurrent systems: synergy emerges when the network expands its accessible dynamical repertoire\, while redundancy reflects a contraction onto lower‑dimensional attractors. This suggests that O‑information can serve as a general marker of dynamical richness and computational flexibility in recurrent architectures. Because the relationship holds across tasks and network configurations\, it may reflect a broader organizational principle of recurrent computation. Future work will test whether this coupling persists in multitask settings\, under perturbations\, and in biologically inspired architectures.\n\n\nReferences\n(1)\tJoseph T. Lizier\, "JIDT: An information-theoretic toolkit for studying the dynamics of complex systems"\, Frontiers in Robotics and AI 1:11\, 2014\; doi:10.3389/frobt.2014.00011 (pre-print: arXiv:1408.3270)\n\n\nAcknowledgement\nThis work is funded by Fondecyt grant 1241469 (ANID\, Chile). AC3E is funded by Basal grant AFB240002 (ANID\, Chile)\n\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:2775fe337cddaa5ebeddc20f6b52e886
URL:http://cns2026.sched.com/event/2775fe337cddaa5ebeddc20f6b52e886
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SUMMARY:P088: From Pixels to Percepts: Understanding Texture Discrimination in the Mouse Visual Cortex
DESCRIPTION:Introduction\nVisual textures\, like blades of grass or bark on a tree\, are pervasive in the natural world. These patterns\, characterized by statistical regularities across spatial scales\, help animals navigate the world and categorize their surroundings[1]. Textures are quite complex\, yet can be readily synthesized and parameterized by computational models\, hence they offer a useful entry point for studying visual processing at multiple levels: from the encoding of complex image statistics to the formation of invariant representations [2]. However\, the circuit-level implementation of these computations in the brain remains poorly understood.\n\n\nMethods\nAs part of the Openscope initiative\, we present a new open dataset [3\,4] consisting of simultaneous two-photon calcium imaging across four distinct regions of the mouse visual cortex and two imaging planes of mice engaged in a texture discrimination task. We investigate how different families of textures are processed before\, during and after learning a texture discrimination task. We examine how population level representations of different classes of textures are encoded within the visual cortex.\n\n\nResults\n\nResults suggest that internal representations of textures emerge during learning (particularly in layer 5 across visual areas V1\, LM\, and AL)\, and mirror behavioral discriminability\, with these encodings being high dimensional. Furthermore\, we find that these representations can be updated\, generalizing to a wide set of images as task complexity increases. Interestingly\, family-specific internal representations appear to be task-dependent\, as during passive viewing\, neural responses are more selective to individual images than&nbsp\; to families.\n\n\n\nDiscussion\nTogether\, these results suggest that texture category representations across visual cortical areas are not fixed\, but are dynamically regulated by task engagement. This is consistent with a top-down attentional mechanism impacting the encoding of naturalistic stimuli\, rather than being an innate property of the visual system. These findings highlight the importance of behavioral contexts in sculpting cortical population codes.\n\n\nReferences\n\n1.Li\, A.\, & Zaidi\, Q. (2000). Perception of three-dimensional shape from texture is based on patterns of oriented energy. Vision Research\, 40(2)\, 217–242. https://doi.org/10.1016/S0042-6989(99)00169-8\n2.Portilla\, J.\, & Simoncelli\, E. P. (2000). A parametric texture model based on joint statistics of complex wavelet coefficients. International Journal of Computer Vision\, 40(1)\, 49–70. https://doi.org/10.1023/A:1026553619983\n3.Ager\, K.\, Akella\, S.\, Bawany\, A.\, Bennett\, C.\, Dichter\, B.\, Ghosh\, S.\, . . . Williams\, A. (2024). The OpenScope Databook (v1.2.0) [Software]. Zenodo. https://doi.org/10.5281/zenodo.12614664\n4.DANDI:001461 [Dataset]. DANDI Archive. https://dandiarchive.org/dandiset/001461\n\n\nAcknowledgement\nThis work was funded by the US National Institutes of Health (NIH) U24NS113646. The imaging dataset was obtained as part of the OpenScope program\, which is operated by the Allen Institute / Neural Dynamics. We thank the OpenScope steering committee for their support\, the Allen Institute founders\, Paul G. And Jody Allen\, and Karel Svoboda\, for their&nbsp\;vision\, encouragement\, and support.\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:f40116b2253d579c262e7794bfabdee7
URL:http://cns2026.sched.com/event/f40116b2253d579c262e7794bfabdee7
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SUMMARY:P089: Biologically plausible Dopamine-Modulated STDP Model of Pavlovian Learning in Spiking Neural Networks
DESCRIPTION:Introduction\nDopamine-modulated STDP is a key implementation of the three-factor learning rule\, in which synaptic changes depend on pre- and postsynaptic activity and a modulatory signal. Izhikevich's model introduced an eligibility trace that enables delayed dopaminergic rewards to reinforce earlier neural activity\, supporting reward-based learning in recurrent spiking neural networks [1]. Previous studies showed that this framework promotes feedforward organization and spatiotemporal sequence encoding [2\,3]. However\, dopamine acts only as a gain factor\, without altering the STDP function shape. Here\, we introduce a modified rule that incorporates dopamine-dependent changes in the STDP window [4]\, yielding more biologically realistic learning behavior.\n\nMethods\nA recurrent spiking neural network of 2\,000 Izhikevich neurons (1\,600 excitatory\, 400 inhibitory) was organized into 100 overlapping stimulus subgroups. Synaptic connectivity was random (p = 0.1). Dopamine-modulated STDP was implemented using either the original Izhikevich rule or a modified rule with separate pre–post and post–pre eligibility traces and a saturating dopamine function. During 7\,000 s of training\, randomly selected subgroups received stimuli at random intervals\, while activation of subgroup S1 triggered delayed dopamine rewards. Learning was evaluated during a 200-s reward-free test phase using spike density function peaks and AUC-based stimulus discriminability.\n\nResults\nIn the original Izhikevich model\, reward-based learning remained robust across a broad range of dopamine concentrations\, with selective responses (AUC ≥ 0.9) maintained even at unrealistically high levels. In contrast\, the modified model exhibited an inverted-U dependence on dopamine concentration (Fig. 1). Learning emerged at low dopamine levels\, peaked at intermediate concentrations\, and deteriorated above ~2 μM\, where responses to rewarded and non-rewarded stimuli became indistinguishable. A narrow intermediate range (0.8–1.2 μM) displayed bistability-like behavior\, with identical dopamine levels producing either high- or low-performance states depending on network history and stochastic training dynamics.\n\n\nDiscussion\n\nUnlike the original Izhikevich model\, in which dopamine only scales synaptic plasticity\, the modified STDP rule allows dopamine to reshape the plasticity window. This produced an inverted-U relationship between dopamine concentration and learning performance\, restricting successful conditioning to a biologically plausible range. The model also exhibited a bistability-like regime\, where identical dopamine levels yielded different learning outcomes depending on network history. High dopamine concentrations impaired learning\, likely through excessive potentiation that disrupted feedforward organization. These findings provide a more biologically realistic framework for dopamine-dependent learning.\n\nFigure 1.&nbsp\;Learning performance as a function of dopamine reward concentration. Mean SDF peak responses during post-training testing are shown for the original Izhikevich model (black dashed line) and the modified model (red solid line)\, averaged over (N = 11) simulations\; error bars indicate SEM. Gray curves represent responses to non-rewarded stimuli. Green shading marks regions with AUC ≥ 0.9.​\n\nReferences\n&nbsp\;Izhikevich\, E. M. (2007). Solving the distal reward problem through linkage of STDP and dopamine signaling. Cerebral Cortex\, 17(10)\, 2443–2452.Jeong\, I. H. & Lee\, K. J. Bursting dynamics and network structural changes towards and away from a pavlovian-conditioned neural network. PLOS Complex Systems 2\, e0000035 (2025). Park\, W.\, Kim\, J.\, Jeong\, I.\, & Lee\, K. J. (2025). Temporal Pavlovian conditioning of a model spiking neural network for discrimination sequences of short time intervals. Journal of Computational Neuroscience\, 53(1)\, 163–179.Zhang\, J. C.\, Lau\, P. M.\, & Bi\, G. Q. (2009). Gain in sensitivity and loss in temporal contrast of STDP by dopaminergic modulation at hippocampal synapses. Proceedings of the National Academy of Sciences\, 106(31)\, 13028–13033.\nAcknowledgement\nThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2024-00335928).
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:2011f3da2dc88707332d2017ab55baee
URL:http://cns2026.sched.com/event/2011f3da2dc88707332d2017ab55baee
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DTEND:20260713T212000Z
SUMMARY:P090: Modeling and Analytical Characterization of Neuronal Networks Constructed from Reservoir Computing Based Model
DESCRIPTION:Introduction\nElectrophysiological studies in neuroscience probe interactions among neuronal populations across multiple scales\, from single-cell activity to large network dynamics [1\,2]. Here\, we present a computational framework to decode signals from microelectrode array (MEA) recordings [3]. The model is based on Reservoir Computing (RC) and learns spike-rate sequences to reproduce network responses to external stimuli. A key outcome is a macroscopic connectivity map capturing effective connectivity with higher accuracy than standard statistical methods such as cross-correlation and transfer entropy. We describe the model\, discuss its implications and limitations\, and present applications to cultured neuronal networks under different interventions.\n\n\nMethods\nThe approach relies on electrophysiological recordings from mouse cortical cultures acquired via microelectrode arrays (MEA). After preprocessing (filtering and spike detection)\, signals are converted into multichannel instantaneous spike-rate (ISR) sequences\, from which bursting episodes are extracted. These are used to train an artificial neural network with a reservoir computing (RC) architecture to learn the synaptic transmission function underlying rate-coded activity. The network is represented macroscopically\, with nodes corresponding to MEA electrodes. The RC reservoir performs nonlinear transformations with leaky memory\, and outputs are obtained via LASSO-regularized linear regression [4].\n\n\nResults\nModel validation followed two complementary approaches. First\, the inferred connectivity map was benchmarked against a ground-truth network generated in silico\, with simulations designed to replicate MEA measurements. Second\, both in silico and in vitro (real neuronal cultures) data were used in a predictive framework: the model was trained and validated on spontaneous activity\, while testing was performed using responses to controlled local stimuli\, including optogenetic perturbations. Model predictions under identical stimuli were then compared with the recorded responses.\nIn the presentation\, we will report the model’s performance and highlight selected applications along with their results.\n\nDiscussion\nIn this study\, we developed a computational model that decodes spatio-temporal data from electrophysiological measurements of neuronal cultures. The model reconstructs the network structure on a macroscopic domain and predicts the response to a localized stimulus. Our primary goal was to create an advanced experimental data analysis tool for processing complex time-series. The results obtained indicate that the model not only serves as a data analyzer but can also function as a network simulator.\n\n\nReferences\n[1]&nbsp\;Llinás\, R. R. (1988). The intrinsic electrophysiological properties of mammalian neurons: insights into central nervous system function.&nbsp\;Science\,&nbsp\;242(4886)\, 1654-1664.\n[2]&nbsp\;Contreras\, D. (2004). Electrophysiological classes of neocortical neurons.&nbsp\;Neural Networks\,&nbsp\;17(5-6)\, 633-646.\n[3]&nbsp\;Auslender\, I.\, Letti\, G.\, Heydari\, Y.\, Zaccaria\, C.\, & Pavesi\, L. (2025). Decoding neuronal networks: A Reservoir Computing approach for predicting connectivity and functionality.&nbsp\;Neural Networks\,&nbsp\;184\, 107058.\n[4]&nbsp\;Tibshirani\, R. (1996). Regression shrinkage and selection via the lasso.&nbsp\;Journal of the Royal Statistical Society Series B: Statistical Methodology\,&nbsp\;58(1)\, 267-288.\n\n\n\nAcknowledgement\nThis work was financed by the European Union - NextGenerationEU - National Recovery and Resilience Plan (NRRP) - Mission 4 Component 2 Investment 1.2 - "Funding projects presented by young researchers" MSCA PNRR Young Researchers\, "CIRCUS project" - MSCA20240000106 - CUP E63C25000820007.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:f6b1446ef98c9782963bd3d3f22c81e0
URL:http://cns2026.sched.com/event/f6b1446ef98c9782963bd3d3f22c81e0
END:VEVENT
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DTSTAMP:20260708T114850Z
DTSTART:20260713T192000Z
DTEND:20260713T212000Z
SUMMARY:P091: The Drosophila connectome reveals axo-axonic synapses on descending neurons
DESCRIPTION:Introduction\nAxo-axonic synapses can veto\, amplify\, or synchronize spikes\, yet their circuit-scale logic is unknown. Using the complete electron-microscopy connectome of the adult male Drosophila ventral nerve cord (MANC v1.2.1)\, we charted every axo-axonic input onto the 1\,314 descending neurons that carry brain commands to the ventral nerve cord.\n\n\n\nMethods\nA split-Gal4 driver specific to axo-axonic neurons was identified and cross to UAS-CsChrimson construct. Giant Fibers were activated by extracellular stimuli with electrodes placed in the brain. Muscle recordings were obtained from jump and flight muscles. A mouse primary antibody against ChAT was used to confirm cholinergic cells. LIF models were used with acetylcholine synapses\, GABA\, and glutamate. Simulations of these neurons were made using the BRIAN2 simulator for the ventral nerve cord (VNC) with the entire MANC v1.2.1 connectome. This resulted in 23\,437 valid neurons and 1\,152\,548 connections between them. In the fly\, cholinergic receptors are excitatory\, whereas glutamatergic and GABAergic receptors are inhibitory.\n\nResults\nOnly 1% of the 861\,591 possible descending–descending neuron pairs form such contacts\, but when present\, synaptic strength grows linearly with partner number regardless of transmitter identity. By definition\, any synapse connected to a descending neuron within the cord is axo-axonic. Neurons with many partners (high-degree nodes) integrate into the network without clustering into a ‘rich-club’ of hubs. We identified an octet of ascending neurons whose axo-axonic input to the Giant Fiber descending neurons predicted modulation of the escape circuit. Immunostaining confirms their cholinergic identity\, while optogenetic activation confirmed that this excitatory cohort increases Giant Fiber excitability\, validating connectome-derived rules.\n\n\n\nDiscussion\nBy analyzing all 1\,314 brain-originating DNs\, we move beyond scattered descriptions of AACs and derive circuit-level design rules for presynaptic modulation. The quantitative principles that emerged within this work are (1) extreme sparseness showing that between 0.7 and 1.2% of possible DN-to-DN\, AN-to-DN\, and IN-to-DN pairs form axo-axonic synapses\; (2) a tight linear relation between synaptic strength and partner multiplicity\; and (3) a small-world architecture that distributes integration rather than concentrating it in a rich-club core. Together\, these features constitute a wiring grammar for axo-axonic control in the adult&nbsp\;Drosophila&nbsp\;motor system.\n\n\nReferences\nCeballos\, Cesar\, Juan Lopez\, Ty Roachford\, Daniel Sanchez\, Sabrina Jara\, Kelli Robbins\, Casey L. Spencer\, Rodney Murphey\, and Rodrigo FO Pena. "The Drosophila connectome reveals axo-axonic synapses on descending neurons."&nbsp\;iScience&nbsp\;29\, no. 5 (2026).\n\nAcknowledgement\nR.P. was funded by an IBRO Collaborative Research Grants. This material is also based upon work supported by the U.S. Department of Education under Research and Development Infrastructure grant no. P116H230018. Any opinions\, findings\, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the U.S. Department of Education.\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:73944b9d8dc81ee74f79b044450378f1
URL:http://cns2026.sched.com/event/73944b9d8dc81ee74f79b044450378f1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260713T192000Z
DTEND:20260713T212000Z
SUMMARY:P092: A Proposed Etiological\, Pathophysiological\, and Rehabilitative Framework for Focal Task-Specific Dystonia: A Theoretical-Empirical Approach
DESCRIPTION:Introduction\nFocal task-specific dystonia (FTSD) is an isolated dystonia in which abnormal contractions emerge during a particular motor activity or task while other movements remain relatively spared [1]. We propose that FTSD arises when a task-specific motor synergy (TSMS) in primary motor cortex (M1) develops excitatory synapses that outpace parvalbumin (PV)-mediated inhibitory synapses\, a mechanism consistent with reduced motor cortical inhibition reported in FTSD [2\,3]. This imbalance gives rise to a symptom-threshold: motor output remains normal at or below a critical task intensity\, whereas the dystonic synergy is recruited once that threshold is exceeded.\n\n\nMethods\nUsing a single-case clinical chronology as motivation\, we built a proof-of-concept spiking neural network with leaky integrate-and-fire excitatory and inhibitory populations\, conductance-based synapses\, probabilistic E-to-I and I-to-E connectivity\, and Poisson external drive. Input amplitude was varied as a proxy for movement intensity. We modeled two TSMS states: a functional synergy with matched excitatory and inhibitory drive and a dystonic synergy with elevated excitatory strength without proportional inhibitory strengthening. Network output was quantified as population-averaged firing rate across input amplitudes.\n\nResults\nBalanced networks produced regular raster activity and firing rates that scaled with input\, while inhibitory-cell activity rose in parallel (Fig. 1). In alternative E/I parameter regimes\, stronger input recruited sufficient inhibitory feedback to stabilize or reduce firing after an initial rise. When functional and dystonic synergies coexisted\, dystonic firing remained low at weak inputs but surpassed functional-synergy firing after a discrete input threshold. Thus\, E/I imbalance generated an intensity-dependent switch from controlled output to hyperexcitable\, dystonic-dominant activity.\n\nDiscussion\nThese results support a TSMS account in which FTSD reflects a local M1 synergy whose excitatory circuit is selectively strengthened by repeated above-capacity practice (overreaching) while its PV-mediated inhibitory circuit fails to strengthen proportionally\, rather than a solely global basal ganglia or cerebellar disorder. The threshold behavior explains why symptoms can be task- and intensity-specific. We further propose below- or at-threshold retraining (BATR)\, a non-invasive motor-retraining protocol conceptually related to slow-down exercise [4]\, in which practice is constrained to at or below the symptom-threshold to strengthen PV-mediated inhibition and restore E/I balance without further potentiating dystonic excitation.\n\nFigure 1.&nbsp\;A\, stimulation evokes motor action via functional excitatory/inhibitory synergy\, with optional overlapping dystonic synergy. B\, healthy-state raster shows regular spikes across neurons. C\, firing rate increases with input strength\; inset shows inhibitory activity. D\, altered E/I balance produces a decreasing response. E\, dystonic and functional firing diverge at a threshold across input levels.​\n\nReferences\n1. Albanese\, A.\, et al. (2025). Definition and classification of dystonia. Movement Disorders\, 40(7)\, 1248–1259.\n2. Stahl\, C. M.\, & Frucht\, S. J. (2017). Focal task-specific dystonia: A review and update. Journal of Neurology\, 264(7)\, 1536–1541.\n3. Ridding\, M. C.\, et al. (1995). Changes in the balance between motor cortical excitation and inhibition in focal\, task-specific dystonia. Journal of Neurology\, Neurosurgery & Psychiatry\, 59(5)\, 493–498.\n4. Yoshie\, M.\, et al. (2015). Slow-down exercise reverses sensorimotor reorganization in focal hand dystonia: A case study of a pianist. International Journal of Neurorehabilitation\, 2(2)\, 2376–0281.\n\nAcknowledgement\nThe authors thank colleagues who provided helpful informal feedback on earlier versions of this work.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:4b03965f17c54ec9a4c8924f1c1c7678
URL:http://cns2026.sched.com/event/4b03965f17c54ec9a4c8924f1c1c7678
END:VEVENT
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DTSTAMP:20260708T114850Z
DTSTART:20260713T192000Z
DTEND:20260713T212000Z
SUMMARY:P093: Stabilizing Fractional Dynamical Networks Effectively Suppresses Epileptic Seizures
DESCRIPTION:Introduction\nFor 15 million patients worldwide with drug-resistant epilepsy\, neurostimulation is a promising solution to&nbsp\;counteract seizure activity [1]. However\, current neurostimulation devices are unable to provide personalized&nbsp\;or adaptive care due to their over reliance on pre-programmed responses that use fixed stimulation&nbsp\;parameters [2]. A new framework for characterizing seizure dynamics is needed to design effective stimulation.&nbsp\;Fractional-order dynamical networks accurately capture multi-scale neural dynamics and the spatial&nbsp\;relationship between brain regions [3]. Here\, we provide a stabilizing fractional-order dynamical framework&nbsp\;to characterize seizure dynamics across epileptic states and effectively suppress epileptic activity.\n\n\nMethods\nUsing intracranial EEG data recorded from 10 focal epilepsy patients\, we explicitly model the multi-scale&nbsp\;temporal structure (captured by fractional-order exponents) and stability properties (captured by eigenvalues&nbsp\;of fractional-order systems) across interictal\, pre-ictal\, ictal\, and post-ictal brain states. We apply the&nbsp\;Kolmogorov-Smirnov 2-sample statistical test to fractional-order exponents and eigenvalues during different&nbsp\;brain states to understand the evolution of brain dynamics across patients. We apply a novel stabilizing&nbsp\;control framework to 35 seizure snapshots. We simulate the controlled signals and compute their difference&nbsp\;in energy with uncontrolled epileptic data to assess effective suppression.\n\nResults\nOur results show that our framework can capture consistent and distinct patterns over all epileptic brain&nbsp\;states in multi-scale and stability properties across most patients. Median fractional-order exponents decreased&nbsp\;from interictal (0.75) to pre-ictal (0.68) and ictal (0.63) and then increased during post-ictal (0.78).&nbsp\;Eigenvalues followed a similar trend as fractional-order exponents. We observed increased variance of eigenvalues&nbsp\;during post-ictal. Our stabilizing control framework achieved seizure suppression in 34/35 seizures\,&nbsp\;successfully stabilizing 77% of initially unstable seizures and reducing seizure amplitude by approximately&nbsp\;49% across all electrodes.\n\nDiscussion\nThe decrease in fractional-order exponent values during interictal to ictal indicates a progressive strengthening&nbsp\;of long-range temporal memory as the seizure approaches\, which is consistent with critical slowing [4]. Furthermore\, the wide spread in fractional-order exponents during post-ictal likely suggests variable long range&nbsp\;temporal memory properties post-seizure\, without returning to baseline interictal levels. Tracking fractional-order exponents may be useful for seizure prediction in future studies. In this work\, we demonstrate&nbsp\;the capability of our state-of-the-art stabilizing state feedback control scheme to effectively suppress&nbsp\;epileptic activity in a computationally tractable control manner that is straightforward to implement.\n\nFigure 1.&nbsp\;Percentage amplitude reduction for each seizure. Gray bars represent seizures that are already stable (maximum eigenvalues &lt\; 1)\, while blue bars indicate seizures that require stabilization. Vertical dashed lines separate patients. Only 1 seizure increased in amplitude after control. Control reduced amplitude by an average of 48.96% + 16.94%.​\n\nReferences\n[1] Edwards\, C. A.\, Kouzani\, A.\, Lee\, K. H. & Ross\, E. K. Neurostimulation devices for the treatment of neurologic disorders. In Mayo Clinic Proceedings\, vol. 92\, 1427–1444 (Elsevier\, 2017).\n\n[2] Morrell\, M. J. Responsive cortical stimulation for the treatment of medically intractable partial epilepsy. Neurology 77\, 1295–1304 (2011).\n\n[3] Reed\, E.\, Chatterjee\, S.\, Ramos\, G.\, Bogdan\, P. & Pequito\, S. Fractional cyber-neural systems—a brief survey. Annual Reviews in Control 54\, 386–408 (2022).\n[4] Maturana\, M. I. et al. Critical slowing down as a biomarker for seizure susceptibility. Nature communications 11\, 2172 (2020).\n\nAcknowledgement\nEP gratefully acknowledges the support of Texas Tech University. GR is funded by national funds through FCT – Fundação para a Ciência e a Tecnologia\, I.P.\, and\, when eligible\, co-funded by EU funds under project/support UID/50008/2025 – Instituto de Telecomunicações\, with DOI identifier&nbsp\;https://doi.org/10.54499/UID/50008/2025.
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:0b46dbfde55cf6b2f0731ce87308ccf2
URL:http://cns2026.sched.com/event/0b46dbfde55cf6b2f0731ce87308ccf2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260713T192000Z
DTEND:20260713T212000Z
SUMMARY:P094: Neuron–Astrocyte Coupling Regulates Ion Homeostasis and Neuronal Excitability in a Multi-Compartment Model
DESCRIPTION:Introduction\nNeuronal excitability depends on transmembrane gradients of sodium (Na)\, potassium (K)\, and chloride (Cl). Astrocytes contribute to ionic homeostasis by regulating extracellular ion concentrations through membrane channels\, transporters\, and spatial buffering across their extended processes and syncytial networks [1]. Among the main pathways are K uptake via astrocytic Na/K-ATPase (aNKA) and Kir4.1 channels\, and Cl regulation via ClC-2 channels and GABA-A receptors [2\, 3]. Although these mechanisms have been studied individually\, their combined influence on extracellular ion dynamics and neuronal activity in the coupled neuron–extracellular space (ECS)–astrocyte system remains unclear.\n\nMethods\nWe developed a multi-compartment model consisting of a neuron (N) interacting with an astrocytic shell (A) through a local extracellular space (E). Astrocytes are coupled via gap junctions to a distal glial syncytium (G)\, which exchanges ions with a bath reservoir (B). Neuronal dynamics follow Hodgkin–Huxley kinetics with voltage-gated and leak Na\, K and Cl channels\, muscarinic currents\, neuron Na/K-ATPase (nNKA)\, and K–Cl cotransporters (KCC)\, while the ECS tracks ionic accumulation. Astrocytes include aNKA\, Kir4.1 channels\, Cl fluxes\, and glutamate (GLT-1) and GABA (GAT) transporters. Simulations quantify how neuronal activity alters extracellular ion concentrations and how astrocytic regulation feeds back onto neuronal excitability.\n\n\nResults\nCoupling the neuron compartment to a closed extracellular space reveals strong activity-dependent ionic accumulation. During sustained stimulation\, extracellular K progressively increases\, shifting reversal potentials and altering neuronal firing dynamics until the system enters depolarization block. In the neuron–ECS configuration this depolarized state is stable\, preventing recovery of the initial resting equilibrium. Introducing astrocytic mechanisms delay depolarization block through the interplay between K uptake and Cl fluxes. When distal buffering pathways are included\, ionic redistribution through astrocytic networks and extracellular diffusion toward a bath reservoir restores the hyperpolarized resting state.\n\nDiscussion\nThese results highlight the importance of astrocyte-mediated ion regulation for stabilizing neuronal excitability. While neuronal mechanisms alone cannot recover from activity-induced ionic imbalance\, astrocytic buffering reduces extracellular K accumulation and delays depolarization block. Recovery of the resting equilibrium requires distal ion redistribution through gap-junction–coupled astrocytic networks and extracellular diffusion toward distal reservoirs [4]. The efficiency of this process depends on the rate of intercellular exchange and parenchyma tortuosity\, which constrains ionic diffusion. Together\, these mechanisms provide a dynamical framework for how astrocytes regulate ionostasis and maintain stable neuronal activity.\n\nReferences\n\nVerkhratsky\, A.\, & Nedergaard\, M. (2018). Physiology of astroglia. Physiological reviews\, 98(1)\, 239-389. https://doi.org/10.1152/physrev.00042.2016\nKofuji\, P.\, & Newman\, E. (2004). Potassium buffering in the central nervous system. Neuroscience\, 129(4)\, 1043-1054. https://doi.org/10.1016/j.neuroscience.2004.06.008\nUntiet\, V.\, & Verkhratsky\, A. (2024). How astrocytic chloride modulates brain states. BioEssays\, 46(6)\, 2400004. https://doi.org/10.1002/bies.202400004\nHübel\, N.\, & Dahlem\, M. A. (2014). Dynamics from seconds to hours in Hodgkin-Huxley model with time-dependent ion concentrations and buffer reservoirs. PLoS computational biology\, 10(12)\, e1003941. https://doi.org/10.1371/journal.pcbi.1003941\n\nAcknowledgement\nWe acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) [Grant ID: RGPIN 2024 04333]\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:e994352035048f82e1242c1f9350e631
URL:http://cns2026.sched.com/event/e994352035048f82e1242c1f9350e631
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DTSTAMP:20260708T114850Z
DTSTART:20260713T192000Z
DTEND:20260713T212000Z
SUMMARY:P095: Universal rules for growing artificial astrocytes at electron microscopy resolution
DESCRIPTION:Introduction\nAstrocytes are ubiquitous glial cells of the cortex which display a complex ramified anatomy. Astrocyte processes envelop synapses and dendrites\, mediating diverse neuromodulatory pathways. While it is speculated that the domain of astrocyte-mediated neuromodulation is influenced by their anatomy\, it is currently unknown what the stereotypical shape of an astrocyte is\, beyond the simple observation of their branching. Nor do we know whether astrocyte anatomy abides by universal rules across brain regions and species.\n\nMethods\nEmploying machine learning\, graph theory\, and topological analysis\, we developed a comprehensive library of morphometric measures that extract quantitative anatomical features of astrocytes and neurons resolved under electron microscopy (Schubert et al.\, 2022). Based on trends in anatomical features specific to astrocyte morphology\, we developed a generative algorithm that synthesizes astrocyte branching structure\, inspired by the Minimum Spanning Tree (Cuntz et al.\, 2011).\n\nResults\nUsing our library of morphometric features\, we can quantitatively differentiate astrocytes both from neurons and from astrocytes of different species. Astrocytes are spatially complex cells with branches packed into a small space. They have large primary branches that define the shape of their territory and fine\, diffusive branchlets that fill up the space within their territory. Based on these observations\, we created a generative algorithm that replicates astrocyte branched anatomy\, including the distinction between primary and terminal processes across different regions and animals.\n\nDiscussion\nWe present the first systematic characterization of cortical astrocyte anatomy from high-resolution EM datasets in different species. Our analysis\, in particular\, allowed us to identify salient astrocyte features that\, in turn\, can be used to constrain the minimal spanning tree as a general recipe to grow astrocytes in silico.\n\nReferences\nCuntz\, H.\, Forstner\, F.\, Borst\, A.\, & Häusser\, M. (2010). One rule to grow them all: a general theory of neuronal branching and its practical application. PLoS computational biology\, 6(8)\, e1000877.\n \nSchubert\, P. J.\, Dorkenwald\, S.\, Januszewski\, M.\, Klimesch\, J.\, Svara\, F.\, Mancu\, A.\, ... & Kornfeld\, J. (2022). SyConn2: dense synaptic connectivity inference for volume electron microscopy. Nature Methods\, 19(11)\, 1367-1370.\n \nThe MICrONS Consortium (2025). Functional connectomics spanning multiple areas of mouse visual cortex. Nature\, 640(8058)\, 435–447.\n\nAcknowledgement\nWe acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) [Grant ID: RGPIN 2024 04333\, 589115 2024]\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:c1ab06c5bd5519af1a3d0e3a98326475
URL:http://cns2026.sched.com/event/c1ab06c5bd5519af1a3d0e3a98326475
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260713T224500Z
DTEND:20260714T004500Z
SUMMARY:Banquet Dinner
DESCRIPTION:\n
CATEGORIES:RECEPTION/PARTY
LOCATION:TBA\, Halifax\, NS\, Canada
SEQUENCE:0
UID:bc12c1a6bc6592eb9ef81147a7d2e317
URL:http://cns2026.sched.com/event/bc12c1a6bc6592eb9ef81147a7d2e317
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BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260714T111500Z
DTEND:20260714T200000Z
SUMMARY:Registration
DESCRIPTION:\n
CATEGORIES:REGISTRATION/ORGANIZATION
LOCATION:TBA\, Halifax\, NS\, Canada
SEQUENCE:0
UID:07a13ae45700082c3ac5f49abca3f047
URL:http://cns2026.sched.com/event/07a13ae45700082c3ac5f49abca3f047
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260714T120000Z
DTEND:20260714T153000Z
SUMMARY:Building clinically impactful models in computational psychiatry: A worked example using induced pluripotent stem-cell derived neuronal data
DESCRIPTION:\n
CATEGORIES:WORKSHOP
LOCATION:Room 506\, Halifax\, NS\, Canada
SEQUENCE:0
UID:4faca6f1cfd913729a00ee99fe840496
URL:http://cns2026.sched.com/event/4faca6f1cfd913729a00ee99fe840496
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260714T120000Z
DTEND:20260714T153000Z
SUMMARY:Constraining large-scale models of brain dynamics with local biological properties: Methods and applications
DESCRIPTION:A central challenge in neuroscience is understanding how brain structure gives rise to complex\, large-scale brain dynamics when local biological properties vary systematically across brain regions rather than being spatially uniform. Converging evidence from connectomics\, transcriptomics\, myeloarchitecture\, chemoarchitecture\, and other tissue-level measurements demonstrates that such regional variation is a fundamental feature of brains across species and a key driver of neural dynamics. Constraining large-scale models of brain dynamics with these local biological properties is therefore essential for increasing biological realism and for developing more accurate mechanistic accounts of brain function. Recent advances in multimodal brain mapping and computational modeling now make it possible to integrate spatially heterogeneous biological information across the whole brain into dynamical models\, enabling stronger links across multiple scales of brain organization. \n\nThis workshop brings together leading experts with complementary expertise in incorporating biologically grounded\, spatially varying constraints into whole-brain field and network models of dynamics. Through six engaging talks\, the speakers will demonstrate how these constraints fundamentally alter predictions of brain criticality\, large-scale wave dynamics\, functional connectivity\, synchronization\, and the spreading of activity and pathology in brain disease\, in both humans and non-human species. Specifically\, the workshop aims to (i) highlight key open-access repositories of spatially resolved biological brain data\, (ii) showcase state-of-the-art tools and methods for large-scale brain modeling\, and (iii) expand the participants’ analytic capabilities. Together\, these contributions will equip the workshop participants with the necessary knowledge for incorporating local biological constraints into their own models and interrogating their influence on the organization of brain activity\, advancing a more unified and biologically grounded understanding of the mechanisms of brain function and dynamics.\n\n\nSchedule of talks:\n09:00 - 09:30 James Pang&nbsp\;(Monash University\, Australia) - Geometric influences on mammalian brain organization\, connectivity\, and dynamics\n\n09:30 - 10:00 Changsong Zhou (Hong Kong Baptist University\, Hong Kong) - Optimal Griffiths phase in heterogeneous human brain networks: Brain criticality embracing stability and flexibility across individuals (virtual presentation)\n\n10:00 - 10:30 Adrian Ponce Alvarez (Polytechnic University of Catalonia\, Spain)&nbsp\;- Nonlinear network mechanisms driving brain activity hierarchies\n\n10:30 - 11:00 BREAK\n\n11:00 - 11:30 Vincent Bazinet (McGill University\, Canada) - Network architecture and regional heterogeneity shape patterns of neurodegeneration\n\n11:30 - 12:00 Joana Cabral (University of Lisbon\, Portugal) - From tissue mechanics to brain dynamics&nbsp\;(virtual presentation)\n\n12:00 - 12:30 John Griffiths (Centre for Addiction and Mental Health\, Canada) - What’s the missing secret sauce in whole-brain models of fMRI functional connectivity?\n\n\n
CATEGORIES:WORKSHOP
LOCATION:Room 504\, Halifax\, NS\, Canada
SEQUENCE:0
UID:18f91fcab12451c258f479d0c18d8737
URL:http://cns2026.sched.com/event/18f91fcab12451c258f479d0c18d8737
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260714T120000Z
DTEND:20260714T153000Z
SUMMARY:Controlling Neural Network Stability
DESCRIPTION:Summary:\nNeural systems are highly recurrent\, nonlinear networks that must balance stability and flexibility to support robust information processing. A growing body of theory suggests that the brain operates near critical regimes\, often described as the edge of chaos\, where dynamics are both stable enough to be controllable and rich enough to enable valuable computation. Maintaining this balance is challenging: biologically realistic networks are sparse\, structured\, and low dimensional\, properties that complicate traditional notions of synaptic balance and make static stability constraints difficult to enforce. Moreover\, ongoing sensory input and behavioral demands continuously perturb network dynamics\, requiring regulation on multiple timescales.\n\nThis workshop brings together diverse perspectives on how neural networks achieve\, lose\, and regain dynamical stability. Topics may include adaptive and plastic mechanisms that modulate effective connectivity\, the role of nonlinear and history-dependent dynamics\, and data-driven approaches for inferring time-varying stability from electrophysiological recordings. The workshop will also explore implications for neurological states characterized by altered excitability\, such as epilepsy or anesthesia\, and discuss how stimulation\, control-theoretic frameworks\, and modern system-identification methods can be used to probe and influence network stability. By integrating theory\, modeling\, and experimental insights\, the workshop aims to foster cross-disciplinary dialogue on principled strategies for understanding and controlling complex brain dynamics.\n\nTentative program:\n9:00-9:10: Introduction – Brian Lundstrom\, MD\, PhD\, Mayo Clinic\n9:10-9:30: Paul Bogdan\, PhD\, University of Southern California - Theoretical Foundations of NeuroAI: A Modeling Framework Motivated by Living Neuronal Network Dynamics\n9:30-9:50: Leandro Fosque\, PhD\, Washington University in St. Louis - Criticality and Adaptation in Neural Systems\n9:50-10:10: Srishty Aggarwal\, Indian Institute of Science - Adaptation\, Ageing\, and Stability in Recurrent Brain Networks\n10:10-10:30: Discussion\n\n10:30-11:00: Break\n\n11:00-11:20: Emily Pereira\, PhD\, Texas Tech University - Stabilizing Fractional Dynamical Networks Suppresses Epileptic Seizures\n11:20-11:40: Tom Richner\, PhD\, Mayo Clinic - Adaptation Modulates Effective Connectivity and Network Stability\n11:40-12:00: Audrey Sederberg\, PhD\, Georgia Institute of Technology - Critical Dynamics\, Effective Dimensionality\, and Flexible Learning in Neural Systems\n12:00-12:30: Discussion
CATEGORIES:WORKSHOP
LOCATION:Room 507\, Halifax\, NS\, Canada
SEQUENCE:0
UID:462a08805dd495d4d17d5334dedfeef7
URL:http://cns2026.sched.com/event/462a08805dd495d4d17d5334dedfeef7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260714T120000Z
DTEND:20260715T153000Z
SUMMARY:Evolution\, Computation and the Origins of Nervous Systems: from Animal Models to Neuromorphic Engineering
DESCRIPTION:Workshop description\n\nEven putatively simple nervous systems exhibit a high degree of complexity that is not fully understood. The recent discovery of associative learning in box jellyfish\, for example\, highlights the surprisingly rich behavioural repertoire of such networks and invites a more in-depth study of the underlying neural mechanisms. These insights serve as a promising inspiration to develop technical systems that fully exploit the underlying principles of the unmatched efficiency and resilience of natural systems.&nbsp\;\nThe aim of this one-day workshop is to bring together biologists working on biological model systems with theoreticians developing approaches to understand these systems and engineers working to implement novel\, non-conventional neuromorphic computing schemes and applications. Relatively simple - but resilient and efficient - nervous systems might be an excellent inspiration for edge AI\, synthetic cells\, sensing or miniature robotics. Therefore this workshop aims to distill principles from the evolution of the nervous system to inform theoretical insight and ultimately to design novel neuro-inspired computing hardware.\n\nHere is the link to the workshop homepage.\n\nWe are very much looking to welcoming you in our workshop.\n\nBest wishes from Kiel\n\nJan Steinkühler and Wilhelm Braun
CATEGORIES:WORKSHOP
LOCATION:Room 603\, Halifax\, NS\, Canada
SEQUENCE:0
UID:1ffbedb66919276918cef2a314f184e9
URL:http://cns2026.sched.com/event/1ffbedb66919276918cef2a314f184e9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260714T120000Z
DTEND:20260714T153000Z
SUMMARY:Mapping extracellular waveforms produced by the three neuronal compartments
DESCRIPTION:Detecting&nbsp\;and sorting available extracellular neuronal action potentials remains&nbsp\;challenging&nbsp\;in neuroscience. This bottleneck stems from uncertainty about the&nbsp\;origin of a substantial&nbsp\;fraction of the signals present in electrophysiological&nbsp\;recordings\, leading to systematic&nbsp\;removal of a fraction of the recordings.&nbsp\;Aiming for generalizable solutions\, this workshop&nbsp\;will approach the bottleneck by&nbsp\;separating the extracellular contributions from dendrites\,&nbsp\;soma\, and axons. Biophysics&nbsp\;predicts that each of these compartments generates distinct&nbsp\;extracellular&nbsp\;waveforms that collectively\, yet in varying proportions\, contribute to&nbsp\;extracellularly recorded spikes. The central aim is to disentangle the&nbsp\;contribution from these&nbsp\;three compartments to enable more inclusive\, and more&nbsp\;precise interpretations of in vivo&nbsp\;electrophysiological data.\n \nThere is a strong push to create more reliable\, more automated approaches that&nbsp\;bypass&nbsp\;the need for manual curation. Machine learning methods show promise to&nbsp\;replace manual&nbsp\;curation\, yet they will require reliable ground-truth. We&nbsp\;hypothesize here that the origin of&nbsp\;exotic waveforms can be reframed as putative&nbsp\;combination(s) from different neuronal&nbsp\;compartments with the known sequential&nbsp\;activation. A systematic reorganization of existing&nbsp\;datasets\, conditioned to&nbsp\;libraries of waveform based on the three ubiquitous neural&nbsp\;compartments\, might&nbsp\;already be sufficient to lay the foundation for tomorrow’s automated&nbsp\;tools.\n \nThe speakers will showcase recent advances in both biophysical modeling and in vivo measurements identifying neuronal compartments in extracellular potentials\, with the objective of better cataloguing the waveforms that are currently attributed to each of the three neuronal compartments. Thus\, we assess the diversity of these waveforms across existing recordings. We target a broad audience\, including computational modelers\, spikes sorting experts\, and users of high-density silicon probes\, each offering complementary perspectives on the complexity of the problem. \n\nSpeakers:\n\n09:00 – 09:15 Jérémie Sibille (Charité University Hospital\, Berlin\, Germany)\nIntroduction\n\n09:15 – 09:40 Rishikesh Narayanan (Indian Institute of Science\, Bangalore\, India) \nLocal field potentials: Active dendritic and gap junctional contributions\n\n09:40 – 10:05 Alexandra Tzilivaki (Charité University Hospital\, Berlin\, Germany) \nTBA\n\n10:05 – 10:30 Paula Kuokkanen&nbsp\;(Humboldt-Universität zu Berlin\, Germany)\nAxonal field contributions – a rule or an exception?\n\n10:30 – 11:00 Pause\n\n11:00 – 11:25 Sharon Crook&nbsp\;(Arizona State University\, AZ\, US)\nDiscovering features for neuron-type identification from extracellular recordings\n\n11:25 – 11:45 Costas Anastassiou (Cedars-Sinai and Caltech\, Los Angeles\, CA\, US)\nIn vivo identification of human cortical cell types in human electrophysiological recordings&nbsp\;\n\n11:45 – 12:15 Nick Steinmetz (University of Washington in Seattle\, WA\, US)\nInsights and puzzles about high density extracellular waveforms from Neuropixels Ultra\n\n12:15 – 12:30 Discussion\n\nThe final schedule is yet to be confirmed
CATEGORIES:WORKSHOP
LOCATION:Room 505\, Halifax\, NS\, Canada
SEQUENCE:0
UID:56d7d62d7e3347495bfe3b6dd794a312
URL:http://cns2026.sched.com/event/56d7d62d7e3347495bfe3b6dd794a312
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260714T120000Z
DTEND:20260714T153000Z
SUMMARY:Modeling Ion Dynamics in the Brain: From Cells to Networks and Global (Dys)-Functional States
DESCRIPTION:The schedule and abstracts are available at: https://brady.cs.cas.cz/events/ocns-2026-workshop\n\nIon balance is a fundamental determinant of brain physiology\, and its dysregulation contributes to a wide range of neurological disorders. Understanding how ion equilibria are established\, maintained\, and disrupted across spatial and temporal scales remains a central challenge in neuroscience. Computational neuroscience provides a robust framework to address this challenge by enabling systematic investigations of ion dynamics under physiological and pathological conditions. Existing modeling approaches span a broad spectrum\, from biophysically grounded models of ion concentrations\, osmolarity\, and cell volume regulation at the single-neuron level\, to population and network models capturing ion exchange mechanisms\, energy-dependent transport\, and their impact on large-scale brain dynamics. Each modeling strategy offers complementary insights depending on the scientific question being addressed. This workshop will highlight recent advances in ion modeling and discuss emerging frameworks\, their underlying assumptions\, and their relevance for understanding ion mechanisms in the brain.\n\nThe workshop is structured to reflect a progression from fundamental cellular principles to network-level dynamics and global brain states\, including resting state\, seizures\, and sleep. One focus is on detailed models addressing the physical and cellular foundations of ion homeostasis and its breakdown at the single-neuron level under pathological conditions like ischemia. Subsequent contributions bridge detailed cellular ion dynamics with population-level descriptions\, highlighting how ion exchange mechanisms and energy-dependent active transport shape collective neuronal behavior. Further talks explore how chronic ion perturbations can drive pathological network dynamics and how intrinsic ion dynamics influence large-scale functional connectivity observed in resting-state brain activity. The workshop concludes with models illustrating how neuromodulatory processes interact with ion dynamics to generate and control global brain states.
CATEGORIES:WORKSHOP
LOCATION:Room 502\, Halifax\, NS\, Canada
SEQUENCE:0
UID:df989af39f64af07edf1596f87d32b96
URL:http://cns2026.sched.com/event/df989af39f64af07edf1596f87d32b96
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260714T120000Z
DTEND:20260714T153000Z
SUMMARY:Neuronal heterogeneity’s role in network dynamics and computation
DESCRIPTION:The detailed agenda for this workshop\, along with abstracts for each talk\, can be found here.\n\nAs high-throughput single-cell experimental workflows become standard in the field of neuroscience\, the immense heterogeneity of neurons in the human brain has become increasingly apparent. While this heterogeneity is in part reflected in the historical pursuit of canonical “cell types\,” contemporary data highlights variability in key intrinsic cellular properties\, even within classical cell types. Whether this heterogeneity serves a functional purpose in the human brain\, or is merely a byproduct of biological noise\, remains an open question. Computational and mathematical techniques are particularly well suited to address this question considering the experimental challenges involved in varying heterogeneity in vitro or in vivo—indeed\, most common experimental manipulations inherently reduce heterogeneity by grossly up or down-regulating a particular neuronal characteristic.\n\nIn this workshop\, we will highlight the growing interest within the field of computational neuroscience to leverage in silico tools to study the functional role of neuronal heterogeneity. This research addresses a wide variety of neuroscientific questions of interest to OCNS attendees\, ranging from mechanisms underlying neuronal synchronization (e.g. epileptic seizures) to memory formation in neural networks. Talks will cover a diverse set of approaches\, ranging from foundational mathematical theory to biophysically detailed models reflecting experimentally observed neuronal heterogeneities.\n\nConfirmed speakers include:\nDr. Richard Gast\nPostdoc\; Dorris Center for Neuroscience\, Scripps Research\n&nbsp\;\nSanjna Kumari\nGraduate Student\; Indian Institute of Science\n&nbsp\;\nDr. Jeremie Lefebvre\nAssociate Professor\; University of Ottawa\; Department of Biology\n\nDr. Andre Longtin \nProfessor\; University of Ottawa\; Department of Physics\n\nDr. Laura Medlock\nPostdoc\; Krembil Research Institute\, University Health Network\n\nDr. Scott Rich&nbsp\;\nAssistant Professor\; University of Connecticut\; Departments of Physiology and Neurobiology\, Biomedical Engineering\, Mathematics\, and Institute for Brain and Cognitive Sciences\n\nMarco Zenari (filling in for Dr. Luca Mazzucato)\nPadova Neuroscience Center\, University of Padova\, Italy
CATEGORIES:WORKSHOP
LOCATION:Room 501\, Halifax\, NS\, Canada
SEQUENCE:0
UID:70f80a520e09d745723fd0348fc03f50
URL:http://cns2026.sched.com/event/70f80a520e09d745723fd0348fc03f50
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260714T120000Z
DTEND:20260714T153000Z
SUMMARY:Social Neuro-AI
DESCRIPTION:\n
CATEGORIES:WORKSHOP
LOCATION:Room 602\, Halifax\, NS\, Canada
SEQUENCE:0
UID:054545f110086f7604229a8bfb2b8972
URL:http://cns2026.sched.com/event/054545f110086f7604229a8bfb2b8972
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260714T120000Z
DTEND:20260715T203000Z
SUMMARY:Workshop on Methods of Information Theory in Computational Neuroscience
DESCRIPTION:Workshop website (including full schedule):&nbsp\;https://jlizier.github.io/CNS2026-InfoTheoryWorkshop/\n\nMethods originally developed in Information Theory have found wide applicability in computational neuroscience. Beyond these original methods there is a need to develop novel tools and approaches that are driven by problems arising in neuroscience. A number of researchers in computational/systems neuroscience and in information/communication theory are investigating problems of information representation and processing. While the goals are often the same\, these researchers bring different perspectives and points of view to a common set of neuroscience problems. Often they participate in different fora and their interaction is limited. The goal of the workshop is to bring some of these researchers together to discuss challenges posed by neuroscience and to exchange ideas and present their latest work. The workshop is targeted towards computational and systems neuroscientists with interest in methods of information theory as well as information/communication theorists with interest in neuroscience. \n\nSpeakers include:\nDemian Battaglia\, CNRS / University of Strasbourg\, FranceLeyla Roksan Caglar\, Icahn School of Medicine at Mount Sinai\, USAMarilyn Gatica\, Northeastern University London\, UKNicolás Hinrichs\, Max Planck Institute for Human Cognitive and Brain Sciences\, Leipzig\, GermanyJaroslav Hlinka\, Czech Academy of Sciences\, Czech RepublicJoseph Lizier\, The University of Sydney\, AustraliaPatricio Orio\, Universidad de Valparaíso\, ChileMaria Pope\, Indiana University\, USA&nbsp\;Thomas Varley\, University of Vermont\, USA\n
CATEGORIES:WORKSHOP
LOCATION:Room 604\, Halifax\, NS\, Canada
SEQUENCE:0
UID:ab536d095f7cf8630d1406c51c60d1bc
URL:http://cns2026.sched.com/event/ab536d095f7cf8630d1406c51c60d1bc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260714T171000Z
DTEND:20260714T182000Z
SUMMARY:Keynote 4
DESCRIPTION:\n
CATEGORIES:KEYNOTE
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:373532839c98da341b8c58e2be1248e9
URL:http://cns2026.sched.com/event/373532839c98da341b8c58e2be1248e9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260714T182000Z
DTEND:20260714T183000Z
SUMMARY:Conference Photo
DESCRIPTION:\n
CATEGORIES:REGISTRATION/ORGANIZATION
LOCATION:TBA\, Halifax\, NS\, Canada
SEQUENCE:0
UID:5c1bb75a982a57c53f121ff7bf065501
URL:http://cns2026.sched.com/event/5c1bb75a982a57c53f121ff7bf065501
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260714T183000Z
DTEND:20260714T190000Z
SUMMARY:Coffee break
DESCRIPTION:\n
CATEGORIES:BREAK
LOCATION:Ballroom Salon\, Halifax\, NS\, Canada
SEQUENCE:0
UID:5f726504e7e90fb5967b728bf40d80fe
URL:http://cns2026.sched.com/event/5f726504e7e90fb5967b728bf40d80fe
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260714T190000Z
DTEND:20260714T200000Z
SUMMARY:Member's meeting
DESCRIPTION:\n
CATEGORIES:REGISTRATION/ORGANIZATION
LOCATION:Ballroom B1\, Halifax\, NS\, Canada
SEQUENCE:0
UID:74de5d47da06ac728b22df0d506ebb5a
URL:http://cns2026.sched.com/event/74de5d47da06ac728b22df0d506ebb5a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260714T200000Z
DTEND:20260714T220000Z
SUMMARY:Poster Session 3
DESCRIPTION:\n
CATEGORIES:POSTER SESSION
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:c07dc75a89832051a6dccd6c95a10d29
URL:http://cns2026.sched.com/event/c07dc75a89832051a6dccd6c95a10d29
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260714T200000Z
DTEND:20260714T220000Z
SUMMARY:P096: Toward Auditory-Like Sparse Representations: Adaptive Central Frequencies Locally Competitive Algorithm for Efficient Neuromorphic Speech Recognition
DESCRIPTION:Introduction\nThe auditory periphery efficiently transmits sparsely encoded information to cochlear nuclei and higher centers while preserving acoustic features. Lateral inhibition among cochlear hair cells and cochlear nucleus neurons increases sparsity\, improves frequency selectivity and resolution. Outer hair cells (OHC) dynamically modulate cochlear responses through stiffness changes\, hypothesized to refine frequency tuning and receptive fields of inner hair cells (IHC).\nInspired by these mechanisms\, we implement a sparse coding approach using a lateral inhibitory neuromorphic network. We propose the Adaptive Central Frequencies Locally Competitive Algorithm (ALCA-CF)\, which adapts neuronal parameters to optimize acoustic signal representation [1].\n\nMethods\nALCA-CF optimizes offline the IHC gammachirp models by adapting receptive field sensitivity to the acoustic environment. This partially mimics the online adaptability of OHC\, where stiffness changes affect IHC responses. Additionally\, lateral inhibition in ALCA-CF enhances frequency resolution while maintaining sparse coding\, like mechanisms in the cochlear system (see Fig. 1). By separately analyzing the impact of bandwidth\, central frequency changes\, and sparsity of the IHC models\, we quantify their influence on signal quality and recognition. We also show that this processing produces state-of-the-art (SOTA) speech representations enabling SOTA classification with much smaller and simpler spiking neural networks.\n\n\nResults\n​ALCA-CF improves reconstruction quality and sparsity over fixed Gammatone filter bank. On Heidelberg Digits\, ALCA-CF achieves an SNR of 15.35 dB (+5.52 dB vs. fixed filters) and reduces sparsity by 17.53%. Similar gains are observed on Google Speech Commands (SNR: 23.04 dB). ALCA-CF learns a non-linear frequency resolution\, removing information in irrelevant frequency intervals while concentrating coefficients in relevant ones\, yielding sparser and more efficient speech representations. On Intel's Loihi 2 [2]\, a neuromorphic chip\, ALCA-CF reaches 94.88% speech classification accuracy at 0.004 W\, a 5.25× reduction vs. fixed filters (0.021 W) and 3.75× vs. LAUSCHER silicon cochlea [3] SHD (0.015 W)\, which achieved a lower accuracy of 83.77%.\n\nDiscussion\nALCA-CF provides an adaptive front-end trained independently of any target application\, distinguishing it from supervised approaches where the front-end is optimized for a specific task. This independence makes it highly versatile\, as the same method has the potential to adapt to acoustic signals of varying nature\, including speech\, environmental sounds\, and music. Furthermore\, ALCA-CF offers the ability to modulate the number of active filters based on data complexity and signal nature\, enabling a flexible trade-off between representation accuracy and computational efficiency. This flexibility is particularly advantageous for embedded neuromorphic systems\, where energy constraints demand sparse and content-adaptive representations.\n\nFigure 1.&nbsp\;Overview of the ALCA-CF front-end. Each filter is represented by a neuron with a receptive field. Red arrows are lateral inhibition synapses and blue arrows are the feedback that adapts each neuron's receptive field. Lateral inhibition weights are the correlation between pre- and post-synaptic neuron receptive fields. Neuron activations ai are represented by blue dots in the time-frequency output.​\n\nReferences\n\n\n[1]&nbsp\;Bahadi\, S.\, Plourde\, E.\, & Rouat\, J. (2025\, April). Adaptive Central Frequencies Locally Competitive Algorithm for Speech. In&nbsp\;ICASSP 2025-2025 IEEE International Conference on Acoustics\, Speech and Signal Processing (ICASSP)&nbsp\;(pp. 1-5). IEEE.&nbsp\;https://doi.org/10.1109/ICASSP49660.2025.10887648\n[2]&nbsp\;Orchard\, G.\, et al. (2021\, October). Efficient neuromorphic signal processing with loihi 2. In&nbsp\;2021 IEEE workshop on signal processing systems (SiPS)&nbsp\;(pp. 254-259). IEEE. https://doi.org/10.1109/SiPS52927.2021.00053\n[3]&nbsp\;Cramer\, B.\, et al. (2020). The heidelberg spiking data sets for the systematic evaluation of spiking neural networks.&nbsp\;IEEE Transactions on Neural Networks and Learning Systems\,&nbsp\;33(7)\, 2744-2757. https://doi.org/10.1109/TNNLS.2020.3044364\n\nAcknowledgement\nThank the ”Fonds de recherche du Québec - Nature et technologies” and ”Natural Sciences and Engineering Research Council of Canada” for funding this research. We extend our appreciation to NVIDIA for donating the GTX1080 and Titan Xp GPUs. We thank Intel for giving us access to Loihi 2 and Andreas Wild for his insights on the power/energy benchmark.
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:e0cdbf039d1d19a041ad29f13d64afcd
URL:http://cns2026.sched.com/event/e0cdbf039d1d19a041ad29f13d64afcd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260714T200000Z
DTEND:20260714T220000Z
SUMMARY:P097: How Axon Refractory Dynamics and Ionic Excitability Shape Peripheral Nerve Stimulation Responses
DESCRIPTION:Introduction\nOptimizing peripheral nerve stimulation is crucial for improving clinical outcomes in neural prosthetics and bioelectronics medicines. The effects of extracellular electrical stimulation of peripheral myelinated axons depend jointly on the stimulus amplitude\, waveform\, and frequency. This work shows that variation in the waveform configuration (monophasic and biphasic with and without interphase gaps) defines the baseline activation thresholds across monopolar and bipolar electrode configurations using the same cuff structure around the rat sciatic nerve. Further\, we explored how pulse-train frequency influences temporal entrainment limits\, subharmonic skipping patterns\, and intrinsic ionic adaptation kinetics.\n\nMethods\nWe used the McIntyre-Richardson-Grill model for a myelinated nerve fiber with modified adaptation kinetics [1]. Next\, a mesh of a rat sciatic nerve was created with GMESH [2]. We solved spatial potential maps for both monopolar and bipolar electrode cuff setups to isolate electric field effects using the Finite Element Method in FEniCSx [3]. We mapped these potential values&nbsp\;onto the axon in NEURON [4] to simulate the nerve behavior. Nodes contained fast transient Na+\, persistent Na+ driving afterdepolarization (ADP)\, and slow adaptation K+ currents. Further\, we evaluated baseline thresholds for 12 biphasic waveforms at 5 Hz to ensure intervals exceeded the refractory period. Lastly\, frequency adaptation was tested at 5\, 100\, and 1000 Hz.\n\nResults\nAt 5 Hz\, the activation threshold was observed at 250 µA for the symmetric and 100 µA for the asymmetric pulse because the bipolar configuration constrains the current spread. However\, the monopolar configuration distributes the field between source and ground\, resulting in variable thresholds ranging from 250 µA to 650 µA. The reduction in activation thresholds was observed with the introduction of an interphase gap. At 100 Hz\, bursting emerged due to ionic tug-of-war: ADP drove rapid firing until a slowly developing AHP suppressed activity\, creating 60–180 ms silent intervals. At 1000 Hz\, axons show spike-skipping (1:3 to 1:5 entrainment) due to the refractory period\, with 3–5 ms intra-burst ISIs and 200–400 ms adaptation gaps.\n\n\nDiscussion\nSymmetric cathodic-leading pulses show lower baseline thresholds due to rapid depolarization\, whereas the anodic-leading pulses show higher baseline thresholds because they hyperpolarize the membrane. However\, the interphase gap (IPG) lowers the thresholds further\, providing extra time for sodium channels to open more before the charge-reversing phase arrives. These findings show that axonal activation is a highly dynamic process shaped by spatial coupling and stimulation rates. The progression from linear scaling at 5 Hz to bursting at 100 Hz and combined bursting and subharmonic skip firing at 1000 Hz indicates that the behavior of axons under electrical stimulation is highly dependent upon the biophysical properties of ion channels.\n\nReferences\n[1] McIntyre\, C. C.\, Richardson\, A. G.\, & Grill\, W. M. (2002). Modeling the excitability of mammalian nerve fibers: Influence of afterpotentials on the recovery cycle. Journal of Neurophysiology\, 87(2)\, 995–1006. https://doi.org/10.1152/jn.00353.2001\n[2] Geuzaine\, C.\, & Remacle\, J. F. (2009). Gmsh: A three-dimensional finite element mesh generator. International Journal for Numerical Methods in Engineering\, 79(11)\, 1309–1331. https://doi.org/10.1002/nme.2579\n[3] Baratta\, I. A.\, et al. (2023). DOLFINx: The next generation FEniCS problem solving environment. Preprint. https://doi.org/10.5281/zenodo.10447666\n[4] Hines\, M. L.\, & Carnevale\, N. T. (1997). The NEURON simulation environment. Neural Computation\, 9(6)\, 1179–1209. https://doi.org/10.1162/neco.1997.9.6.1179\n\nAcknowledgement\nThis work was conducted at the Prescott Lab\, University of Calgary. Computational resources and simulation infrastructure were provided by the Prescott Lab\, with additional laboratory infrastructure and support from the Hotchkiss Brain Institute\, University of Calgary.\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:a778c6509bd4d6ad1172d7835c1b3d80
URL:http://cns2026.sched.com/event/a778c6509bd4d6ad1172d7835c1b3d80
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260714T200000Z
DTEND:20260714T220000Z
SUMMARY:P098: CompNeuroVis: Connecting Existing Computational Neuroscience Workflows to Interactive Applications
DESCRIPTION:Introduction\nUnderstanding and developing computational models of neurons relies on complementary views\, such as voltage traces\, activity mapped onto morphology\, and channel kinetics. Interactive adjustment of parameters can further expose how model mechanisms shape dynamics. Existing support spans simulator-native interfaces [1]\, model platforms [2\,3]\, network builders [4]\, and model-tuning environments [5]\, but these often require working within a particular simulator\, platform\, or model representation. CompNeuroVis addresses the gap between general-purpose plotting and specialized tools by helping researchers assemble interactive applications around workflows they already use.\n\n\nMethods\nCompNeuroVis is a Python toolkit for building interactive applications around existing models and data. In a compositional style similar to common scientific plotting libraries\, the user defines a source\, such as a runnable simulation\, Python model\, or recorded data stream\, then adds views and controls. The application is launched with a single call (Fig. 1). Views are extensible: current examples include trace plots\, morphology views\, and state diagrams\, with raster and network views in progress. Controls adjust parameters and other settings live. Each composition reduces to a shared specification of data\, views\, controls\, and synchronizing messages\, allowing the same application to run across sources and within notebooks.\n\n\nResults\nWe demonstrated the toolkit across several workflow classes. For a compartmental NEURON model with Hodgkin-Huxley dynamics\, we built an interactive application with controls that modify parameters during execution and a morphology view that color-codes spatial variables while allowing compartments to be selected for plotting [1]. We reproduced these views for an equivalent Jaxley model\, illustrating use across simulators. We also interfaced with user-defined models such as LIF neurons written in Python\, embedded a live interface within a Jupyter notebook\, and created state diagrams for Markov-based ion channel models and 3D surface plots for higher-dimensional data.\n\n\nDiscussion\nCompNeuroVis complements existing computational neuroscience interfaces by emphasizing low-friction integration with the code\, models\, data\, and simulators researchers already have. Rather than introducing a new simulator\, platform\, or model format\, it provides reusable components for assembling individualized interactive applications. Because each application reduces to a shared specification\, the same components can extend to networked interfaces and purpose-built tools\, from model editors and teaching interfaces to remote simulator dashboards and multi-simulator comparison views. This supports model development\, teaching\, debugging\, and exploratory analysis\, all of which help researchers build intuition about neural mechanisms.\n\n\nFigure 1.&nbsp\;A CompNeuroVis application built around a compartmental NEURON model of a reconstructed cell with Hodgkin-Huxley dynamics. The morphology is color-coded by a selected variable\, here membrane voltage. For a selected segment\, linked plots show membrane voltage and the gating variables m\, h\, and n. A dropdown sets the mapped variable and sliders adjust stimulus and biophysical parameters.​\n\nReferences\n\nHines\, M. L.\, & Carnevale\, N. T. (1997). The NEURON simulation environment. Neural Computation\, 9(6)\, 1179-1209. https://doi.org/10.1162/neco.1997.9.6.1179Gleeson\, P.\, et al. (2019). Open Source Brain: A collaborative resource for standardized models. Neuron\, 103(3)\, 395-411.e5.Cantarelli\, M.\, et al. (2018). Geppetto: A reusable modular open platform for neuroscience data and models. Philosophical Transactions B\, 373(1758)\, 20170380.Dura-Bernal\, S.\, et al. (2019). NetPyNE\, a tool for data-driven multiscale modeling of brain circuits. eLife\, 8\, e44494.Makarov\, R.\, Chavlis\, S.\, & Poirazi\, P. (2025). DendroTweaks: An interactive approach for unraveling dendritic dynamics. eLife\, 13\, RP103324.\n\nAcknowledgement\nI thank Ethan Irby (Research Assistant\, Nathan Kline Institute) for ideas on use cases and capability requirements\, and the open-source computational neuroscience community and simulator developers whose tools and discussions informed this work.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:f2cd24545ae840133b149a275432a768
URL:http://cns2026.sched.com/event/f2cd24545ae840133b149a275432a768
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260714T200000Z
DTEND:20260714T220000Z
SUMMARY:P099: Hierarchical Multi-Timescale learning in a mushroom body network model
DESCRIPTION:Introduction\nIn&nbsp\;Drosophila melanogaster\, associative learning&nbsp\;occurs&nbsp\;in the mushroom body\, where synapses between Kenyon&nbsp\;cells (KCs) and mushroom body output neurons (MBONs)[1]&nbsp\;are modulated by dopaminergic neurons (DANs) that convey reinforcement signals.&nbsp\;KC–MBON–DAN circuits form parallel functional units that influence behaviour [2]\, and distinct learning properties across compartments allow appetitive and aversive memories for the same stimulus to coexist [3].&nbsp\;However\,&nbsp\;the&nbsp\;interaction&nbsp\;between the compartments&nbsp\;is not fully&nbsp\;understood.&nbsp\;Here we&nbsp\;implemented&nbsp\;a&nbsp\;mushroom body network model having parallel&nbsp\;MBON&nbsp\;units&nbsp\;with different time scales and valences to investigate how the interactions between these units help in shaping different behaviours.\n\n\nMethods\nWe propose a network model consisting of a KC layer for&nbsp\;odor&nbsp\;representation\, multiple MBONs with short-term (STM) and long-term (LTM) memory\, and a set of DANs&nbsp\;representing&nbsp\;unconditioned stimuli.&nbsp\;Synaptic weight between KC and MBON depends on the relative timing between KC and DAN&nbsp\;activity.&nbsp\;We have implemented cross valence&nbsp\;inhibitory modulation and&nbsp\;hierarchical interaction between LTM and STM\, where strong LTM activity can&nbsp\;positively influence STM compartment activity. Behavioural readout is&nbsp\;determined&nbsp\;by the relative firing rates of the MBON population encoding opposite valences.\n\n\nResults\nThe model could reproduce the core features&nbsp\;of associative learning including first order conditioning. Parallel MBONs allow associations with opposite valence to coexist for the same stimuli. Due to the presence of cross valence inhibitions\, the model&nbsp\;can exhibit&nbsp\;valence shifting during sequential experiences&nbsp\;of opposing reinforcements or&nbsp\;when relative influence of other compartment changes over time. Hierarchical LTM-STM interactions further enable second order conditioning\,&nbsp\;producing a short-term&nbsp\;memory.\n\n\nDiscussion\nOur results&nbsp\;indicate&nbsp\;that interactions&nbsp\;between&nbsp\;parallel memory units can produce flexible&nbsp\;behaviour&nbsp\;even with a&nbsp\;relatively simple&nbsp\;plasticity&nbsp\;rule. The&nbsp\;hierarchical interaction between&nbsp\;memory units of different&nbsp\;timescales&nbsp\;allows stable long-term memories&nbsp\;to influence short term memories\, which helps in adapting to a dynamic environment. These results&nbsp\;highlight how&nbsp\;network architecture&nbsp\;of the&nbsp\;mushroom body&nbsp\;can&nbsp\;support flexible yet stable&nbsp\;behaviours.&nbsp\;\n\n\n\n\nReferences\nWaddell\, S. (2013). Reinforcement signalling in Drosophila\; dopamine does it all after all. Current Opinion in Neurobiology\, 23(3)\, 324–329. https://doi.org/10.1016/j.conb.2013.01.005Aso\, Y.\, Hattori\, D.\, Yu\, Y.\, Johnston\, R. M.\, Iyer\, N. A.\, Ngo\, T.\, Dionne\, H.\, Abbott\, L.\, Axel\, R.\, Tanimoto\, H.\, & Rubin\, G. M. (2014). The neuronal architecture of the mushroom body provides a logic for associative learning.&nbsp\;eLife\, 3\, e04577.&nbsp\;https://doi.org/10.7554/elife.04577&nbsp\;Aso\, Y.\, & Rubin\, G. M. (2016). Dopaminergic neurons write and update memories with cell-type-specific rules. ELife\, 5. https://doi.org/10.7554/eLife.16135&nbsp\;\nAcknowledgement\nThis work is supported by the Centre for High Impact Neuroscience and Translational Applications (CHINTA)\, TCG CREST.&nbsp\;I sincerely thank Dr. C. Sivaraju for his valuable discussions and encouragement.&nbsp\;\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:ae1ff7f6f28c01e382d3991e45f6a869
URL:http://cns2026.sched.com/event/ae1ff7f6f28c01e382d3991e45f6a869
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SUMMARY:P100: JARDESIGNER: Installation-free in-browser modeling of multiscale models with signaling and conductances in multicompartment neurons.
DESCRIPTION:Introduction\nNeuronal model building remains a challenge despite highly capable simulator platforms [1\,2]. This is even more the case for multiscale models spanning molecular to electrophysiological detail\, which are increasingly relevant for modeling plasticity\, neuromodulation and long time-scale neuronal dynamics. Recent GUIs such as DendroTweaks have simplified electrophysiology modeling [3]\, but GUIs for multiscale models are scarce. JARDESIGNER is the Javascript App for Reaction-Diffusion and Electrical SIGnaling in NEuRons. It provides three things: a no-installation GUI for building and running models\, a JSON file format for defining multiscale models\, and a standalone Python library for building and running the models on the MOOSE simulator.\n\nMethods\nThe Jardesigner GUI is a client-server system. The GUI is implemented in Javascript in the REACT framework on the client browser\, and the server backend is coded in Python. It uses the jardesigner Python library to build models running on the C++ codebase of MOOSE to efficiently perform the computations. There is a publicly hosted Jardesigner server at https://www.jardesigner.org/. The Jardesigner GUI does not use cookies or track users\, and any files uploaded for model building are deleted as soon as the user quits a modeling session. The complete client-server package can be locally installed using pip install.\n\nResults\nJardesigner enables rapid building of production multiscale neuronal simulations. Successive menu boxes specify morphology\, spine distributions\, passive and active properties\, signaling pathways\, and mappings between ephys and signaling entities (Fig. 1). Users load neuronal morphologies\, channel mechanisms\, and signaling kinetic specifications from standard databases and assemble them in the GUI. Additional menu boxes allow one to plot graphs\, set up 3-D animations\, and save simulation results. As the simulation is assembled\, a 3-D view of the cell grows and provides icons for mechanisms and display elements. It is trivially easy to swap out one morphology for another\, even as all the other components of the model are retained.\n\nDiscussion\nJardesigner makes it easy to build and run complex\, multiscale models entirely in the GUI\, using predefined morphology\, channel\, and signaling building blocks. It is particularly well suited for teaching\, and has been used effectively by students with no previous modeling experience. It is also in production use to build and test multiscale models for research simulations. Development directions include video tutorials and better integration with online databases. We encourage user feedback.\n\nFigure 1.&nbsp\;Screenshot of Jardesigner GUI. The menu options are in the blue bar above\, and the Channel Menu box is open to the left. A snapshot of the current simulation is presented in the 3D display to the right.​\n\nReferences\n[1] Hines\, M. (2009). NEURON and Python. Frontiers in Neuroinformatics\, 3. doi: 10.3389/neuro.11.001.2009\n[2] Ray\, S.\, Bhalla\, U.S. (2008). PyMOOSE: interoperable scripting in Python for MOOSE. Frontiers in Neuroinformatics\, 2\, 365 https://doi.org/10.3389/neuro.11.006.2008\n[3] Makarov\, R.\, Chavlis\, S.\, Poirazi\, P (2025). DendroTweaks\, an interactive approach for unraveling dendritic dynamics. eLife\, https://doi.org/10.7554/eLife.103324.3\n\nAcknowledgement\nThe development of Jardesigner was supported by the Kavli Foundation through the EOSS Grant Cycle 6. USB receives research support from NCBS-TIFR through the Department of Atomic Energy\, Government of India\, under Project Identification No. RTI 4006. SR is supported by CHINTA and IAI under TCG CREST.
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:177f10e287e9cefdcf3126ee7bb2e1bb
URL:http://cns2026.sched.com/event/177f10e287e9cefdcf3126ee7bb2e1bb
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SUMMARY:P101: BrainSymphony Reveals Psilocybin-Induced Network Reorganization
DESCRIPTION:Introduction\nfMRI foundation models have grown rapidly in size [1-2]\, but scale may be suboptimal for neuroimaging given domain constraints and interpretability needs. BrainSymphony instead embeds neurobiological priors to build a lightweight\, parameter-efficient\, multimodal model capturing spatiotemporal BOLD dynamics and diffusion-MRI connectivity. We test out-of-distribution generalization on PsiConnect (62 participants\; pre/post psilocybin\; rest\, meditation\, music\, movie) and relate model-derived dynamics to MEQ-30 mystical-type experience ratings. BrainSymphony reconstructs unseen fMRI time series with high fidelity and exposes fine-grained psychedelic network reorganization beyond classical functional connectivity.\n\nMethods\nBrainSymphony uses three fMRI encoders: an ROI-wise Spatial Transformer with mixed positional embeddings (including cortex-gradient priors)\, a Temporal Transformer with sinusoidal time embeddings\, and a 1D convolutional stream for short-range transients. Encoder outputs are fused by a Perceiver module. We applied a frozen model pretrained on HCP/HCP-Aging to PsiConnect (baseline and psilocybin\; rest\, meditation\, music\, movie). Reconstruction was quantified by R²\, Pearson r\, and MAE against ROI-shuffled controls. Directed influence matrices were computed from Perceiver attention (incoming/outgoing) and used to decode context and to characterize psilocybin–baseline reorganization\, including modulation by experience intensity.\n\nResults\nBrainSymphony reconstructed unseen psychedelic fMRI with high fidelity\; R² and Pearson r exceeded shuffled controls (Fig. 1). Directed influence matrices decoded rest/meditation/music/movie at baseline (~0.64) and remained above chance under psilocybin (~0.46\; chance 0.25)\, consistent with reduced modular boundaries and increased integration. Psilocybin increased outgoing influence in DMN\, Control\, and Visual networks\, suggesting reduced DMN autonomy. Incoming influence highlighted Visual cortex as a dominant driver even in eyes-closed states\, with limbic/salience drivers strongest in affective contexts. These effects scaled with Mystical Experience Questionnaire intensity with conventional functional connectivity failing to identify them.\n\nDiscussion\nCompact\, domain-informed foundation models can be predictive and mechanistically useful. BrainSymphony’s attention-derived directed influence reveals psilocybin-driven redistribution of large-scale communication that tracks behavioral context and subjective intensity of the experience. Visual territories emerge as consistent drivers even with closed eyes\, aligning with internally generated imagery\; limbic circuits amplify during emotionally rich contexts\; DMN/Control/ systems show condition-specific increases in being influenced\, reflecting decreased segregation and increased integration. BrainSymphony demonstrates that efficiency and interpretability can coexist with strong generalization.\n\nFigure 1.&nbsp\;BrainSymphony reconstruction and attention-based reorganization. (a) Paired dots: real vs permuted ROI series across conditions\; higher R²\,r\, lower MAE. (b) Circos: Admin–Baseline attention Δ (top 500 edges) colored by source network\; inner track = total outgoing. (c) Network-mean incoming attention Δ. (d) High vs Low MEQ receptive-attention maps plus inter-network Δ matrices.​\n\nReferences\n1.&nbsp\;Caro\, J. O.\, Fonseca\, A. H. D. O.\, Averill\, C.\, Rizvi\, S. A.\, Rosati\, M.\, Cross\, J. L.\, ... & van Dijk\, D. (2023). BrainLM: A foundation model for brain activity recordings.&nbsp\;BioRxiv\, 2023-09.\n2.&nbsp\;Dong\, Z.\, Li\, R.\, Wu\, Y.\, Nguyen\, T. T.\, Chong\, J.\, Ji\, F.\, ... & Zhou\, J. H. (2024). Brain-jepa: Brain dynamics foundation model with gradient positioning and spatiotemporal masking.&nbsp\;Advances in Neural Information Processing Systems\,&nbsp\;37\, 86048-86073.\n3. Stoliker\, D.\, Novelli\, L.\, Khajehnejad\, M.\, Biabani\, M.\, Barta\, T.\, Greaves\, M. D.\, ... & Razi\, A. (2025). Psychedelics align brain activity with context. bioRxiv\, 2025-03.\n\nAcknowledgement\nA.R. is affiliated with The Wellcome Centre for Human Neuroimaging\, supported by core funding from Wellcome [203147/Z/16/Z]. A.R. is a CIFAR Azrieli Global Scholar in the Brain\, Mind & Consciousness Program.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:dff56b2e8bdbf1b84a7f47d1beb6f951
URL:http://cns2026.sched.com/event/dff56b2e8bdbf1b84a7f47d1beb6f951
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SUMMARY:P102: Long-timescale Plasticity Mediated by ER-Dependent Synaptic Stabilization
DESCRIPTION:Introduction\nBridging the short-timescale of classic Hebbian learning and long-timescale of behavioral memory remains a challenge in the study of synaptic plasticity. The synaptic tag-and-capture theory proposes that activity-dependent molecular markers enable a state of synaptic stability [1]\, and it has been shown theoretically that multiple stability states enhance memory capacity [2]. Trafficking of the endoplasmic reticulum (ER) in and out of dendritic spines could viably serve as such a “tag” [3]\, but its effects on plasticity have yet to be studied in silico. We hypothesize that ER-mediated synaptic stabilization extends memory capacity and supports stable learning in neuronal microcircuits through activity-dependent modulation of plasticity.\n\nMethods\nWe model ER trafficking in dendritic spines using a piecewise deterministic Markov process with three synaptic states: ER−\, ER+\, and ER stable. Potentiation increases the probability of ER entry to a given synapse while depression promotes ER exit. Synaptic plasticity follows a calcium-based rule [4] where weight changes decay to baseline within minutes. However\, ER+ synapses can transition to ER stable when activity elevates calcium above a threshold\, resetting the baseline weight and enabling potentiation to persist over long timescales. The ER model is incorporated into recurrent networks of leaky integrate-and-fire neurons to test its impact on learning and memory through the lens of neuronal assemblies [5].&nbsp\;\n\nResults\nIn a single synapse subject to high-frequency stimulation\, potentiation from the calcium-dependent plasticity rule will decay on the order of minutes without the presence of ER (Figure 1A). A second high-frequency stimulus first successfully draws the ER into the synapse and subsequently triggers ER stabilization\, preserving synaptic potentiation over longer timescales (Figure 1B-C). We expect the differences in these timescales to play a significant role in learning and memory formation in in silico recurrent neuronal microcircuits.\n\nDiscussion\nOur model of stochastic ER recruitment stabilizes the effects of potentiation in individual synapses in a biologically informed manner [3]. By extending the timescale of potentiation beyond that predicted by calcium-dependent plasticity alone\, we expect to similarly affect the timescale of learning and memory in in silico microcircuits. Therefore\, our model will generate experimentally testable predictions regarding the effects of ER-mediated stabilization on learning dynamics and long-term memory storage. These conclusions will be augmented by constraining the model parameters for ER visitation with experimentally measured dendritic ER distributions obtained from electron microscopy studies to make brain region specific predictions.\n\nFigure 1. Stochastic activity-driven ER entry and stabilization preserve calcium-dependent plasticity over long timescales. (A) Identical high-frequency pre/post spike trains (pink) produce synaptic weight changes\; the first stimulation fails to trigger ER entry\, while the second induces ER entry (green) followed by stabilization (purple). (B–C) Insets show calcium (left) and weight (right) dynamics​References\n1.&nbsp\;&nbsp\;&nbsp\; Redondo et al. (2011). Making memories last: the synaptic tagging and capture hypothesis.&nbsp\;Nat Rev Neurosci&nbsp\;12\, 17–30.\n2.&nbsp\;&nbsp\;&nbsp\; &nbsp\;Fusi et al. (2005). Cascade models of synaptically stored memories.&nbsp\;Neuron\,&nbsp\;45(4)\, 599–611.\n3.&nbsp\;&nbsp\;&nbsp\; Dittmer et al. (2024). L-type Ca2+&nbsp\;channel activation of STIM1-Orai1 signaling remodels the dendritic spine ER to maintain long-term structural plasticity.&nbsp\;Proc Natl Acad Sci USA.\,&nbsp\;121(35)\, e2407324121. \n4.&nbsp\;&nbsp\;&nbsp\; Moldwin et al. (2025). A generalized mathematical framework for the calcium control hypothesis describes weight-dependent synaptic plasticity.&nbsp\;J Comput Neurosci&nbsp\;53\, 333–357. \n5.&nbsp\;&nbsp\;&nbsp\; &nbsp\;Miehl et al. (2023). Formation and computational implications of assemblies in neural circuits.&nbsp\;J physiol\,&nbsp\;601(15)\, 3071–3090. \n\nAcknowledgement\nNone.
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:8a13f676bc178e640605cca6c56a0cc1
URL:http://cns2026.sched.com/event/8a13f676bc178e640605cca6c56a0cc1
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SUMMARY:P103: Pathological cortical oscillations disrupted by the cholinergic response to vagus nerve stimulation
DESCRIPTION:Introduction\nWhile vagus nerve stimulation (VNS) coupled with motor rehabilitation significantly improves post-stroke recovery [1]\, its mechanism of action remains poorly understood. Preclinical studies indicate that VNS enhances motor learning when stimulation is precisely paired with task success [2\,3]\, with cholinergic neuromodulation necessary for these effects [4]. These findings collectively suggest that rapid cholinergic signaling may be pivotal in stroke rehabilitation\, although this response to VNS has not been well characterized experimentally.\nWe hypothesize that the immediate cholinergic response to VNS produces rapid changes in cortical oscillatory dynamics through modulation of the muscarinic M-current in cortical neurons.\n\nMethods\nWe model the brainstem circuitry connecting the vagus nerve to the cortex with populations of quadratic integrate and fire (Izhikevich) neurons: the nodose ganglion of the vagus nerve\, the nucleus of the solitary tract\, the locus coeruleus\, and cholinergic neurons of the basal forebrain projecting to the cortex (Fig. 1A). Each neuronal population is parameterized using firing rate and adaptation properties fit to published electrophysiological data.\nThe predicted VNS-triggered cholinergic output derived from basal forebrain neuronal spiking modulates an excitatory-inhibitory (E-I) cortical microcircuit of Hodgkin-Huxley neurons through a conductance-based model of the muscarinic M-current (Fig. 1B).\n\nResults\nPreclinical [2] and preliminary clinical EEG recordings show that VNS immediately reduces cortical gamma power\, reflecting a decrease in synchronized spiking activity. Our model generates a dynamic cholinergic output which in turn modulates the M-current within an in silico cortical network [5]. The resulting synaptic activity is used to generate a pseudo-EEG signal that can be compared directly to the spectral changes observed in clinical recordings (Fig. 1B). Expanding upon recent in silico work showing that dynamically increasing acetylcholine concentrations desynchronize cortical microcircuits dependent upon the rate of modulation [6]\, we determine whether the specific cholinergic response to VNS can account for VNS’s effect on EEG.\n\nDiscussion\nThe combination of an experimentally constrained brainstem model and a cortical microcircuit subject to cholinergic neuromodulation allows us to test whether rapid cholinergic signaling can account for the immediate decrease in cortical gamma power observed following VNS. This approach allows for explorations of how stimulation parameters—such as inter-stimulation interval\, pulse train duration\, and stimulation intensity—influence cholinergic output and cortical dynamics beyond what is currently done clinically. In doing so\, the model may help identify stimulation paradigms that maximize beneficial neuromodulatory effects\, providing mechanistic insight into how VNS protocols can be optimized to improve stroke rehabilitation.\n\nFigure 1.&nbsp\;(A) The circuitry between the vagus nerve and the basal forebrain is modeled using quadratic integrate-and-fire (Izhikevich) neurons fitted to experimental data. Representative voltage responses to a 50 pA injection are shown. (B) The output of the basal forebrain in (A) is used to modulate ACh-sensitive K+ channels in an E–I network. Synaptic activity within this network is used to compute an EEG​References\n1.&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Dawson\, J.\, et al. (2021). Vagus nerve stimulation paired with rehabilitation for upper limb motor function... The Lancet\, 397(10284)\, 1545–1553.\n2.&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Engineer\, N. D.\, et al. (2011). Reversing pathological neural activity... Nature\, 470(7332)\, 101–104. https://doi.org/10.1038/nature09656\n3.&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Hays\, S. A.\, et al. (2014). The timing and amount of vagus nerve stimulation... Neuroreport\, 25(9)\, 676–682.\n4.&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Bowles\, S.\, et al. (2022). Vagus nerve stimulation drives selective... Neuron\, 110(17)\, 2867–2885.e7. \n5.&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Stiefel\, K. M.\, et al. (2009). The effects of cholinergic neuromodulation... Journal of Computational Neuroscience\, 26(2)\, 289–301. \n6.&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Pandian\, S.\, & Rich\, S. (2025). Dynamic cholinergic signaling differentially... (p. 2025.06.20.660675). bioRxiv
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:51d651d753d9d1fd31d2ce09cc65f7e0
URL:http://cns2026.sched.com/event/51d651d753d9d1fd31d2ce09cc65f7e0
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SUMMARY:P104: Characterization of nontrivial voltage noise in electrosensory pyramidal neurons
DESCRIPTION:Introduction\nThe stochastic flickering of ion channels is known to cause ongoing membrane potential fluctuations in neurons [1]. This channel noise is often considered negligible when compared to synaptic noise\, yet it can shape the integrative properties of neurons [2]. Here\, we show that valuable information on a neuron's intrinsic dynamics can be extracted by closely inspecting recordings of spontaneous membrane potential fluctuations in the absence of synaptic input.\n\nMethods\nWe offer a reanalysis of previously published data [3] that consists of in vitro recordings from primary electrosensory pyramidal neurons in weakly electric fish under complete synaptic blockade. Raw voltage traces are segmented based on the applied holding currents\, and each segment is decomposed into a fast and slow component. These components are analyzed with a suite of techniques\, including wavelet transform\, principal component analysis\, Hilbert transform\, and empirical mode decomposition.&nbsp\;\n\n\nResults\nOur analyses reveal an intrinsic noise structure that is richer than what could be expected based on usual assumptions pertaining to intrinsic voltage noise: we identify rapid\, small-amplitude\, shot noise-like events\, and we quantify how their rate and amplitude are modulated by slower\, large-amplitude fluctuations. This cross-relation is evidence that\, at the single-neuron level\, membrane potential dynamics can exhibit a form of phase-amplitude coupling. We also investigate the appearance of fast\, intermittent subthreshold oscillations and investigate whether they are manifestation of stochastic linear dynamics\, possibly with time-varying parameters.\n\nDiscussion\nTo our knowledge\, this is the first study to explore and quantify a form of cross-frequency phase-amplitude coupling within spontaneous voltage fluctuations in single neurons. Collectively\, our results suggest that spontaneous voltage noise at the single-neuron level can be nontrivial and should be closely investigated to fully contextualize the arrival of synaptic input. Given the richness of the intrinsic noise structure that we uncover\, we lend concrete support to the notion that “the precise impact of synaptic noise can be evaluated only once channel noise is understood and quantified” [2].\n\n\nReferences\n1. Faisal\, A. A.\, Selen\, L. P. J.\, & Wolpert\, D. M. (2008). Noise in the nervous system.&nbsp\;Nature Reviews Neuroscience\, 9\, 292–303.\n2. White\, J. A.\, Rubinstein\, J. T.\, & Kay\, A. R. (2000). Channel noise in neurons.&nbsp\;Trends in Neurosciences\, 23\, 131–137.\n3. Marcoux\, C. M.\, Clarke\, S. E.\, Nesse\, W. H.\, Longtin\, A.\, & Maler\, L. (2016). Balanced ionotropic receptor dynamics support signal estimation via voltage-dependent membrane noise.&nbsp\;Journal of Neurophysiology\, 115\, 530–545.\n\nAcknowledgement\nThis work was funded by the Natural Sciences and Engineering Research Council of Canada under Grant No. RGPIN-2022-0 531 4.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:1758064dbd56548947c4c6cac0a6fd5e
URL:http://cns2026.sched.com/event/1758064dbd56548947c4c6cac0a6fd5e
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SUMMARY:P105: Gamma Oscillations in Conductance-Based QIF Networks: A Three-Stage Progression and Its Limits
DESCRIPTION:Introduction\nGamma oscillations in cortical circuits are often modeled through excitatory-inhibitory (E-I) interactions underlying Pyramidal-Interneuron Network Gamma (PING) or through inhibitory interactions underlying Interneuron Network Gamma (ING). Quadratic integrate-and-fire (QIF) neuron models are widely used in modeling such dynamics because they support a direct correspondence between spiking-network simulations and tractable mean-field reductions. However\, many QIF studies on PING oscillations mix current and conductance drive or use non-physiological reversal potentials\, making it unclear whether conductance-based QIF E-I networks can generate biologically realistic gamma under physiological constraints.\n\nMethods\nWe studied an all-to-all conductance-based QIF E-I spiking network together with a corresponding mean-field firing-rate model. To isolate conductance-induced effects\, we varied synaptic conductances and excitatory/inhibitory reversal potentials without additive direct current injection. Parameters were restricted to biologically realistic reversal-potential ranges. Because the resulting network dynamics are analytically difficult to interpret directly\, we also analyzed single-neuron QIF responses under different conductance-based inputs and reversal potentials\, using their analytical solutions to gain insight into how these parameters shape emergent network behavior.\n\nResults\nThe conductance-based QIF E-I network exhibited gamma-range PING-like oscillations in some parameter regimes. As external drive to the excitatory population increased\, the network exhibited a three-stage progression: a PING-like regime\, a weak-ING-like intermediate regime marked by suppressed excitatory oscillations and persistent weak inhibitory oscillations\, and population quenching at high drive (Fig.1). Under the same physiological constraints\, both the spiking network and the mean-field model also displayed systematic non-biological features\, including excitatory-cell doublets\, weak inhibitory post-hyperpolarization currents\, and overshooting inhibitory synaptic currents during depolarization.\n\nDiscussion\nThese results reveal a three-stage dynamical progression in conductance-based QIF E-I networks under physiological synaptic constraints. This progression provides a compact description of how increasing drive reshapes network dynamics. At the same time\, the observed anomalous firing patterns and synaptic-current dynamics reveal important limitations under biologically realistic synaptic constraints and suggest that intrinsic properties of the QIF formalism contribute substantially to these deviations\, beyond generic E-I network mechanisms alone. Our results clarify when QIF models succeed or fail in capturing realistic gamma dynamics and motivate refinement of reduced spiking models for studying cortical oscillations.\n\nFigure 1.&nbsp\;E-I Network dynamics as a function of external excitatory conductance drive in high input regime​\n\nReferences\n\nCoombes\, S.\, & Byrne\, Á. (2019). Next generation neural mass models. In F. Corinto & A. Torcini (Eds.)\, Nonlinear Dynamics in Computational Neuroscience (pp. 1-16). Springer. https://doi.org/10.1007/978-3-319-71048-8_1Ermentrout\, B. (1996). Type I membranes\, phase resetting curves\, and synchrony. Neural Computation\, 8(5)\, 979-1001. https://doi.org/10.1162/neco.1996.8.5.979Keeley\, S.\, Byrne\, Á.\, Fenton\, A.\, & Rinzel\, J. (2019). Firing rate models for gamma oscillations. Journal of Neurophysiology\, 121(6)\, 2181-2190. https://doi.org/10.1152/jn.00741.2018Montbrió\, E.\, Pazó\, D.\, & Roxin\, A. (2015). Macroscopic description for networks of spiking neurons. Physical Review X\, 5(2)\, 021028. https://doi.org/10.1103/PhysRevX.5.021028\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:1835077a22888318f301ad5b5e0de32d
URL:http://cns2026.sched.com/event/1835077a22888318f301ad5b5e0de32d
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SUMMARY:P106: Controlling the Speed–Accuracy Trade-Off in Brain–Computer Interfaces
DESCRIPTION:Introduction\nBrain–computer interfaces (BCIs) decode brain signals into device commands [1]. Their performance is limited by a low signal-to-noise ratio\, leading to a speed–accuracy trade-off: increasing accuracy through trial averaging requires more trials and reduces communication speed [2]. Existing metrics\, such as the Information Transfer Rate (ITR) or BCI-Utility [3]\, obscure how accuracy depends on the number of trials — potentially introducing biases and limiting explainability. We propose a framework that separates and optimizes speed and accuracy\, enabling explicit control of the trade-off and improving user- and experiment-specific adaptation.\n\nMethods\nThe framework quantifies BCI speed and accuracy through two measures: Gain (relative speed improvement) and Conservation (relative accuracy preservation)\, defined with respect to a baseline BCI. These are combined into a trade-off equation termed Gain–Cons Balance (GCB)\, αGain + (1 − α)Cons\, where α controls the desired balance. The GCB specifies the target trade-off\, while an early-stopping strategy adjusts the number of trials to satisfy the selected α. In this way\, the early-stop implements the policy dictated by the GCB. The approach was validated on 63 subjects using two P300 paradigms\, three classifiers\, and three stopping criteria within a nested leave-one-session-out cross-validation procedure.\n\nResults\nOptimization strategies produced distinct BCI behaviors (Fig. 1). The ITR behaved similarly to GCB(α = 0.75)\, favoring faster decisions with lower accuracies\, whereas GCB(α = 0.25) prioritized accuracy at the cost of more trials. The balanced configuration (α = 0.5) achieved high accuracy with moderate speed. This trend was consistent across paradigms\, subjects\, sessions\, classifiers\, and early-stopping methods. These findings demonstrate that tuning α in the Gain–Cons Balance explicitly controls the speed–accuracy trade-off: selecting a desired accuracy determines the required number of trials\, and vice versa.\n\nDiscussion\nThe proposed Gain–Cons Balance framework enables explicit control of the speed–accuracy trade-off across Rapid Serial Visual Presentation and Row–Column Paradigm P300 datasets. By tuning the parameter α\, the trade-off becomes a controllable design variable rather than an implicit constraint. The framework supports conditional analysis of expected accuracy or required trials\, facilitates subject-level performance prediction\, and reveals metric biases — particularly ITR’s preference for speed. Overall\, the Gain–Cons Balance provides a general and interpretable tool to optimize and compare BCIs across paradigms\, users\, sessions\, classifiers\, and early-stopping strategies.\n\nFigure 1.&nbsp\;Required trials and obtained accuracies across classifiers\, early-stopping strategies\, and optimization methods for Hoffmann et. al. Rapid Serial Visual Presentation dataset [1]. Points show mean performance under leave-one-session-out validation. The fitted line illustrates the speed–accuracy trade-off.​\n\nReferences\n[1] Hoffmann\,U.\, Vesin\,J.-M.\, Ebrahimi\,T.\, & Diserens\,K.(2008).An efficient P300-based brain–computer interface for disabled subjects. Journal of Neuroscience Methods\, 167(1):115–125\, https://doi.org/10.1016/j.jneumeth.2007.03.005.\n[2] Schreuder\,M.\, Höhne\,J.\, Blankertz\,B.\, Haufe\,S.\, Dickhaus\,T.\, & Tangermann\,M.(2013).Optimizing event-related potential based brain-computer interfaces: A systematic evaluation of dynamic stopping methods. Journal of Neural Engineering\, 10(3):036025\, https://doi.org/10.1088/1741-2560/10/3/036025.\n[3] Yuan\,P.\, Gao\,X.\, Allison\,B.\, Wang\,Y.\, Bin\,G.\, & Gao\,S.(2013).A study of the existing problems of estimating the information transfer rate in online brain-computer interfaces. Journal of Neural Engineering\, 10(2):026014\, https://doi.org/10.1088/1741-2560/10/2/026014.\n\nAcknowledgement\nThis work was supported by the Predoctoral Research Grants of the Universidad Autónoma de Madrid (FPI-UAM) and by PID2023-149669NB-I00 (MCIN/AEI and ERDF – “A way of making Europe”).
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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URL:http://cns2026.sched.com/event/5c087f5eea2e7fab2c9ac2a863deac88
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SUMMARY:P107: Resonance-Driven Phase Locking and Temporal Coding in CA1 Pyramidal Neurons
DESCRIPTION:Introduction\n\n\n\nNeural oscillations in the hippocampus at theta and gamma bands are believed to support temporal coding in memory and learning [2]. We hypothesize that intrinsic theta resonance in hippocampal CA1 pyramidal neurons facilitate frequency-selective enhancement and phase locking required for precise phase coding. Through computational (biophysically plausible) network models and Simulation Based Inference (SBI) [1]\, we examine how intrinsic and synaptic dynamics&nbsp\; influence the transition between firing patterns which shape oscillatory synchronization. Our results link biophysical parameters to phase locked firing in the theta and gamma frequency ranges to provide mechanistic insight into underlying temporal coding in hippocampal circuits.&nbsp\;\n\n\n\nMethods\n\n\n\nWe model a simplified hippocampal network of neurons consisting of one excitatory population and two inhibitory populations of neurons. The inhibitory populations consist of slow spiking and fast spiking interneurons. We include key ionic currents (e.g. H-\, M-\, persistent Na+) and synaptic connections to capture spiking mode transitions and resonant dynamics. To estimate the underlying biophysical parameters we apply SBI\, a Bayesian approach used to infer parameter distributions from voltage data using neural density estimators. This method allows us to capture the underlying mechanisms governing the formation of neuronal oscillations\, degeneracies\, and temporal coding. \n\n\nResults\n\n\n\nOur simulations show that intrinsic theta resonant currents are sufficient for enhancing spike timing and frequency selective phase locking in CA1 neurons. Phase locking analysis reveals a pronounced peak in phase locking value (PLV) near theta resonance\, which is diminished with the removal of resonant currents. In addition our results demonstrate that intrinsic theta resonance is able to recruit fast spiking inhibitory neurons in order to modulate phase locking in the gamma ranges. Finally\, our results show that by tuning resonant currents the phase at which neurons lock to can be modulated.\n\n\n\nDiscussion\n\n\n\nSynaptic connections are capable of setting temporal windows where excitatory neurons can fire which on its own can produce weak phase locking. However\, precise phase coding requires millisecond-scale spike timing that synaptic connections on their own cannot account for due to variability and noise. Intrinsic resonance is therefore required to stabilize spike timing\, amplify responses at preferred frequencies\, and promote robustness in frequency and phase specific domains. By tuning intrinsic resonant currents we are able to show robust phase locking in both theta and gamma ranges. Therefore\, network connection sets firing windows\, while resonance determines spike strength and phase by adaptively recruiting network activity.&nbsp\; \n\n\n\nReferences\n\nReferences&nbsp\;\nBoelts\, J.\, et al (2022). Flexible and efficient simulation-based inference for models of decision-making. eLife\, 11\, e77220. https://doi.org/10.7554/eLife.77220\nLowet\, E.\, et al. (2023). Theta and gamma rhythmic coding through two spike output modes in the hippocampus during spatial navigation. Cell Reports\, 42(8)\, 112906. https://doi.org/10.1016/j.celrep.2023.112906\nRotstein\, H. G.\, et al (2005). Slow and fast inhibition and an H-current interact to create a theta rhythm in a model of CA1 interneuron network. Journal of Neurophysiology\, 94(2)\, 1509–1518. https://doi.org/10.1152/jn.00957.2004\n\n\n\nAcknowledgement\nAcknowledgements: NSF IOS-2002863 (HGR)\n\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:a26c2c9bf57fb20be1243ee34e8aaca6
URL:http://cns2026.sched.com/event/a26c2c9bf57fb20be1243ee34e8aaca6
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SUMMARY:P108: Computational Phenotyping of Neurotrauma Using High-Throughput Actigraphy-Derived Sleep Signatures
DESCRIPTION:Introduction\nSleep disturbance is a common consequence of neurotrauma\, including traumatic brain injury (TBI) and spinal cord injury (SCI)\, yet objective biomarkers capable of distinguishing injury type remain limited. Wearable actigraphy generates high-dimensional physiological time-series data that may contain latent signatures of injury-related sleep disruption. Although sleep disturbances also occur following severe orthopedic injury (SOI)\, it remains unclear whether computational analysis of actigraphy-derived sleep architecture can detect injury-specific phenotypes. Here\, we tested whether supervised machine learning models could classify injury type from wrist actigraphy-derived sleep signatures.\n\n\nMethods\nContinuous wrist actigraphy was collected from 61 inpatients (TBI n = 33\, SCI n = 12\, SOI n = 15). Raw actigraphy time-series were processed using a custom Python-based computational pipeline that performed automated preprocessing\, circadian segmentation and high-throughput extraction of sleep–wake metrics. This workflow produced 24 features per observation\, including sleep efficiency\, fragmentation\, and bout structure. Five engineered composite features capturing circadian disruption and sleep consolidation yielded a 31-feature representation of sleep dynamics. Seven supervised machine learning classifiers were evaluated using stratified five-fold cross-validation with performance assessed by AUC and average precision. (Fig. 1)\n\n\nResults\nA stacking ensemble method achieved the strongest performance across both classification tasks. Discrimination was strong for TBI versus SOI (AUC=0.825±0.033\; average precision=0.896) and SCI versus TBI (AUC=0.900±0.054\; average precision=0.975). Nighttime sleep efficiency emerged as the most informative feature\, suggesting that nocturnal sleep consolidation carries strong diagnostic value across injury groups.\n\n\nDiscussion\nThese findings demonstrate injury-specific sleep signatures and show that actigraphy-derived sleep phenotyping can distinguish central nervous system injury from peripheral trauma using scalable\, noninvasive monitoring. This computational framework may enable objective clinical stratification in neurotrauma and provides a foundation for future machine learning approaches to track recovery trajectories and identify sleep-based biomarkers of injury burden.\n\nFigure 1.&nbsp\;Actigraphy-based sleep feature pipeline and machine learning model performance for TBI classification.​\n\nReferences\n1. Courville\, E.\, Kazim\, S. F.\, Vellek\, J.\, Tarawneh\, O.\, Stack\, J.\, Roster\, K.\, Roy\, J.\, Schmidt\, M.\, & Bowers\, C. (2023). Machine learning algorithms for predicting outcomes of traumatic brain injury: A systematic review and meta-analysis. Surgical Neurology International\, 14\, 262. https://doi.org/10.25259/SNI_312_2023\n2. Tunthanathip\, T.\, & Oearsakul\, T. (2021). Application of machine learning to predict the outcome of pediatric traumatic brain injury. Chinese Journal of Traumatology\, 24(6)\, 350–355. https://doi.org/10.1016/j.cjtee.2021.06.003\n&nbsp\;\n\nAcknowledgement\nAcknowledgements: A.L. was supported by the National Institutes of Health Training Program in Sleep and Circadian Biology (T32HL149646). Additional thanks to the Sleep\, Inflammation\, and Neuropathology Lab members for the support.
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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URL:http://cns2026.sched.com/event/4c614277d31af7c39a58b81f81687a90
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SUMMARY:P109: Imperfectly synchronous dynamics of gamma rhythms and its response to network inputs
DESCRIPTION:Introduction\nTemporal patterning of neural synchronization was observed to be related to symptoms of multiple neurological and neuropsychiatric disorders. In particular\, alterations in the patterns of intermittent synchrony in multiple spectral bands (including gamma band) were found in autism spectrum disorder (ASD)\, Alzheimer’s disease (AD)\, and frontotemporal dementia (FTD) as demonstrated in recent electrophysiological studies with resting state EEG recordings [1\,2\,3]. Computational models may provide mechanistic insights into how synaptic parameters and network properties shape properties of intermittent synchronization dynamics and might underlie the neural dynamics observed experimentally in pathological conditions.\n\n\nMethods\nWe used a conductance-based models of pyramidal neurons and interneuron to simulate systems of synaptically connected randomly organized networks of excitatory and inhibitory neurons\, that exhibits gamma-band activity\, and studied how changes in the local vs long-range connectivity impact the temporal patterning of intermittent gamma synchrony\, following the recent model developments presented in [4\,5]. The intermittently synchronized dynamics was characterized using measures (such as measures of the distributions of the desynchronization intervals duration) similar to those used in the experimental investigations of human EEG recordings as mentioned above.\n\n\nResults\nNumerical simulations showed that synaptic strength affected not only the average synchronization strength but also the distribution of desynchronization intervals durations (including situations where the former is not substantially different\, but the latter is markedly altered). This happened in a way that stronger local connectivity tended to prolong desynchronization intervals\, whereas stronger long-range connectivity tended to shorten them across a fairly broad parameter range. The changes in the temporal patterning of synchronized dynamics in these networks may lead to the changes in how these networks respond to common input signals affecting mutual synchronizability of connected networks.\n\n\nDiscussion\nOur results suggest that synaptic parameters not only exert control over the strength of gamma synchrony but also its fine temporal structure over relatively short time scales. The differences in synchronizability properties between the networks may be responsible for the differences in the information processing in these networks and their effective communication efficiency. It is plausible to hypothesize that the experimentally observed differences in the patterning of the synchronous dynamics (as mentioned in the Introduction) may be related to the synaptically-induced changes in the temporal patterning of the synchronized dynamics and changes in synchronizability of the networks as those found in numerical simulations.\n\n\nReferences\n1.&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Malaia\, E. A.\, Ahn\, S.\, & Rubchinsky\, L. L. (2020).&nbsp\;Autism Research\, 13\, 24-31. https://doi.org/10.1002/aur.2219\n2.&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Ahn\, S.\, Malaia\, E. A.\, & Rubchinsky\, L. L. (2025).&nbsp\;Clinical Neurophysiology\,&nbsp\;177\, 2110931. https://doi.org/10.1016/j.clinph.2025.2110931\n3.&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Ahn\, S.\, Rubchinsky\, L. L.\, & Malaia\, E. A. (2025).&nbsp\;Biological Psychology\,&nbsp\;199\, 109077. https://doi.org/10.1016/j.biopsycho.2025.109077\n4.&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Nguyen\, Q. A.\, & Rubchinsky\, L. L. (2021). Chaos\, 31\, 043134. https://doi.org/10.1063/5.0042451\n5.&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Nguyen\, Q. A.\, & Rubchinsky\, L. L. (2024).&nbsp\;Cognitive Neurodynamics\, 18\, 3821–3837. https://doi.org/10.1007/s11571-024-10150-9\n\nAcknowledgement\n&nbsp\;\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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URL:http://cns2026.sched.com/event/1f93f6b864f62de6ec586a404117bd6b
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SUMMARY:P110: The thalamocortical spiking model demonstrating the kinetics of sleep spindle suppression upon noradrenergic neuromodulation
DESCRIPTION:Introduction\nThe sleep spindles are a characteristic oscillatory EEG pattern occurring during NREM and are their presence is controlled by the noradrenaline released from the Locus Coeruleus (LC). Current consensus attributes the noradrenaline effects to slow depolarization caused by a reduction in the potassium leak current conductance mediated by the α1 adrenergic receptors\, although role of β1 receptors affecting the I_h current was also reported. In our study we investigate molecular mechanisms underlying noradrenergic modulation in thalamus and address the discrepancy between the observed time course of the sleep spindle suppression and the reported kinetics the K+ leak channels.\n\n\nMethods\nThe ECoG and LC recordings from adult male Sprague-Dawley rats were reanalyzed from previous study [1]. The sleep spindles were extracted from band-pass (11–16 Hz) filtered ECoG power according to the previously published methods [2]. We used existing spiking sleep network model that comprises four distinct populations: 500 pyramidal neurons\, 100 inhibitory interneurons\, 100 thalamocortical relay neurons\, and 100 thalamic reticular neurons\, with the details of the implementation are in the original paper [3]. Our most recent extension of this model featured time-dependent noradrenergic modulation mediated by α1 and β1 adrenergic receptors.\n\n\nResults\nFollowing the onset of the LC burst\, after the short time lag the complete spindle suppression is achieved. Our simulations demonstrate the role of individual adrenergic receptors activation in the enzymatic pathways leading to this rapid suppression of the sleep spindles. Furthermore\, we show that the investigated pathways are independent of each other\, accumulating to an additive effect. Finally\, we establish which downstream secondary messengers cascades operate on the timescales required to account for the rapid onset of spindle suppression.\n\n\nDiscussion\nWhile there are many models involving the effects of the noradrenergic modulation on the thalamocortical system\, no &nbsp\;concise framework explains how the noradrenaline - induced molecular mechanisms translate to network-level thalamic dynamics. The rapid time course of the observed spindle reduction indicates the prominent role of the initial products of adrenergic receptor activation such as G-protein subunits and diglyceride. Our simulation results provide mechanistic explanation of how sleep - related events are affected during NREM sleep.\n\n\nReferences\n1. Yang\, M.\, & Eschenko\, O. (2025). Differential locus coeruleus–hippocampus interactions during offline states. eLife\, 14\, Article e109159.&nbsp\;https://doi.org/10.7554/eLife.109159.1\n2.&nbsp\;Durán\, E.\, Pandinelli\, M.\, Logothetis\, N. K.\, & Eschenko\, O. (2023). Altered norepinephrine transmission after spatial learning impairs sleep-mediated memory consolidation in rats. Scientific Reports\, 13\, Article 4231. https://doi.org/10.1038/s41598-023-31308-1\n3.&nbsp\;Krishnan\, G. P.\, Chauvette\, S.\, Shamie\, I.\, Soltani\, S.\, Timofeev\, I.\, Cash\, S. S.\, Halgren\, E.\, & Bazhenov\, M. (2016). Cellular and neurochemical basis of sleep stages in the thalamocortical network. eLife\, 5\, Article e18607. https://doi.org/10.7554/eLife.18607\n\nAcknowledgement\nThis work was supported by ERDF-Project No. CZ.02.01.01/00/22_008/0004643\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:9db36f2fbfc9259e8d92752434382b4f
URL:http://cns2026.sched.com/event/9db36f2fbfc9259e8d92752434382b4f
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SUMMARY:P111: VIP-Mediated Attentional Modulation of Persistent Activity in a Cortical Microcircuit Model
DESCRIPTION:Introduction\nPersistent neuronal activity is a proposed neural mechanism supporting the maintenance of information in working memory (WM) [1]. Attention is known to influence WM performance and the stability of internal representations [2]. However\, how attentional signals modulate the circuit mechanisms that generate persistent activity remains insufficiently explored in computational models. In this study\, we investigate whether modulatory input to vasoactive intestinal peptide (VIP) interneurons can regulate the emergence of persistent activity in a biologically constrained cortical microcircuit model.\n\n\nMethods\nWe employed a cortical microcircuit model consisting of excitatory (E) and inhibitory interneuron populations (PV\, SOM\, and VIP) distributed across cortical layers L2/3\, L4\, L5\, and L6 [3]. The model incorporates biologically informed parameters\, including connection probabilities\, synaptic strengths\, neuronal densities\, and firing rate functions for each cell type. Three classes of long-range inputs were implemented: (i) lateral input to E2/3 and SOM2/3 populations\, (ii) modulatory input targeting VIP2/3 neurons\, and (iii) bottom-up input to E4 and PV4 populations. We systematically varied key parameters to examine their influence on the emergence of persistent activity (Fig. 1).\n\n\n\nResults\nThe model exhibits bistability with respect to bottom-up input. Bistability emerges across a range of parameter configurations\, including variations in VIP interneuron cell count\, recurrent connectivity within the E2/3 population\, recurrent connectivity within the E4 population\, and the strength of modulatory input. The size of the bistable region is sensitive to these parameters\, particularly the modulatory input to VIP2/3 neurons. Notably\, stronger modulatory input reduces the minimum bottom-up input required to sustain persistent activity.\n\n\nDiscussion\nVIP interneurons form a canonical disinhibitory circuit motif and are frequently associated with attentional modulation. Our simulations suggest that attentional signals targeting VIP interneurons can facilitate the emergence and maintenance of persistent activity in cortical microcircuits. These findings provide a potential circuit-level mechanism by which attention may enhance working memory stability.\n\nFigure 1.&nbsp\;The cortical microcircuit model and its memory behavior. Key model parameters include VIP interneuron cell count (yellow)\, recurrent connectivity within the E2/3 population (blue)\, and recurrent connectivity within the E4 population (green). The minimum bottom-up input intensity for a memory behavior varies across different levels of lateral and modulatory inputs.​\n\nReferences\n1. Kamiński\, J.\, & Rutishauser\, U. (2020). Between persistently active and activity-silent frameworks: novel vistas on the cellular basis of working memory. Annals of the New York Academy of Sciences\, 1464(1)\, 64-75. https://doi.org/10.1111/nyas.14213\n2. Gazzaley\, A.\, & Nobre\, A. C. (2012). Top-down modulation: bridging selective attention and working memory. Trends in cognitive sciences\, 16(2)\, 129-135. https://doi.org/10.1016/j.tics.2011.11.014 \n3. Chien\, V. S.\, Jiříček\, S.\, Knösche\, T. R.\, Hlinka\, J.\, & Schmidt\, H. (2025). Long-Range Input to Cortical Microcircuits Shapes EEG-BOLD Correlation. bioRxiv\, 2025-06. https://doi.org/10.1101/2025.06.06.658058\n\nAcknowledgement\nThe work was supported by a Lumina-Quaeruntur fellowship (LQ100302301) by the Czech Academy of Sciences (awarded to HS) and ERDF-Project Brain Dynamics\, No. CZ.02.01.01/00/22_008/0004643.\n\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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URL:http://cns2026.sched.com/event/0a416b446d016b44ee180b8c1499d7a5
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SUMMARY:P112: Biologically Realistic Models of Synaptic Release at Human Cortical Synapses
DESCRIPTION:Introduction\nSynapses are integral to information processing and plasticity in the brain. Their functional properties\, such as their strength&nbsp\;and plasticity profiles\, are&nbsp\;determined&nbsp\;by their underlying synaptic structure and organization. A recent study by Rollenhagen and colleagues (unpublished data) reconstructed the structure and vesicle organization of cortical layers 1-6 presynaptic boutons in the human temporal lobe neocortex (hTLN) using transmission electron microscopy and quantitative 3D volume reconstructions. The data reveal variations in vesicle numbers\, sizes\, and positions within each bouton\, as well as differences in bouton organization\, such as active zone areas\, across cortical layers.&nbsp\;\n\n\nMethods\nUsing these&nbsp\;ultrastructural measurements\, we develop biophysically detailed\, spatially explicit\, stochastic models of&nbsp\;hTLN&nbsp\;presynaptic boutons across cortical layers\, implemented in STEPS (Stochastic Engine for Pathway Simulation\,&nbsp\;https://steps.sourceforge.net/)\, and simulate neurotransmitter release to investigate how synaptic transmission is shaped by bouton organization.\n\n\nResults\nWe&nbsp\;observe&nbsp\;that vesicle size diversity within a presynaptic bouton enhances variability in excitatory postsynaptic current amplitudes\, and that smaller vesicles&nbsp\;exhibit&nbsp\;higher fusion propensity due to faster diffusion compared to larger vesicles. Additionally\, our modeling framework allows us to reconcile structural measurements and electrophysiological recordings from similar synapses\, suggesting layer-specific differences in voltage-dependent calcium channel expression in&nbsp\;hTLNboutons.&nbsp\;&nbsp\;\n\n\nDiscussion\nThese findings suggest that nanoscale structural heterogeneity within presynaptic boutons can shape synaptic transmission dynamics and variability. Moreover\, our modeling approach provides a quantitative framework linking ultrastructural measurements to functional synaptic outputs.\n\n\nReferences\nWils\, Stefan\, and Erik De Schutter. "STEPS: modeling and simulating complex reaction-diffusion systems with Python." Frontiers in neuroinformatics 3 (2009): 374.&nbsp\;\n\n\nHepburn\, Iain\, et al. "Vesicle and reaction-diffusion hybrid modeling with STEPS." Communications Biology 7.1 (2024): 573.&nbsp\;\n\nGallimore\, Andrew R.\, et al. "Dynamic regulation of vesicle pools in a detailed spatial model of the complete synaptic vesicle cycle." Science Advances 11.22 (2025): eadq6477.&nbsp\;\n\nAcknowledgement\nAstrid Rollenhagen and Joachim Lübke\,&nbsp\;Institute of Neurosciences and Medicine (INM-10)\,&nbsp\;Forschungszentrum&nbsp\;Jülich\,&nbsp\;for sharing unpublished ultrastructural data and helpful discussions\; and the Scientific Computing and Data Analysis Section at OIST for access to the Deigo supercomputing cluster.&nbsp\;This work was supported by OIST Graduate University.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:f6fd5490fd092ad4c2e3a1b4e259b361
URL:http://cns2026.sched.com/event/f6fd5490fd092ad4c2e3a1b4e259b361
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SUMMARY:P113: Model of Mossy Fiber Bouton (MFB) in hippocampus- the detonator synapse with vesicle release in realistic EM morphology
DESCRIPTION:Introduction\n\n\nThe mossy Fiber Bouton (MFB) in hippocampus acts as a detonator synapse with sophisticated morphology\, large number of vesicles and multiple active zones (AZs)\, which provide the physical basis for the detonator properties [1]. However\, how presynaptic MFB terminals decode the frequency and number of action potentials to transmit information remains poorly understood.&nbsp\; Due to its complicated morphology\, by far there has been no detailed simulation on the presynaptic neurotransmission in a realistic MFB morphology.\n\nMethods\n\n\nHere\, we utilize experimental 3D electron microscopic (EM) data from our collaborators of Prof. Dr. Joachim Lübke unit in Juelich Germany to investigate the basic mechanism of how vesicle docking and release machinery within complicated morphology contributes to MFB’s detonator property. Based on the detailed kinetics of vesicle docking process and rich distribution of mitochondria for uptake calcium in MFB\, by implementing the calcium sensor synaptotagmin 1(syt1) and synaptotagmin 7 (syt7) into the exocytosis machinery\, we simulate the presynaptic vesicle release of MFB by STochastic Engine for Pathway Stimulation (STEPS)(https://steps.sourceforge.net/manual/) [2\,3].\n\nResults\n\n\nOur results show that surprisingly\, all the eight morphologically distinct MFBs consistently exhibit the detonator properties\, despite the diversity and variety of vesicle number\, bouton size\, AZ distributions from each bouton. MFB boutons exhibit frequency independent neural transmission with fast vesicle docking mechanism (spike counting strategies). The release event per pulse highly depends on calcium channel density yet did not change the intrinsic Short Time Facilitation (STF) feature.&nbsp\; Active transport of vesicles can facilitate the fast vesicle docking in some boutons enhancing the detonator properties\; for other boutons\, active transport did not show difference from pure diffusion probably due to the high density of vesicles. \n\nDiscussion\n\n\nImportantly\, our results demonstrate that in certain bouton uptake of calcium by mitochondria plays an important role in regulating precise signal transduction\, while for other bouton the effect is not so obvious\, though mitochondria still play a role in regulating calcium homeostasis. Finally\, we show that spike-locked synchronous release by syt1 dominate over occasional asynchronous releases by syt7\, which is consistent to experimental results. In conclusion\, a STEPS model of neural transmission in giant MFB with realistic morphology and molecular details provides insights of why MFBs show detonator properties.\n\nReferences\n\n\n[1] Nicoll\, R. A.\, & Schmitz\, D. (2005). Synaptic plasticity at hippocampal mossy fibre synapses. Nature Reviews Neuroscience\, 6(11)\, 863–876. https://doi.org/10.1038/nrn1786\n[2] Gallimore\, A. R.\, Hepburn\, I.\, Georgiev\, S. V.\, Rizzoli\, S. O.\, & De Schutter\, E. (2025). Dynamic regulation of vesicle pools in a detailed spatial model of the complete synaptic vesicle cycle. Science Advances\, 11(22)\, eadq6477. https://doi.org/10.1126/sciadv.adq6477\n[3] Hepburn\, I.\, Lallouette\, J.\, Chen\, W.\, Gallimore\, A. R.\, Nagasawa-Soeda\, S. Y.\, & De Schutter\, E. (2024). Vesicle and reaction-diffusion hybrid modeling with STEPS. Communications Biology\, 7(1)\, 573. https://doi.org/10.1038/s42003-024-06276-5\n\nAcknowledgement\n\n\nWe thank our collaborator Prof. Dr. Joachim H. R. Lübke from Juelich Germany to kindly provide EM data of MFB: Rollenhagen\, A.\, Sätzler\, K.\, Rodríguez\, E. P.\, Jonas\, P.\, Frotscher\, M.\, & Lübke\, J. H. R. (2007). Structural Determinants of Transmission at Large Hippocampal Mossy Fiber Synapses. The Journal of Neuroscience\, 27(39)\, 10434–10444. https://doi.org/10.1523/JNEUROSCI.1946-07.2007.\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:1b8406d45e4159a4b66907d15093d15e
URL:http://cns2026.sched.com/event/1b8406d45e4159a4b66907d15093d15e
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SUMMARY:P114: The effect of weak electric deep brain stimulation fields on the synchronization of multi-compartment neuron models
DESCRIPTION:Introduction\nDeep brain stimulation (DBS) is widely used to treat motor symptoms of Parkinson’s disease\, but the mechanism by which it alters neural dynamics is still not fully understood. DBS typically consists of short\, high-frequency electrical pulses delivered through an implanted electrode\, and its effects are attributed to strong electric fields near the electrode. However\, studies of non-invasive brain stimulation show that weak sinusoidal electric fields below 1V/m can modulate spike timing and neural synchronization [1]. This raises the possibility that weak electric fields during DBS (Fig. 1A) can affect cortical neuron dynamics. We test this hypothesis by investigating entrainment of multi-compartment neuron models under weak DBS fields.\n\nMethods\nWe employed morphologically realistic multi-compartment models of cortical neurons (Fig 1B)\, comprising five cell types from different cortical layers [2]. The models were subjected to an externally applied electric field (Fig. 1C) designed to reproduce high-frequency DBS pulses. The orientation of the electric field was varied relative to neural morphology using the polar angle θ. Entrainment was quantified using the phase-locking value across a range of electric field amplitudes and orientations.\n\nResults\nWeak DBS fields can entrain the spike timing of single cortical neurons (Fig. 1D). Spikes tend to cluster at specific phases of the applied DBS waveform\, producing a non-uniform spike-time distribution and an increase in phase-locking value. The preferred entrainment phase varies across neuron types and stimulation amplitudes. Differences in neural morphology and electric field orientation contribute to heterogeneous entrainment patterns across cells. However\, when the electric field is aligned with each neuron’s preferred orientation\, consistent and stronger entrainment emerges.\n\nDiscussion\nOur results suggest that electric fields with amplitudes below 10 V/m can synchronize the spike timing of individual cortical neurons with the applied stimulation. This finding implies that weak fields\, despite being far smaller than those near the electrode\, may still influence cortical activity during DBS. Such effects could interact with the effects of stronger fields present at the stimulation site and potentially contribute to both therapeutic outcomes and stimulation-related side effects. Future work will investigate synchronization in network models composed of two-compartment neurons to determine whether network interactions enhance these effects and whether they can promote synchronization at the population level.\n\nFigure 1.&nbsp\;Cortical neuron entrainment by weak electric DBS fields. (A) Illustration of electric fields generated in the brain during DBS. (B) Multi-compartment models of cortical neurons. (C) Electric field orientation relative to the neural morphology. Waveform of the applied field\, representing a DBS-like stimulus. (D) Mean phase-locking value increases when amplitude increases.​References\n[1] Krause\, M. R.\, Vieira\, P. G.\, Thivierge\, J. P.\, & Pack\, C. C. (2022). Brain stimulation competes with ongoing oscillations for control of spike timing in the primate brain. PLoS Biology\, 20(5)\, e3001650. https://doi.org/10.1371/journal.pbio.3001650\n[2] Tran\, H.\, Shirinpour\, S.\, & Opitz\, A. (2022). Effects of transcranial alternating current stimulation on spiking activity in computational models of single neocortical neurons. NeuroImage\, 250\, 118953. https://doi.org/10.1016/j.neuroimage.2022.118953\n\nAcknowledgement\nThis work is part of a project&nbsp\; that has received funding from the European Research Council (ERC StG DECODE\, grant number 101116047\, to B.C.S).&nbsp\;We would like to thank Ciska Heida for valuable discussions and guidance.&nbsp\; &nbsp\;
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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SUMMARY:P115: Digital Twins Enable Early Alzheimer’s Disease Diagnosis
DESCRIPTION:Introduction\nEarly detection and prognostic prediction of Alzheimer’s disease (AD) is essential for timely intervention and improved patient outcomes. However\, current diagnostic methods\, including cerebrospinal fluid (CSF) analysis and neuroimaging techniques\, are often invasive\, costly\, and unsuitable for early screenings. Non-invasive neural recordings like electro- or magnetoencephalography (EEG or MEG) provide a non-invasive alternative\, yet they often struggle to identify cortical alterations associated with AD at preclinical stages. To address these limitations\, we propose a novel approach based on digital twin models that extract personalized digital biomarkers reflecting individual neurodegeneration levels from non-invasive neural recordings.\n\n\nMethods\nWe developed the DADD (Digital Alzheimer’s Disease Diagnosis) digital twin model to derive digital biomarkers from non-invasive neural recordings [1\, 2]. DADD reconstructs personalized levels of neurodegeneration from individual neural recordings via biophysical modeling of AD-related functional alterations. DADD parameters reconstruct patient-specific levels of synaptic degeneration\, network-level brain disconnection and neuroplastic rewiring. DADD biomarkers were used to predict evolution of cognitive decline in 459 participants with prodromal AD\, also testing their cross-center and cross-modal generalizability on additional two cohorts totaling 46 HC and 76 MCI participants undergoing different types of neural recordings.\n\n\nResults\n\n\nThe DADD model significantly outperformed standard EEG analysis in predicting follow-up results in concurrent machine-learning classifications (AUC=0.71 vs AUC=0.62\, p&lt\;0.00001). Combining DADD digital biomarkers with CSF biomarkers significantly increased the prediction of future conversions (AUC=0.81 vs AUC=0.74\, p=0.009). A Cox Proportional Hazard model found that digital biomarkers had the highest predictive power for future conversions in SCD participants (HR=1.94\, p=0.003)\, also outperforming CSF biomarkers (HR=1.40\, p=0.013). In the cross-center classification\, digital biomarkers obtained consistent cross-center classification (77–78% accuracy)\, while standard biomarkers performed poorly in the generalization attempt (56–65%) [3].\n\nDiscussion\nThese findings suggest that DADD might be a powerful tool for early AD diagnosis and prognosis based on non-invasive recordings. Our approach supports accurate and generalizable classification of dementia staging\, combined with accurate estimation of future cognitive decline risk. The ability of DADD to reconstruct individual neurodegeneration levels provides deeper insights into disease progression\, bridging the gap between network structure and cognitive outcomes. This method represents a scalable\, generalizable and cost-effective solution for early AD detection\, potentially facilitating widespread clinical implementation and improving patient management strategies.\n\n\nReferences\n\n\n[1]&nbsp\; Amato\, L. G.\, et al. (2024). Personalized modeling of Alzheimer’s disease progression estimates neurodegeneration severity from EEG recordings. Alzheimer’s & Dementia: Diagnosis\, Assessment & Disease Monitoring\, 16(1)\, e12526. https://doi.org/10.1002/dad2.12526\n[2]&nbsp\; Amato\, L. G.\, et al. (2025). Digital twins and non-invasive recordings enable early diagnosis of Alzheimer’s disease. Alzheimer’s Research & Therapy\, 17(1)\, 125. https://doi.org/10.1186/s13195-025-01765-z\n[3]&nbsp\; Amato\, L. G.\, et al. (2026). Digital twins support cross-modal and cross-centric classification of mild cognitive impairment. Communications Medicine\, 6(1)\, 30. https://doi.org/10.1038/s43856-025-01281-z\n\nAcknowledgement\nThis work was supported by the Italian Ministry of Research\, in the context of the project NRRP “Fit4MedRob-Fit for Medical Robotics” Grant (# PNC0000007)\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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SUMMARY:P116: Vasoactive intestinal polypeptide-expressing neurons (VIPs) as a mechanism for flexible cognitive control
DESCRIPTION:Introduction\nWorking memory and decision making are mediated by common cortical areas [1]\, but they make conflicting demands of circuit dynamics. Competition between neural populations is essential for decision making\, but exacerbates working memory storage limitations. This discrepancy implies a mechanism for mediating between dynamic regimes. VIPs are strong candidates for this role\, as they receive long-range cortical and modulatory inputs\, and selectively target classes of inhibitory interneurons with varying connectivity profiles [2\,3]. As such\, VIPs are well positioned to integrate a range of task factors and to control the spatial structure of inhibition in a context-dependent manner\, providing a rich mechanism for cognitive flexibility.\n\nMethods\nWe developed a cortical circuit model comprising pyramidal neurons\, large basket cells (broad\, indiscriminate inhibition) and small basket cells (local surround inhibition) [4]\, connected by AMPA\, NMDA\, and GABA receptor synapses with conductance strengths informed by electrophysiological data. VIP control was simulated by independently modulating inhibitory conductance onto each interneuron class. We simulated a luminance-contrast visual discrimination task in which the network identified a target among distractors and a visuospatial working memory task in which the network maintained stimulus information across a delay. Both tasks received identical stimuli to enable direct comparison between the dynamics imposed by inhibitory structure.\n\nResults\nUnder a single set of parameter values\, our model accounts for neural and behavioural signatures of decision making and working memory. On our decision task\, these data include target-selective and distractor-selective neural activity\, and psychometric and chronometric curves. On our working memory task\, they include statistics of persistent mnemonic activity and storage limitations. Local inhibition has a strong stabilizing effect in the model\, attenuating the competitive dynamics engendered by large basket cells. As such\, VIPs establish a decision regime by preferentially targeting small basket cells (local disinhibition) and establish a working memory regime by targeting large basket cells (broad disinhibition).\n\nDiscussion\nOur findings support the hypothesis that VIPs mediate flexible cognitive control by selectively targeting inhibitory cell classes with different connectivity profiles\, effectively determining the spatial structure of inhibition in cortical circuits. This mechanism enables the rapid switching between task-appropriate dynamic regimes for decision making (competition) and working memory (stability with minimal competition) as well as fine-tuning the dynamics within each regime. The model makes testable predictions for neural and behavioural data from visual discrimination and working memory tasks.\n\nReferences\nMurray\, J. D.\, Jaramillo\, J.\, & Wang\, X. J. (2017). Working memory and decision-making in a frontoparietal circuit model.&nbsp\;Journal of Neuroscience\,&nbsp\;37(50)\, 12167-12186.Pi\, H. J.\, Hangya\, B.\, Kvitsiani\, D.\, Sanders\, J. I.\, Huang\, Z. J.\, & Kepecs\, A. (2013). Cortical interneurons that specialize in disinhibitory control.&nbsp\;Nature\,&nbsp\;503(7477)\, 521-524.Wall\, N. R.\, De La Parra\, M.\, Sorokin\, J. M.\, Taniguchi\, H.\, Huang\, Z. J.\, & Callaway\, E. M. (2016). Brain-wide maps of synaptic input to cortical interneurons.&nbsp\;Journal of Neuroscience\,&nbsp\;36(14)\, 4000-4009.Wang\, Y.\, Gupta\, A.\, Toledo-Rodriguez\, M.\, Wu\, C. Z.\, & Markram\, H. (2002). Anatomical\, physiological\, molecular and circuit properties of nest basket cells in the developing somatosensory cortex.&nbsp\;Cerebral cortex\,&nbsp\;12(4)\, 395-410.
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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URL:http://cns2026.sched.com/event/2805282c1e405af6083acd21f20a2401
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SUMMARY:P117: Neurite heterogeneity controls signal propagation in a model of the ctenophore syncytial nerve net
DESCRIPTION:Introduction\n\nRecent work [1] on the ctenophore&nbsp\;M. leidyi&nbsp\;has shown that the standard architecture of excitable neurons connected by neurites terminated by synapses is not universal. The ctenophore subepithelial nerve net (SNN) is a continuous cytoplasmic network lacking chemical or electrical synapses. Electron micrographs revealed a nerve net with a beaded or “pearls-on-a-string” morphology in which spherical swellings alternate with thin cylindrical constrictions&nbsp\;[1].&nbsp\;This morphology appears to be heterogeneous\, with varying degrees of pearls (or beads) and connectors between them.&nbsp\;The&nbsp\;overall function and electrical measurements of the&nbsp\;M. leidyi&nbsp\;SNN are not yet known\, inviting investigation into basic models of possible signal propagation modes.\n\n\n\nMethods\n\nWe&nbsp\;model the&nbsp\;structure of the&nbsp\;SNN as consisting of&nbsp\;a variable number of&nbsp\;polygons embedded on a circular disk\, respecting the organism’s physical dimensions. The aboral organ and the comb rows are modeled&nbsp\;as excitable tissue with the help of a standard diffusively coupled neuron model.&nbsp\;The neurites connecting neurons are modelled as only partially excitable\, with two parameters controlling their excitability\,&nbsp\;reflecting&nbsp\;the&nbsp\;special ‘blebbed’ morphology of the SNN. We then study activity spread on the computational domain by numerically solving the underlying partial differentials equations. We also derive an effective cable equation taking into account the blebbed morphology.\n\n\nResults\n\nA central function of the SNN is the conduction of a swimming or reversal signal from the aboral organ to all eight comb rows&nbsp\;[2].&nbsp\;We examine the conditions under which this can occur&nbsp\;using our model\, and derive constraints on neurite heterogeneity.&nbsp\;We discuss neuropeptides and other biophysical stimuli as drivers for the beaded morphology. Using simulations of excitable and diffusive elements on a network\, we parameterize the model with a modified cable equation and compare the conduction speed and directionality&nbsp\;in such networks to the observed ciliary beating.&nbsp\;We use our modified cable equation to gauge the accuracy of parameters used in our simulations and suggest that the SNN uses neurite morphology to adjust propagation delays.\n\n\nDiscussion\n\nTaken together we&nbsp\;theoretically investigate SNN neurite structure and heterogeneity for its consequences for signal propagation on multiple scales.&nbsp\;Notably\, the same organism at a later developmental stage possesses a second syncytial nerve net - the mesogleal nerve net - whose neurites display a distinct cylindrical morphology [4].&nbsp\;The coexistence of two nerve nets with morphology in the same animal suggests a link between SSN morphology and function. Indeed\, in mammalian central nervous system axons\, remodelling of an analogous beaded morphology fine-tunes action potential conduction velocity and overall neuronal function was recently discussed [3]. Yet the connection to the synapse-free ctenophore SNN has not been exploreduntil now.&nbsp\;\n\n\nReferences\n\nBurkhardt\, P.\, et al (2023). Syncytial nerve net in a ctenophore adds insights on the evolution of nervous systems.&nbsp\;Science\, 380(6642)\, 293–297. https://doi.org/10.1126/science.ade5645\nTamm\, S. L. (2014). Cilia and the life of ctenophores.&nbsp\;Invertebrate Biology\, 133(1)\, 1–46. https://doi.org/10.1111/ivb.12042\nGriswold\, J. M.\, et al (2025). Membrane mechanics dictate axonal pearls‑on‑a‑string morphology and function.&nbsp\;Nature Neuroscience\, 28(1)\, 49–61. https://doi.org/10.1038/s41593-024-01813-1\nJokura\, K.\, Jasek\, S.\, Niederhaus\, L.\, Burkhardt\, P.\, & Jékely\, G. (2026). Neural connectome of the ctenophore statocyst.&nbsp\;eLife\, 14\, e108420. https://doi.org/10.7554/eLife.108420\n\n\nAcknowledgement\n\nJS&nbsp\;acknowledges&nbsp\;funding by&nbsp\;the European Union (ERC\, SYNNEURO\, 101163768). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:42461a5e6910fe68f918f8774722ee9c
URL:http://cns2026.sched.com/event/42461a5e6910fe68f918f8774722ee9c
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SUMMARY:P118: Development and Validation of a Multi-scale Model of Cerebellar Transcranial Magnetic Stimulation
DESCRIPTION:Introduction\nTranscranial magnetic stimulation (TMS) is a promising technique to alleviate symptoms related to cerebellar pathologies. However\, the outcomes of TMS are variable and it is not understood how exactly neuronal activity is affected by it. Many models\, at different scales\, have been developed to try to address the open questions on the functioning of TMS. The state of the art are multi-scale models\, which integrate MRI generated head models of the electric field induced by TMS and biologically realistic neuron models. A limitation of multi-scale models lies in their validation [1]. In this work we present a framework to model and validate TMS targeting the cerebellum.\n\n\nMethods\n\nWe calculated the mean electric field induced by TMS in the cerebellar vermis using the SimNIBS simulator. We developed a biologically realistic model of the cerebellar cortex [2] and coupled the TMS induced electric field to its neurons using NEURON’s extracellular mechanism.\nWe generated the input-output curve of the cerebellar network\, in baseline and in stimulated conditions\, by increasing the input mossy fibre frequency from 0 to 150 Hz in 3 Hz steps.&nbsp\;\nWe normalised the input-output curves in the two conditions and utilised them in a vertical oculomotor system model [3] to predict eye movements. We compared predicted eye movements in the baseline and stimulated conditions with experimental data to validate the TMS model.\n\n\n\nResults\n\nThe maximum mean electric field induced by TMS in the cerebellum vermis is 30.4 V/m. The stimulation does not alter the mean firing rate of the neurons\, but it alters the spike timing and the instantaneous firing rate.&nbsp\;\nWe reproduced the eye movements of a healthy subject with the network in baseline conditions. Eye movements in the stimulated condition show a drift during gaze holding. Saccades are not affected by the stimulation.\nExperimentally it was found that the application of TMS generated an increase in the amplitude of ipsilateral reflexive saccades and in the acceleration of ipsilateral smooth pursuit movements.\n\n\n\nDiscussion\n\nThe vertical oculomotor system model produces observable differences in predicted eye movements following stimulation. This suggests that the model can serve as a validation framework for transcranial magnetic stimulation models targeting cerebellar circuits involved in saccades and gaze holding.\nPredicted eye movements do not accurately reproduce the experimental findings. This may suggest that the employed model is not detailed enough to reproduce experimental results. It may also suggest eye movement changes seen experimentally&nbsp\; are not primarily driven by neuronal activity within the targeted cerebellar region\, but may also arise from activation of more superficial neural structures or neighbouring regions in the cerebral cortex.\n\n\n\nReferences\n\nShahid\, S. S.\, Bikson\, M.\, Salman\, H.\, Wen\, P.\, & Ahfock\, T. (2014). The value and cost of complexity in predictive modelling: Role of tissue anisotropic conductivity and fibre tracts in neuromodulation. Journal of Neural Engineering\, 11(3)\, 036002. https://doi.org/10.1088/1741-2560/11/3/036002&nbsp\;\nBernasconi\, E.\, Yousif\, N.\, & Steuber\, V. (2024). 32nd Annual Computational Neuroscience Meeting: CNS*2023 - P194 Development of a biologically realistic cerebellar model to study the effects of non-invasive stimulation. Journal of Computational Neuroscience\, 52(1)\, 3–166. https://doi.org/10.1007/s10827-024-00871-5&nbsp\;\nGlasauer\, S.\, & Rössert\, C. (2008). Modelling drug modulation of nystagmus. Progress in Brain Research\, 171\, 527–534. https://doi.org/10.1016/S0079-6123(08)00675-4 \n\n\n\nAcknowledgement\n-\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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URL:http://cns2026.sched.com/event/b648d7d1f401a09989d49221c69b100a
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SUMMARY:P119: Low-Effort Attention as Free-Energy Optimization via Cingulo–Autonomic Control
DESCRIPTION:Introduction\n\n\nActive inference models of attention and self-control typically emphasize effortful precision weighting implemented through executive control. However\, empirical findings indicate that attentional stability and self-regulation can improve under minimal cognitive effort. This poses a challenge for effort-centric interpretations of control cost in predictive systems [1]. We address this gap by proposing a regulatory framework in which attention optimization emerges through reductions in expected free energy mediated by brain–body coupling\, rather than increased top-down control. The model reframes low-effort regulation as efficient precision allocation across interoceptive and exteroceptive hierarchies.\n\nMethods\n\n\nWe formalize low-effort attention as an active inference process in which expected free energy is minimized via autonomic and cingulate-mediated precision control. The model centers on an anterior cingulate–posterior cingulate–striatal (APS) circuit interacting with parasympathetic regulation to modulate policy selection and control cost. Empirical constraints include observed shifts in midline theta–alpha dynamics\, heart-rate variability\, and network reconfiguration following brief low-effort training paradigms [2]. Control is modeled as adaptive precision modulation rather than sustained executive signaling.\n\nResults\n\n\nThe framework predicts that reducing expected free energy can be achieved by stabilizing interoceptive predictions and lowering control-related metabolic demand. Simulated dynamics reproduce empirical signatures of low-effort regulation\, including increased midline coherence\, parasympathetic dominance\, and transitions from frontoparietal engagement to cingulo-striatal coordination. The model accounts for rapid plasticity in cingulate pathways and the emergence of automaticity without increased policy complexity. These results suggest that attentional efficiency arises from optimized precision weighting rather than enhanced effort.\n\nDiscussion\n\n\nThis work situates low-effort attention and self-control within an active inference framework\, offering a computational account of how regulation can improve while control cost decreases. By treating attention as precision optimization coupled to autonomic regulation\, the model reconciles empirical findings with free-energy principles and generates testable predictions for electrophysiological and interoceptive markers [1\, 2]. The framework generalizes across training paradigms and informs theories of embodied cognition\, adaptive control\, and energy-efficient artificial agents.\n\nReferences\n\n\n1. Tang\, Y.Y.\, Tang\, R\,&nbsp\;Posner\, M. I.\,&nbsp\;&&nbsp\;Gross\, J. J. (2022). Effortless training of attention and self-control: mechanisms and applications. Trends Cogn Sci. 26(7)\, 567-577. https://doi.org/10.1016/j.tics.2022.04.006\n2. Friston\, K. (2010). The free-energy principle: a unified brain theory?. Nat Rev Neurosci 11\, 127–138. https://doi.org/10.1038/nrn2787\n\nAcknowledgement\n\n\nThis work is supported by the ONR N000142412270 and NIH R33 AT010138. \n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:4f296c498d55d8e998873d4a32f67396
URL:http://cns2026.sched.com/event/4f296c498d55d8e998873d4a32f67396
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SUMMARY:P120: Adaptive Multisensory Support for Low-Effort Attention in Embodied Agents
DESCRIPTION:Introduction\n\n\nAttention regulation depends on how neural systems balance internal control with sensory input. When this balance becomes rigid\, regulation requires greater effort and becomes unstable. Empirical work shows that a brief low-effort training paradigm\, the integrative body–mind training (IBMT) improves attention by inducing a low-effort regulatory state [1]\, yet individuals vary in their ability to access and stabilize this state. Existing accounts do not explain how such low-effort regulation is learned and generalized at the level of control dynamics. We propose a theoretical framework in which embodied AI supports this learning by stabilizing a measurable target regulatory state via adaptive multisensory input.\n\nMethods\n\n\nWe model attention regulation using predictive coding and the free energy principle [2]\, treating control as precision-weighted inference. Effortful states arise from excessive precision on self-related control priors\, increasing energetic cost. Low-effort states correspond to reduced control precision and efficient integration of interoceptive and exteroceptive signals. We introduce an AI-based multisensory support architecture operating as a closed-loop system layered onto IBMT. The AI estimates regulatory state from physiological and attentional markers and adaptively modulates basic sensory parameters (timing\, intensity\, variability) to bias precision allocation without explicit instruction or reinforcement.\n\nResults\n\n\nThe framework predicts that adaptive multisensory support facilitates access to low-effort regulatory states in individuals with high attentional noise or rigid control. Predicted outcomes include reduced energetic cost\, reflected in stabilized autonomic markers and decreased indices of cognitive effort. With repeated practice\, the model predicts internalization of the target state\, leading to reduced dependence on external modulation. The framework also predicts failure regimes\, including destabilization under excessive modulation and limited benefit when control precision is already low\, highlighting the need for individualized adaptation.\n\nDiscussion\n\n\nBy treating low-effort attention as a configuration of regulatory dynamics rather than a subjective state\, this framework bridges contemplative neuroscience and embodied active inference. Individual differences in attention training are reframed as differences in precision dynamics and learning trajectories. The model generates testable predictions that adaptive sensory environments accelerate stabilization and effortless re-entry into low-effort regulation\, and suggests general design principles for embodied agents that support regulation by shaping sensory context rather than increasing control intensity.\n\nReferences\n\n\n1. Tang\, Y.Y.\, Tang\, R\,&nbsp\;Posner\, M. I.\,&nbsp\;&&nbsp\;Gross\, J. J. (2022). Effortless training of attention and self-control: mechanisms and applications. Trends Cogn Sci. 26(7)\, 567-577. https://doi.org/10.1016/j.tics.2022.04.006\n2. Friston\, K. (2010). The free-energy principle: a unified brain theory?. Nat Rev Neurosci 11\, 127–138. https://doi.org/10.1038/nrn2787\n\nAcknowledgement\n\n\nThis work is supported by the ONR N000142412270 and NIH R33 AT010138. \n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:b6365fba7adaf6c6980cb730ccb60d38
URL:http://cns2026.sched.com/event/b6365fba7adaf6c6980cb730ccb60d38
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SUMMARY:P121: Optogenetic WiChR based seizure control in a potassium driven epilepsy model
DESCRIPTION:Introduction\nOptogenetic modulation of pyramidal cells using potassium selective opsins has been shown to inhibit neuronal activity\, making it a promising strategy for suppressing seizures [1]. A prior study in a point‑neuron hippocampal model showed seizure termination after brief opsin activation\, but different seizure generation mechanisms may alter intervention efficacy [2]. In this study\, we examine optogenetic modulation in a multicompartment model based on the potassium hypothesis\, in which elevated extracellular potassium drives seizure initiation and maintenance.\n\nMethods\nWe extended the potassium based seizure model of Gentiletti et al. (2022) by adding a synaptically connected cell (0) without extracellular coupling and incorporating the WiChR opsin model proposed by Weyn et al. (2025) into all principal cell (1-4) compartments\, as schematically shown in (Fig.\u202f1A) [1\,3]. We illuminated different subsets of principal cells for varying illumination durations (tI) and quantified (1) mFRall\, the mean firing rate of cells\u202f1-4\; (2) FR0\, the firing rate of cell\u202f0 as a measure of SLE related synaptic propagation\; (3) seizure-like event (SLE) duration\, defined as periods where mFRall\u202f&gt\;\u202f2.5\u202fHz for\u202f&gt\;\u202f7\u202fs\; and (4) SLE end time.\n\nResults\nThe results are shown in Fig.\u202f1B. mFRall was consistently reduced during illumination\, with stronger suppression when larger fractions of the onset zone were targeted. FR0 was highly sensitive to the number of illuminated cells and returned to pre‑SLE (&lt\;1\u202fHz) levels only when 75% of the zone was targeted. Longer tI further decreased firing\, although similar reductions also occurred without stimulation due to intrinsic SLE dynamics. Despite transient suppression\, the increased SLE end time shows that activity resumed after illumination. Only prolonged\, centered (cells 2/3) partial or full onset‑zone (cells\u202f1-4) targeting fully abolished the SLE\, reducing both duration and end time.\n\nDiscussion\nIn potassium driven SLEs\, inhibiting a large proportion of the onset zone is necessary to prevent synaptic propagation during illumination. Furthermore\, transient complete pyramidal inhibition is insufficient for SLE termination\, as activity reliably resumes once illumination ceases. Effective suppression depends on the target zone and requires prolonged illumination that outlasts the potassium driven dynamics sustaining SLEs. These findings contrast with the earlier point‑neuron study\, where short optogenetic intervention fully stopped SLEs\, suggesting that the efficacy of optogenetic strategies critically depends on the underlying biophysical mechanism and the need for mechanism specific intervention design.\n\nFigure 1. A. Schematic representation of the model and example output. B. Impact of illumination duration (tI) and targeted cell subsets (stimCells) on firing rates during illumination (mFR_all\, mFR0) and on SLE duration and end time.​\n\nReferences\n[1] Weyn\, L.\, Tarnaud\, T.\, Schoeters\, R.\, De Becker\, X.\, Joseph\, W.\, Raedt\, R.\, & Tanghe\, E. (2025). Computational analysis of optogenetic inhibition of CA1 neurons using a data‑efficient potassium‑ and chloride‑conducting opsin model. Journal of Neural Engineering\, 22(4)\, 046051. https://doi.org/10.1088/1741-2552/adf94a\n[2] Weyn\, L.\, Tarnaud\, T.\, Joseph\, W.\, Raedt\, R.\, & Tanghe\, E. (2026). Optogenetic inhibition of a hippocampal network model. Journal of Computational Neuroscience\, 54(Suppl 1). https://doi.org/10.1007/s10827-025-00915-4\n[3] Gentiletti\, D.\, de Curtis\, M.\, Gnatkovsky\, V.\, & Suffczynski\, P. (2022). Focal seizures organized by feedback between neural activity and ion concentration changes. eLife\, 11\, e68541. https://doi.org/10.7554/eLife.68541&nbsp\;\n\nAcknowledgement\nThis work is supported by BOF project SOFTRESET.
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:63b58e8ff034d4b379e5fbb8e5a6aa3f
URL:http://cns2026.sched.com/event/63b58e8ff034d4b379e5fbb8e5a6aa3f
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SUMMARY:P122: Computational modeling of ultrasonic neuromodulation in realistic cortical cells
DESCRIPTION:Introduction\nTranscranial ultrasound stimulation (TUS) is an emerging neuromodulation technique offering millimeter‑scale precision and non‑invasive stimulation. While experimental studies show that ultrasound can modulate neural activity across multiple brain regions\, the underlying biophysical mechanisms remain incompletely understood\, limiting optimal protocol design. Among the proposed mechanisms\, including flexoelectricity\, mechanosensitivity\, thermodynamic effects\, and membrane displacement\, intramembrane cavitation (IC) has gained particular attention. IC describes the oscillating gas cavities between the phospholipid membrane leaflets\, generating capacitive currents and membrane charge oscillations that can alter neuronal excitability.\n\nMethods\nEarly computational work using the neuronal intramembrane cavitation excitation (NICE) model demonstrated the potential of this mechanism but suffered from large computational cost [1]. Subsequent developments\, such as the multi‑scale optimized SONIC model and the spatially extended SECONIC model\, introduced precomputation\, hybrid integration\, and Fourier analysis to accelerate simulations and incorporate charge redistribution dynamics [2\,3]. However\, these models were limited to one-dimensional representations of neurons\, restricting their ability to capture realistic spatial activation patterns. In this work\, we extend IC‑based ultrasonic neuromodulation modeling to morphologically realistic neurons across different cortical layers.\n\nResults\nBy integrating detailed neuronal\, we investigate how ultrasound parameters such as frequency\, pressure amplitude\, pulse repetition frequency (PRF)\, duty cycle (DC)\, and sonophore radius\, shape neuronal responses. Fig. 1 shows how the firing rate of a L2/3 pyramidal cell depends on the pressure amplitude and the duty cycle. This approach enables localization of activation sites\, assessment of the role of higher‑order charge overtones\, and evaluation of whether IC can account for experimentally observed cell‑type specificity and protocol sensitivity. For instance\, across multiple cortical cell types\, we found that activation consistently originated at terminal nodes.\n\nDiscussion\nBy integrating advanced biophysical models with detailed\, realistic neuronal morphologies\, this study advances a mechanistic understanding of how TUS influences neural activity and offers a foundation for accurate\, in‑silico optimization of stimulation strategies.\n \nFigure 1.&nbsp\;The intramembrane cavitation model in a pyramidal cell (top left)\, a schematic of the implementation of ultrasound field – neuron coupling (top right) and the firing rate of a L2/3 pyramidal cell as a function of amplitude and duty cycle.​\n\nReferences\n[1] Plaksin\, M.\, Kimmel\, E.\, & Shoham\, S. (2016). Cell-Type-Selective Effects of Intramembrane Cavitation as a Unifying Theoretical Framework for Ultrasonic Neuromodulation. eNeuro\, 3(3)\, ENEURO.0136-15.2016. https://doi.org/10.1523/ENEURO.0136-15.2016\n[2] Lemaire\, T.\, Neufeld\, E.\, Kuster\, N.\, & Micera\, S. (2019). Understanding ultrasound neuromodulation using a computationally efficient and interpretable model of intramembrane cavitation. Journal of neural engineering\, 16(4)\, 046007. https://doi.org/10.1088/1741-2552/ab1685\n[3] Tarnaud\, T.\, Joseph\, W.\, Schoeters\, R.\, Martens\, L.\, & Tanghe\, E. (2020). SECONIC: Towards multi-compartmental models for ultrasonic brain stimulation by intramembrane cavitation. Journal of neural engineering\, 17(5)\, 056010. https://doi.org/10.1088/1741-2552/abb73d\n\nAcknowledgement\n/
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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SUMMARY:P123: Population-based Functional Source Separation enables identification of cortical sources in new individuals
DESCRIPTION:Introduction\nFunctional Source Separation (FSS) extends Blind Source Separation (BSS) by incorporating prior knowledge about the functional characteristics of neural responses during source extraction. FSS has previously been used to estimate primary motor (FS_M1) and somatosensory (FS_S1) cortical representations from stimulus-evoked EEG responses [1]\, and to identify FS_M1 in passive subjects [2]. Here we propose a population-based FSS approach that estimates spatial filters from pooled cross-subject data. We test whether these population-derived filters can identify motor and sensory cortical sources in new individuals using resting-state EEG\n\n\nMethods\nEEG recordings were obtained from 22 healthy subjects during median nerve stimulation (Sensory condition)\, weak isometric handgrip (Motor condition)\, and resting state with eyes open and closed. FSS was applied in two ways: (i) FSSindividual\, computed separately for each subject\, and (ii) FSSpopulation\, estimated from pooled cross-subject data. Both approaches were used to identify FS_S1 and FS_M1 sources in both hemispheres. The similarity between sources extracted with the two approaches was assessed using mutual information (MI)\, time correlation coefficient (TCC)\, and Kullback–Leibler (KL) divergence. Cortico-muscular coherence (CMC) was computed in the Motor condition to evaluate the functional relevance of the motor sources.\n\n\nResults\nIn the Motor condition\, CMC between the population-derived motor source and the prime mover muscle during isometric handgrip did not differ from the coherence obtained using the individual motor source in either hemisphere (p = 0.3). Similarly\, the population-derived sensory source showed responses to median nerve stimulation comparable to those obtained with the individual sensory source (p = 0.1). Across all four sources\, the broadband activity of each population-derived source showed higher MI and TCC\, and lower KL divergence\, with its corresponding individual source than with other sources (p &lt\; 0.03).\n\nDiscussion\nThese results show that motor and sensory cortical sources can be identified from resting-state EEG using population-derived FSS filters that generalize to new subjects. This approach may be particularly useful in clinical settings where task-related activity is difficult to obtain\, such as in patients with stroke or limb amputation [3\,4]. Because FSS is deterministic\, it may also improve the reproducibility of EEG-based studies of regional neuronal activity. Future work should extend this framework to larger populations\, pathological conditions\, and additional cortical regions such as auditory and visual cortices.\n\n\nReferences\n1. Barbati\, G.\, Sigismondi\, R.\, Zappasodi\, F.\, Porcaro\, C. et al. (2006). Functional source separation from magnetoencephalographic signals. Hum. Brain Mapp. doi:10.1002/hbm.20232\n2. Porcaro\, C.\, Cottone\, C.\, Cancelli\, A.\, Salustri\, C.\, Tecchio\, F. (2018). Functional semi-blind source separation identifies primary motor area without active motor execution. Int. J. Neural Syst. doi:10.1142/S0129065717500472\n3. Delcamp\, C.\, Srinivasan\, R.\, Cramer\, S. C. (2024). EEG provides insights into motor control and neuroplasticity during stroke recovery.&nbsp\;Stroke. doi:10.1161/STROKEAHA.124.048458\n4. Liu\, S.\, Fu\, W.\, Wei\, C.\, Ma\, F. et al. (2022). Interference of unilateral lower limb amputation on motor imagery rhythm and remodeling of sensorimotor areas.&nbsp\;Front. Hum. Neurosci.doi:10.3389/fnhum.2022.1011463\n\n\n\n\n\nAcknowledgement\nThe authors thank Annalisa Pascarella\, Gian Luca Loli\, Rosario Ribecco\, and Filippo Zappasodi for their scientific contributions over the years\, and the participants who took part in the recordings.\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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URL:http://cns2026.sched.com/event/99d3dc0b139f7f87fd5f9bd143644af7
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SUMMARY:P124: Power Spectrum Harmonics Provide a Signature of Balanced Excitation-Inhibition Across Cortical Scales
DESCRIPTION:Introduction\nTranscranial alternating current stimulation (tACS) is widely used to modulate brain activity\, yet its mechanisms remain incompletely understood and its effects vary across individuals and studies [1]. When nonlinear neural circuits are driven by sinusoidal input\, responses often contain harmonic frequencies in addition to the stimulation frequency [2]. Although commonly observed in neural recordings\, the dynamical origin of these harmonics remains unclear. We hypothesized that harmonic structure may reflect the balance of excitation and inhibition in the stimulated network.\n\nMethods\nWe used a Wilson–Cowan mean-field model to examine how recurrent connectivity\, transfer-function gain\, and input amplitude shape harmonic responses under periodic drive. Model predictions were evaluated using broadband LFP recordings from macaque V4 during prefrontal tACS (N = 17 sessions\; randomized blocks at 5 and 10 Hz\; [3]). Power spectra were computed from 60-s segments (frequency resolution ≈ 0.017 Hz)\, with all harmonics well below the Nyquist limit. Results were consistent using both fast Fourier transform (FFT) and Welch spectra. For comparison\, we also implemented a spiking leaky integrate-and-fire (LIF) network with balanced connectivity.\n\n\nResults\nIn the Wilson–Cowan model\, harmonic structure depended on the population’s operating regime\, determined by net E–I connectivity and input amplitude. Activity near the transfer function’s inflection region produced predominantly odd harmonics\, whereas excursions toward ceiling or floor regions generated even harmonics (Fig. 1). Across 193 experimental epochs\, 88% showed entrainment to the input frequency. Among these\, 84% exhibited odd harmonics\, 16% were restricted to the fundamental frequency\, and none displayed even harmonics. The LIF network reproduced both odd and even harmonic patterns under comparable connectivity regimes.\n\nDiscussion\nOur results show that harmonic structure during rhythmic stimulation reflects the circuit operating point\, determined by E–I balance and input strength. Changes in gain modify the width of odd-dominant regions but preserve the overall pattern of responses. The predominance of odd harmonics in the experimental data is consistent with model regimes associated with balanced excitation–inhibition dynamics typical of healthy cortical networks. Together\, these findings suggest that harmonic analysis could provide a non-invasive probe of cortical state\, and that physiological or behavioral changes affecting network gain may alter harmonic profiles.\n\nFigure 1.&nbsp\;(A) Power spectrum of experimental recordings during SHAM and 5 Hz sinusoidal stimulation. Stimulation induces odd harmonics (15–35 Hz). (B) Log10-OEHR heatmaps from the Wilson–Cowan model showing harmonic dominance across recurrent connectivity (red = odd\, dark blue = even). Connectivity sets the net input to the sigmoid transfer function and thus the operating regime (circles).​References\n1. Krause&nbsp\;MR\, Vieira&nbsp\;PG\, Pack&nbsp\;CC. (2023).&nbsp\;Transcranial electrical stimulation: How can a simple conductor orchestrate complex brain activity?. PLOS Biology 21(1): e3001973.&nbsp\;https://doi.org/10.1371/journal.pbio.3001973\n2. Jin C\, Wang X\, Chen B\, Wan H\, Lu Z\, Sun Y\, Sun Y\, Bu J. (2026). Media Transmission Characteristics of Harmonic Signatures in tACS: From Simple Linear Phantoms to Complex Biological Systems\, Brain Stimulation\, https://doi.org/10.1016/j.brs.2026.103052.\n3. Krause&nbsp\;MR\, Vieira&nbsp\;PG\, Thivierge&nbsp\;JP\, Pack&nbsp\;CC. (2022).&nbsp\;Brain stimulation competes with ongoing oscillations for control of spike timing in the primate brain. PLOS Biology 20(5): e3001650.&nbsp\;https://doi.org/10.1371/journal.pbio.3001650\n\nAcknowledgement\nThank you to C. C. Pack\, P. Vieira and M. R. Krause for the experimental data.&nbsp\;
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:48dd7d746d7ddd5a896f45ac2312885d
URL:http://cns2026.sched.com/event/48dd7d746d7ddd5a896f45ac2312885d
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SUMMARY:P125: Learning Barrel Cortex Representations from Whisker Dynamics and Multimodal Convergence
DESCRIPTION:Introduction\nRodents can discriminate object shape\, size\, and texture through active whisker contacts. Accordingly\, rodent primary somatosensory cortex (S1) contains topographic representations of the whiskers\, and neurons in S1 represent multiple stimulus features associated with whisker based touch. Although these representations have been characterized experimentally\, the computational mechanisms that generate them remain unclear. In addition\, there is substantial convergence between whisker-based touch and other sensory modalities in multiple cortical areas. We test whether training whisker and visual input representations to align in a self-supervised way can reproduce representations in barrel cortex.\n\n\nMethods\nUsing a morphologically accurate model of the mouse whisker array [2]\, we simulated whisker contact sequences in terms of the radial distance and angle of contact. These signals were encoded using per-whisker temporal units\; these units also integrated the signals over the whisk cycle. Learned whisker embeddings were then arranged into a 5×7 topographic grid approximating barrel cortex. The encoder was trained using a contrastive objective that aligned whisker embeddings with visual embeddings produced by MouseNet [1]. Condition-averaged embeddings were used to construct representational dissimilarity matrices (RDMs)\, which were compared to neural population RDMs using Pearson correlation and established noise correction procedures.\n\n\nResults\nThe anatomically structured whisker network produced embeddings aligned with neural population geometry. Representational similarity analysis (RSA) yielded a raw RSA of 0.54 between model and neural dissimilarity matrices across stimulus conditions\, with a noise-corrected estimate of 1.14 relative to the neural reliability ceiling. Alignment was reduced when temporal integration was removed or when whisker embeddings were not arranged in a topographic grid\, indicating that both recurrent dynamics and barrel-like spatial organization contribute to emergent representational structure.\n\n\nDiscussion\nThese results suggest that biologically relevant representations may arise from temporally integrated whisker signals constrained by anatomical hierarchy\, even when the model is trained using a multimodal contrastive objective rather than explicit supervision on the whisker discrimination task\, as in [3]. The dependence of alignment on both recurrent dynamics and topographic organization supports the hypothesis that barrel organization and recurrent dynamics are key determinants of tactile shape coding. This framework provides a principled foundation for extending biologically constrained models toward multimodal cortical integration.\n\n\nReferences\n1. Shi\, J.\, Tripp\, B.\, Shea-Brown\, E.\, Mihalas\, S.\, & A. Buice\, M. (2022). MouseNet: A biologically constrained convolutional neural network model for the Mouse Visual Cortex. PLOS Computational Biology\, 18(9). https://doi.org/10.1371/journal.pcbi.1010427\n2. Bresee\, C. S.\, Belli\, H. M.\, Luo\, Y.\, & Hartmann\, M. J. Z. (2023). Comparative morphology of the whiskers and faces of mice (Mus musculus) and rats (Rattus norvegicus). Journal of Experimental Biology\, 226(19)\, jeb245597. https://doi.org/10.1242/jeb.245597\n3. Chung\, T.\, Shen\, Y.\, Kong\, N. C. L.\, & Nayebi\, A. (2025). Task-optimized convolutional recurrent networks align with tactile processing in the rodent brain. arXiv. https://doi.org/10.48550/arXiv.2505.18361\n\n\n\nAcknowledgement\nCN was supported by NSERC RGPIN-2025-04919 and Alliance International Catalyst.&nbsp\;KK and MJH were supported by NIH award R01 NS-116277.&nbsp\;\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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URL:http://cns2026.sched.com/event/e38bb4feeaf9105fb941411c55dc925c
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SUMMARY:P126: Remapping Convolutional Layers to Fit Cortical Connectivity
DESCRIPTION:Introduction\nConvolutional networks are commonly used in brain models [e.g. 3\, 5]. Convolutions assume a uniform connection structure from source to target. However\, a recent analysis of mesoscale connectivity in the mouse cortex [4] showed that many connections over and under-represent parts of the source and/or target area\, and may project distant parts of a source into nearby parts of a target. Because multiple connections converge in each cortical area\, these complex projection structures constrain which signals converge in the brain. Here we analyze the structural error inherent in using standard convolutional networks to model the mouse cortex\, and develop an extension that has a more accurate structure.\n\nMethods\nWe first calculated 2D cortical-surface coordinates for each voxel of each mouse neocortical area\, as in [4]\, and normalized them to have standard deviations (SD) of 1. For each projection\, we used voxel-voxel connection density estimates from [2] to calculate the weighted-mean source-area coordinates that provided input to each target-area voxel. We evaluated potential remapping functions (f) that mapped target coordinates (t) to corresponding source coordinates (s)\, such that s = f(t). We first fitted a hierarchy of constrained geometric models\, including rotation/flip\, linear\, and affine remapping. We also introduced a more flexible nonlinear remapping model based on a radial basis function interpolator with thin-plate spline kernel [1].\n\nResults\nWe calculated the mean Euclidean distance dμ between actual source coordinates and those predicted by each function. Rotation/flip remapping\, which is consistent with standard convolution (accounting for different map orientations in cortical coordinates)\, gave the poorest fit (dμ&nbsp\;= 0.62 over all connections\, vs. coordinate SD=1). Linear and affine remapping\, which correspond to convolution with non-integer stride and offset\, reduced the mean error to 0.17 and 0.10\, respectively. However\, large errors remained in some voxels for each of these models. The general RBF model further reduced mean error to &lt\; 0.001\, with no large errors. This model corresponds to convolution layers followed by general data-driven remapping layers.\n\nDiscussion\nThis analysis shows that standard convolutional networks are inconsistent with the mesoscale connectivity of mouse cortex. However\, realistic connectivity can be recovered by inserting remapping layers into convolutional networks. This proposed extension improves approximation of a wide range of complex cortical structures\, including diverse subfields in higher visual areas\, low-level multisensory connections\, and complex convergence patterns in prefrontal areas.\n\nReferences\n[1] GE Fasshauer. “Meshfree Approximation Methods with Matlab\, World Sci”. In: Publishing Co\, Singapore (2007).\n[2] Joseph E Knox et al. “High-resolution data-driven model of the mouse connectome”. In: Network Neuroscience 3.1 (2018)\, pp. 217–236.\n[3] Jonathan A Michaels et al. “A goal-driven modular neural network predicts parietofrontal neural dynamics during grasping”. In: Proceedings of the national academy of sciences 117.50 (2020)\, pp. 32124–32135.\n[4] Kinjal Patel et al. “Spatial organization of multisensory convergence in mouse isocortex”. In: bioRxiv (2024)\, pp. 2024–12.\n[5] Jianghong Shi et al. “MouseNet: A biologically constrained convolutional neural network model for the mouse visual cortex”. In: PLOS Computational Biology 18.9 (2022)\, e1010427.\n\nAcknowledgement\nThis work was supported by NSERC Discovery Grant RGPIN-2025-04919.&nbsp\;\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:e0ef4be289488a890ee647e47f99d024
URL:http://cns2026.sched.com/event/e0ef4be289488a890ee647e47f99d024
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SUMMARY:P127: Distinguishing between fetal alcohol spectrum disorders and attention-deficit/hyperactivity disorder using diffusion modelling and spiking neurons
DESCRIPTION:Introduction\nDrift Diffusion (DD) models and spiking neural networks have been effective in predicting and simulating disordered behaviour. They offer deep insight into behavioural processes and spiking neuronal models understand the relationship between such processes and the underlying biological system. A joint approach is valuable as DD models reflect the entirety of performance data and spiking networks mimic neuronal communication. This offers a more detailed analysis that is biologically-plausible and gives a deeper insight into neural dynamics and cognitive processes. Thus\, our work uses both models to identify differences among fetal alcohol spectrum disorders (FASD)\, attention-deficit/hyperactivity disorder (ADHD)\, and comorbid presentations.\n\nMethods\nDD models assume\, following encoding\, that decisions are made via a noisy process where evidence for a response is accumulated until it crosses a response boundary\, after which a corresponding motor response is initiated. DD analysis is used to identify how attention processes differ and to inform spiking Search over Time and Space (sSoTS) model parameters. sSoTS is a spiking neuronal model including several synaptic components (AMPA\, NMDA\, GABA) and frequency adaptation mechanisms.&nbsp\;Pool coupling&nbsp\;parameter is changed to simulate differences in encoding between FASD and ADHD. Furthermore\, we extend prior research in visual search [1\,2] by simulating visual selective attention processes to distinguish FASD\, ADHD\, and comorbid presentations.\n\nResults\nDD analysis showed ADHD favoured accuracy\, whereas FASD (without ADHD) had a similar boundary separation to controls on easy search. ADHD and controls had a similar drift rate\, whereas FASD (with and without ADHD) had a slower drift rate on easy visual search. On difficult search\, all participants had a similar boundary separation favouring accuracy. All diagnostic groups had similar drift rates on difficult search\, which was slower than controls. Our results suggest that FASD affect bottom-up attention processes but an ADHD comorbidity may buffer some effects. To further understand how attention processes differ\, data from DD analysis was used to inform sSoTS parameters and changes were successfully simulated.&nbsp\;\n\nDiscussion\nResults from our visual search paradigm demonstrated the importance of combining computational models to distinguish different patterns of attention deficit among individuals with FASD\, ADHD\, and comorbid presentations. Incorporating coupling parameter changes into the sSoTS model successfully simulated the observed behaviours. Our outcomes provide an initial demonstration of how integrating computational methodologies can further enhance our understanding of how attention processes differ across different disorders. Our work underscores that FASD\, ADHD\, and when both disorders present comorbidly may be able to be distinguished based on the efficiency of bottom-up attention processes.\n\nReferences\n[1]&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Mavritsaki\, E.\, Heinke\, D.\, Allen\, H.\, Deco\, G.\, & Humphreys\, G. W. (2011). &nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Bridging the Gap Between Physiology and Behavior: Evidence from the sSoTS &nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Model of Human Visual Attention. Psychological Review\, 118(1)\, 3–41. https://doi.org/10.1037/a0021868\n[2]&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Mavritsaki\, E.\, & Humphreys\, G. (2016). Temporal Binding and Segmentation in Visual Search: A Computational Neuroscience Analysis. Journal of Cognitive &nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\;&nbsp\; Neuroscience\, 28(10)\, 1553–1567. https://doi.org/10.1162/jocn_a_00984\n\nAcknowledgement\nI would like to thank my supervisory team and all collaborators for their support in preparing this abstract.\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:23c083728dd63184592ed0ff4fadf405
URL:http://cns2026.sched.com/event/23c083728dd63184592ed0ff4fadf405
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SUMMARY:P128: Ion Channels Tune Population-Level Intrinsic Biophysical Heterogeneity
DESCRIPTION:Introduction\n\nHuman neurons show remarkable variation\, even among the same type. Contrary to the belief that it is noise\, within-cell-type heterogeneity enhances information coding and\, as our recent work has shown\, endows dynamical resilience to insults [1]. Furthermore\, our work has shown that heterogeneity is malleable and is reduced in seizures and [1] under correlated inputs [2\,3].\n\nWe focus on intrinsic biophysical heterogeneity--relating to passive and firing properties of a neuron--which is influenced by ion channels. We have discovered that blocking one type of ion channel\, the HCN channel\, can restore heterogeneity under certain conditions [2]. This study aims to clarify the mechanism and conditions under which the effect emerges.\n\nMethods\nFollowing common approaches\, large ensembles of human L5 pyramidal neuron models were built from a pre-tuned model\, sampling ion channel conductances across empirically defined ranges and validating the resulting populations against experimental datasets. Intrinsic heterogeneity was quantified through variance of normalized spiking metrics and information-theoretic measures analogous to those used in our experimental work\, so that the results may be directly compared.\n\nResults\nOur results at this time show that upregulation of specific ion channels\, particularly HCN\, strongly influences population heterogeneity ofsome intrinsic properties such as resting membrane potential (RMP) and input resistance (Fig. 1). While trends appear modest across the full log-scale conductance range\, focusing on the physiological regime (10-4–10⁻³) reveals a sharp decline in variance. In contrast\, transient sodium and M-type potassium channels show weak correlations with the variance of most intrinsic properties.\n\nDiscussion\nWe hypothesize that HCN channels’ unique influence on population-level heterogeneity arises from their involvement in a voltage-dependent negative feedback\, such that increased expression of HCN channels reduces the range of potential intrinsic properties. However\, the counterargument is that\, despite similar feedback\, M-type potassium channels do not show this effect. Owing to inter-channel interactions and degeneracy\, further systematic investigation is needed to understand these non-linear relationships fully. Given the established role of ion channels in diseases and our finding that reduced heterogeneity drives pathology\, this work will inform strategies for modulating intrinsic heterogeneity as a novel therapeutic approach.\n\nFigure 1.&nbsp\;Scatter plot of the (A) resting membrane potential (RMP) and (B) input resistance of a population of ~8000 neurons sorted in increasing order of apical H-current (Ih) maximal conductance\, with the coefficient of variation (CV) of RMP (C) and input resistance (D) respectively plotted on the right.​\n\nReferences\n1. Rich\, S.\, Moradi Chameh\, H.\, Lefebvre\, J.\, & Valiante\, T. A. (2022). Loss of neuronal heterogeneity in epileptogenic human tissue impairs network resilience to sudden changes in synchrony. Cell Reports\, 39(8)\, 110863.\n2. Chameh\, H. M.\, Falby\, M.\, Yang\, Y.\, Movahed\, M.\, Arbabi\, K.\, Sarathy\, C.\, Tripathy\, S. J.\, Zhang\, L.\, Lefebvre\, J.\, & Valiante\, T. A. (2025). Hyperpolarization-activated cation channel mediated intrinsic plasticity changes underlie the malleability of within cell-type electrophysiological heterogeneity. In Neuroscience. bioRxiv.\n3. Trotter\, D.\, Valiante\, T.\, & Lefebvre\, J. (2026). Intrinsic plasticity underlies the malleability of neural network heterogeneity. PRX Life\, 4(1)\, 013023.\n\n\n\nAcknowledgement\nThis work is supported by the Ontario Graduate Scholarship and the Canada Graduate Research Scholarship through Canadian Institutes of Health Research (CIHR).\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:de5628eeb4785a816896db175dcc4229
URL:http://cns2026.sched.com/event/de5628eeb4785a816896db175dcc4229
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SUMMARY:P129: Sequential variability of Steady State Visually Evoked Potentials and its relation with BCI performance
DESCRIPTION:Introduction\nRecent work highlights the importance of identifying and assessing sequential neural activity. Here we introduce a novel approach to characterize sequential patterns in EEG data\, focusing on steady-state visually evoked potentials (SSVEPs) that appear as oscillations in the occipital lobe when attending to a flickering light. This phenomenon is widely used in brain-computer interfaces [1]\, and is often characterized solely in terms of oscillations and their frequency components\, a framework that may overlook critical aspects of neural dynamics [2]. Using spectrograms and wavelet transforms\, we examine the temporal evolution of frequency and power in an SSVEP BCI speller dataset and assess its relationship with BCI performance.\n \nMethods\n\n\nThe EEG recordings used in our analysis come from the BETA dataset [3]\, which includes recordings from 70 subjects in a BCI speller experiment with 40 different stimulus frequencies and 4 trials per frequency. We used spectrograms and wavelet transforms to examine the evolution of the signal’s frequency content and power over time. In this analysis\, we aimed to assess how long a response attributable to the stimulus frequency is maintained throughout the trial\, as well as how long it takes for the response to appear (latency). These results were obtained for all subjects\, averaged across trials\, and compared with their respective performance using a traditional canonical correlation analysis (CCA) detection method.\n\nResults\nAfter characterizing the EEG signals in terms of three metrics (active time percentage (ATP) in the wavelet transform\, active time percentage (ATP) in the spectrogram\, and response latency)\, we computed the average results for each subject across four trials. &nbsp\;We then examined these results and compared them with each subject’s CCA performance. We found a highly significant positive correlation between the CCA performance and the two metrics evaluating the percentage of active time: CWT ATP (r = 0.896)\, and Spectrogram ATP (r = 0.943). On the other hand\, we found a highly significant negative correlation with the response latency metric (r = –0.907).\n\nDiscussion\nOur characterization of the evolving response reveals that SSVEPs are not universally steady as their name suggests. High-performing subjects react to the stimulus earlier and tend to maintain a response throughout the trial. &nbsp\;In contrast\, subjects with poor performance exhibit unstable responses with sequential activations across different frequency bands contributing to detection errors. We also found that response latency is linked to performance: a subject who responds correctly but reacts slowly will perform poorly in the BCI task. Our analysis suggests that poor performance may not always result from an inherent inability to respond to stimuli\, but rather from visual attention issues in the context of simultaneous visual stimuli.\n\nReferences\n[1] Liu\, &nbsp\;S.\, Zhang\, D.\, &nbsp\;Liu\, &nbsp\;Z.\, &nbsp\;Liu\, &nbsp\;M.\, &nbsp\;Ming\, &nbsp\;Z.\, &nbsp\;Liu\, &nbsp\;T.\, &nbsp\;Suo\, D.\, Funahashi\, S.\, and Yan\, T. (2022).&nbsp\; &nbsp\;Review of brain–computer in- terface based on steady-state visual evoked &nbsp\;potential.&nbsp\; &nbsp\;Brain &nbsp\;Science Advances\,&nbsp\; &nbsp\;8(4): 258–275\, https://doi.org/10.26599/BSA.2022.9050022.\n[2] Labecki\, M.\, Nowicka\, M. M.\, Wrobel\, &nbsp\;A.\, and Suffczynski\, P. (2024). Frequency-dependent &nbsp\;dynamics of &nbsp\;steady-state visual &nbsp\;evoked pottials under sustained flicker stimulation. &nbsp\;Scientific Reports\, 14(1):9281\, https://doi.org/10.1038/s41598-024-59770-5.\n[3] Liu\, B.\, Huang\, X.\, Wang\, Y.\, Chen\, X.\, and Gao\, X. (2020). BETA: A Large Benchmark Database Toward SSVEP-BCI Application. &nbsp\;Frontiers in Neuroscience\, 14\, https://doi.org/10.3389/fnins.2020.00627.\n\nAcknowledgement\nResearch funded by grants PID2024-155923NB-I00 and CPP2023-010818 (MCIN/AEI and ERDF- "A way of making Europe").\n\n
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LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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URL:http://cns2026.sched.com/event/5b752d1818096d55bf335a8bd32fbdcc
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SUMMARY:P130: Burst-to-burst information resetting in Central Pattern Generators
DESCRIPTION:Introduction\nBursting neural activity is widely expressed in many neural systems and has been proposed to underlie reliable information multiplexing\, propagation\, and execution. This type of neural activity typically involves interactions at slow and fast timescales. In the context of central pattern generators (CPGs)\, bursting is part of the sequential motor control mechanisms responsible for respiration\, locomotion\, chewing\, and other key motor tasks. In various CPGs\, sequential dynamical invariants in the form of cycle-by-cycle relationships between specific intervals have been identified. These invariants are thought to balance sequence robustness with flexibility by creating timing constraints to the events that compose the sequence [1\,2\,3].\n\nMethods\nWe combined models and experiments to examine how modulations introducing trends in the CPG rhythm shape the cycle-by-cycle and cross-cycle structure of these invariants. We quantified the variability of the intervals forming the sequence between the LP and PD neurons\, as well as their pairwise invariant relationships in the pyloric CPG and also in a conductance-based model [4]. Relationships between intervals belonging to the same and to different cycles were assessed in experiments and in the model where ionic and synaptic currents were linked to interval durations. Invariants were identified with pairplots and quantified using the coefficient of determination R².\n\nResults\nWe show that\, both in computational models and electrophysiological experiments\, sequential dynamical invariants are generally present only between intervals that compose the sequence within the same cycle. Only when a slow external modulation introduces a trend can intervals from different cycles become related. This is observed when modulation arises naturally or is experimentally induced in the pyloric CPG\, and when a slow modulating current is injected into the model CPG. Model ionic and synaptic currents reset in every cycle and do not influence intervals in the next one\, but modulating the CPG causes intervals to be related across cycles.\n\nDiscussion\nAssessing sequential dynamical invariants is key to understanding the autonomous coordination of rhythmic motor sequences produced by CPGs. Not all intervals in the sequence are related\, which contributes to the timing flexibility within the constraints of the invariants. The persistence of relationships between intervals across cycles can serve as a means to characterize CPG modulation. Combining models and electrophysiological experiments\, we established the conditions under which sequential intervals can be related both cycle-by-cycle and across cycles when perturbations introduce trends in the CPG rhythm. These results are also relevant for bio-inspired robotics\, where such principles could support autonomous motor control.\n\nReferences\n[1] Elices et al (2019). doi:https://doi.org/10.1038/s41598-019-44953-2.\n[2] Berbel et al (2025). doi:https://doi.org/10.1016/j.neucom.2025.130218.\n[3] Garrido-Peña et al (2021). doi:https://doi.org/10.1016/j.neucom.2020.08.093.\n[4] Deistler et al (2022). doi:https://doi.org/10.1073/pnas.2207632119.\n\nAcknowledgement\nResearch funded by grants PID2024-155923NB-I00\, PID2023-149669NB-I00\, CPP2023-010818 (MCIN/AEI and ERDF- "A way of making Europe").\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:c6c310792fa6b654bf6f3cc75dfdef44
URL:http://cns2026.sched.com/event/c6c310792fa6b654bf6f3cc75dfdef44
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SUMMARY:P131: Decreased alpha/theta temporal ExSEnt of left prefrontal cortex in dementia: a robust biomarker
DESCRIPTION:Introduction\nDementia involves progressive cognitive decline\, with Alzheimer’s disease (AD) and frontotemporal dementia (FTD) as major subtypes. Electroencephalography (EEG) provides a noninvasive and accessible measure of brain dynamics and able to capture pathological slowing\, often reflected by increased theta-to-alpha power ratio (TAR) [1]. Reduced signal complexity has also been reported in dementia. We tested whether Extrema-Segmented Entropy (ExSEnt)\, which separates temporal and amplitude irregularity\, improves discrimination and interpretability beyond conventional electroencephalography biomarkers [2].\n\nMethods\nWe analyzed resting-state EEG from 88 subjects\, including 36 with AD\, 23 with FTD\, and 29 controls [3]. After preprocessing and independent component analysis\, we classified brain sources into four clusters: right and left prefrontal cortices (R/LPFC) and right and left visual associations (R/LVA). Then we computed a set of classical spectral and complexity features. We also measured the ExSEnt metrics\, which quantifies the entropy of extrema-based segment durations\, amplitudes\, and their pair. To find table features for dementia detection\, we performed stability selection with elastic-net logistic regression and nested leave-one-subject-out validation [4].\n\n\n\nResults\nExSEnt improved discrimination mainly in the LPFC\, where balanced accuracy increased from 71.5% to 82.6%. In that region\, ExSEnt features showed high selection stability and replaced conventional measures as dominant predictors in 3 out of 4 brain areas. The most informative variables were temporal and joint temporal-amplitude entropy measures in alpha and theta bands\, with negative coefficients indicating reduced irregularity and reduced dynamical variability in dementia. Other regions showed weaker or inconsistent gains\, suggesting a localized effect rather than a global one.\n\nDiscussion\nExSEnt provides a compact and interpretable measure for EEG-based dementia classification. The results suggest that dementia is associated not only with spectral slowing but also with reduced diversity of extrema timing and temporal-amplitude variability in LPFC dynamics. Because ExSEnt is explainable by design\, when combined with stability selection and sparse linear classification\, it yields robust biomarkers with direct physiological interpretation. These findings support ExSEnt as a promising candidate for explainable dementia screening and motivate future validation against cognitive severity measures.\n\n\n\nReferences\n[1] M. Penttil¨a\, J. V. Partanen\, H. Soininen\, P. Riekkinen\, Quantitative analysis of occipital EEG in different stages of Alzheimer’s disease\, Electroencephalography and Clinical Neurophysiology 60 (1985) 1–6. doi:10.1016/0013-4694(85)90942-3\n[2] S. Kamali\, F. Baroni\, P. Varona\, Exsent: Extrema-segmented entropy analysis of time series\, arXiv (2025). doi:10.48550/arXiv.2509.07751.\n[3] A. Miltiadous\, et al.\, A dataset of scalp EEG recordings of Alzheimer’s disease\, frontotemporal dementia and healthy subjects from routine EEG\, Data 8 (2023) 95. doi:10.3390/data8060095.\n[4] N. Meinshausen\, P. B¨uhlmann\, Stability selection\, Journal of the Royal Statistical Society Series B: Statistical Methodology 72 (2010) 417–473. doi:10.1111/j.1467-9868.2010.00740.x.\n\nAcknowledgement\nThis work was supported by grants PID2024-155923NB-I00 and CPP2023-010818.\n\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:36064608f0a40a49c7a3da500cf327ec
URL:http://cns2026.sched.com/event/36064608f0a40a49c7a3da500cf327ec
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SUMMARY:P132: A Knowledge Integration Workflow to Define Functional Interactomes in Neural Systems
DESCRIPTION:Introduction\nThis&nbsp\;study&nbsp\;addresses the need for&nbsp\;standard workflows and best practices&nbsp\;in&nbsp\;developing&nbsp\;evidence-based&nbsp\;computational tools and&nbsp\;models&nbsp\;in neuroscience&nbsp\;[1].&nbsp\;Linking multiscale molecular and&nbsp\;cellular&nbsp\;interactions&nbsp\;(e.g.\,&nbsp\;between&nbsp\;neurons and glia) are yet to be mainstream&nbsp\;in Systems Neuroscience. As a step in this direction\, we present an organized workflow to&nbsp\;consolidate&nbsp\;causal&nbsp\;evidence of&nbsp\;intermolecular\,&nbsp\;multicellular&nbsp\;and multifunctional&nbsp\;crosstalk\,&nbsp\;and&nbsp\;to develop&nbsp\;novel&nbsp\;functional interactomes in neural systems (FINS).&nbsp\;As&nbsp\;proof-of-concept\, we&nbsp\;apply&nbsp\;our&nbsp\;workflow&nbsp\;to&nbsp\;test the hypothesis that neuroinflammatory&nbsp\;and excitability&nbsp\;functions are in a&nbsp\;closed&nbsp\;loop of&nbsp\;multicellular&nbsp\;molecular&nbsp\;interactions&nbsp\;between neurons and microglia.\n\n\nMethods\nOur&nbsp\;workflow involves&nbsp\;1)&nbsp\;screening&nbsp\;primary research articles (PRAs)\,&nbsp\;2) extracting&nbsp\;structured meta-summaries (SMS)\, 3)&nbsp\;generating&nbsp\;the FINS network graph model. We screened&nbsp\;&gt\;120 heterogenous&nbsp\;published&nbsp\;studies&nbsp\;from&nbsp\;2002-2025\,&nbsp\;of which&nbsp\;65&nbsp\;reporting&nbsp\;validated&nbsp\;causal functional associations&nbsp\;were included. Diverse features of&nbsp\;biomolecules&nbsp\;such as&nbsp\;functional identity\,&nbsp\;cell type\,&nbsp\;experimental&nbsp\;methodology\,&nbsp\;species\,&nbsp\;and brain regions&nbsp\;were curated.&nbsp\;We&nbsp\;identified&nbsp\;pleiotropic actions of&nbsp\;activator molecules&nbsp\;on&nbsp\;targets&nbsp\;at&nbsp\;the&nbsp\;molecular (expression and function)\, cellular (excitability\, cell survival)\, and&nbsp\;neural circuit&nbsp\;levels (synaptic effects).&nbsp\;We then assembled pairwise interactors to&nbsp\;create&nbsp\;a network graph using the open-source&nbsp\;Cytoscape&nbsp\;software.\n\nResults\nThe resulting FINS network revealed&nbsp\;a&nbsp\;non-random\, hub-like topology (see&nbsp\;Figure. 1).&nbsp\;We&nbsp\;introduce a Functional Interaction Score (FIS) that&nbsp\;encodes edge thickness&nbsp\;to&nbsp\;capture the magnitude and&nbsp\;direction&nbsp\;of literature evidence&nbsp\;between two nodes/interactors.&nbsp\;Thicker edges correspond to greater reproducibility of empirical effects.&nbsp\;Furthermore\, edge annotations encode activator-mediated&nbsp\;increase (solid lines) v/s decrease (dashed lines) in target&nbsp\;function.&nbsp\;Node size encodes&nbsp\;total PRAs that&nbsp\;report&nbsp\;the node. Overall\, the proinflammatory&nbsp\;cytokines\, particularly TNF-α\,&nbsp\;emerge as&nbsp\;activator hubs with&nbsp\;ion channel&nbsp\;proteins&nbsp\;mediating neural excitability (e.g.\, Nav1.8)\, as convergence points&nbsp\;of&nbsp\;these&nbsp\;inflammatory mediators&nbsp\;between neurons and microglia.\n\nDiscussion\nThe&nbsp\;systematic pipeline of activities can be widely adopted to develop multiscale network models of molecular interactions that&nbsp\;integrate diverse&nbsp\;evidence&nbsp\;into unified network&nbsp\;graphs.&nbsp\;Unlike predicted networks\, our FINS model&nbsp\;represents&nbsp\;a validated network of molecular interactors across&nbsp\;the brain&nbsp\;scale.&nbsp\;The case of neuroimmune crosstalk demonstrated in this study identifies significant neuroimmune modulation of neural excitability by microglial cytokines\, which alter the expression and function of intrinsic and synaptic ion channels in neurons. This framework provides a new&nbsp\;direction for Systems&nbsp\;Neuroscience&nbsp\;to&nbsp\;combine&nbsp\;neural circuit-level&nbsp\;biomolecular&nbsp\;interactors&nbsp\;to investigate nervous system function and dysfunction.\n\nFigure 1.&nbsp\;A FINS model of neuroimmune crosstalk. Nodes represent biomolecules\, node shape indicates the cell type in which one or more studies reported its localization. The node size corresponds to the number of PRAs in which the node was studied. The node color correspond to the functional category assigned in the nervous system. Edge represents pairwise interaction. Their color shows interaction types\, a​References\nMcDougal RA\,&nbsp\;Bulanova&nbsp\;AS\, Lytton WW (2016). Reproducibility in Computational Neuroscience Models and Simulations. IEEE Trans Biomed Eng. 2016 Oct\;63(10):2021-35.&nbsp\;doi: 10.1109/TBME.2016.2539602.\n\n\nAcknowledgement\nThis work was partly supported by the&nbsp\;Leslie K\u202fWynston\u202fSummer Research Assistantship\, awarded&nbsp\;to MZY&nbsp\;by the California State University Long Beach (CSULB) College of Natural Sciences and Mathematics&nbsp\;(CNSM)\,&nbsp\;and by the CSULB CNSM&nbsp\;new faculty&nbsp\;startup funds to SV. We thank&nbsp\;many&nbsp\;student contributors for their&nbsp\;assistance&nbsp\;in&nbsp\;data&nbsp\;consolidation.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:81e6c7a7854b543021b46c10ce46f5f4
URL:http://cns2026.sched.com/event/81e6c7a7854b543021b46c10ce46f5f4
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SUMMARY:P133: A Learning-Rule-Independent Method for Evaluating Memory Capacity in Biophysical Neuron Models
DESCRIPTION:Introduction\nThe spatial distribution of parallel fiber (PF) synaptic inputs to a Purkinje cell (PC) induces complex intracellular dynamics along the dendrites\, enabling nonlinear pattern discrimination [1]. However\, the relationship between PC morphology and memory capacity remains unclear. Although simulations of biophysical neuron models incorporating morphology are effective for such investigations\, current models lack long-term depression (LTD) implementation. Therefore\, we propose a learning-rule-independent method to evaluate memory capacity. In this study\, we apply the "Survival Game [2]" from machine learning to biophysical models to evaluate neuronal memory capacity without explicit learning rules.\n\nMethods\nTo evaluate the PC model\'s memory capacity\, we presented input patterns by activating 30 of 100 dendritic PF synapses\, using the following procedure: (1) 50\,000 PC models were prepared with 100 random PF synaptic weights. (2) 30 synapses were randomly activated for current injection\, and a teacher label was randomly assigned. (3) Models whose outputs (firing or not) matched the label "survived"\; others were eliminated. (4) All models received the same 30 inputs to determine survival. (5) Steps (2)-(4) were repeated until all models were eliminated. Theoretically\, with a sufficiently large number of models\, the number of rounds corresponds to the memory capacity.\n\n\nResults\nWe constructed the PC model using morphology data [3] and ion channel data [4]\, and performed simulations on the supercomputer "Fugaku." First\, the simulation results showed that the PC model memorized an average of 6.7 patterns (SD 1.9) when selecting 30 active synapses out of 100. Next\, as the number of PF synapses increased from 100 to 400\, the memory capacity increased in proportion to the input dimension. This trend follows the theoretical predictions in [2]\, validating our evaluation method. Furthermore\, evaluating memory capacity using a random subset of 500 models yielded results nearly identical to the full-scale simulation of 50\,000 models.\n\n\nDiscussion\nOur proposed method offers two primary advantages. First\, it is independent of specific neuronal learning rules. While biophysical simulations are effective\, the realistic implementation of diverse learning rules (e.g.\, Hebbian\, STDP\, LTP/LTD\, or dopaminergic modulation) for different neuron types is difficult. Our method bypasses that complexity\, allowing for a direct evaluation of the fundamental relationship between neuronal morphology and memory capacity. Second\, the memory capacity for complex tasks\, such as the 30-out-of-100 synapse activation\, can be estimated with hundreds of simulations. Using parallel simulators and GPUs\, these simulations can be completed within several hours without requiring a supercomputer.\n\n\nReferences\n[1] Tamura\, K.\, et al. (2023). Discrimination and learning of temporal input sequences in a cerebellar Purkinje cell model.&nbsp\;Front. Cell. Neurosci. 17:1075005.\n[2] Okada\, M. (2005). [Statistical mechanics of ensemble learning]. Ansanburu gakushu no toukei rikigaku (in Japanese). Watanabe\, S. (Eds). [Theory and implementation of learning systems] Gakushu shisutemu no riron to jitsugen (pp. 132-159).&nbsp\;Morikita Publishing Co.\, Ltd.&nbsp\;\n[3] Nedelescu\, H.\, et al. (2018).&nbsp\;Regional differences in Purkinje cell morphology in the cerebellar vermis of male mice.&nbsp\;Neurosci. Res.&nbsp\;96:1476–1489.\n[4] Masoli\, S.\, et al. (2024).&nbsp\;Human Purkinje cells outperform mouse Purkinje cells in dendritic complexity and computational capacity.&nbsp\;Commun. Biol. 7:5.&nbsp\;\n\nAcknowledgement\nThis research was supported by AMED under Grant Number JP25wm0625418h0001.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:ba21efc66b8f765dff4f4cc8f58186e9
URL:http://cns2026.sched.com/event/ba21efc66b8f765dff4f4cc8f58186e9
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SUMMARY:P134: Coupling TMS induced electric fields to neural state variables
DESCRIPTION:Introduction\nTranscranial magnetic stimulation (TMS) of the human primary motor cortex (M1) is well studied\, aided by abundant possible experimental readouts\, such as EEG and motor evoked potentials (MEP). One key readout are DI-waves\, reflecting the TMS-evoked output of M1 and characteristically depend on TMS parameters (e.g.\, coil orientation and pulse strength). Previous modeling studies approximate TMS inputs in a more abstract way as currents or synaptic inputs\, thereby missing the biophysical coupling between TMS induced electric fields and the neural states. This study uses cable simulations of realistic neuron morphologies to couple electric fields (and thereby stimulation parameters) to the firing patterns of cortical output neurons [1].\n\n\nMethods\nThe coupling model leverages a number of realistic neuronal morphologies for simulating the elicitation of action potentials in upstream cells projecting onto the cortical output neurons\, the spreading of the activation across the axonal arbors of these cells\, the synaptic coupling onto the output neurons\, and the dendritic dynamics in these cells. Averaging over cell morphologies and orientations leads to a statistical kernel representation linking the field to the average input kernel entering the somata of the output neurons. An example M1 cortical circuit is studied\, in which TMS stimulates layer 2/3 excitatory and inhibitory neurons that project synapses onto layer 5 corticospinal neurons.\n\n\nResults\nResults indicate that TMS induces unique directionally sensitive distributions of synaptic outputs in time and space for each cell type. Directional and dosage sensitivity carries forward to the dendritic current flowing into layer 5 cells\, which can then be translated into cortical output firing patterns.\n\n\nDiscussion\nThe proposed coupling model provides a novel architecture to translate electric fields from TMS into activation functions that alter neural states and may serve as inputs to cortical circuit modeling. The study of other brain regions is achievable through an alternate choice of cell morphologies\, cell locations\, and circuit design.\n\n\nReferences\n\n\n1.&nbsp\;&nbsp\;&nbsp\;&nbsp\; Miller\, A.\, Knösche\, T.R.\, Weise\, K. (2026). A coupling model of transcranial magnetic stimulation induced electric fields to neural state variables. Brain Stimulation\, in press (preprint at https://www.biorxiv.org/content/10.1101/2025.08.11.669601v1)\n\nAcknowledgement\nThe authors thank Torge Worbs for consultation and support in developing the model interface to 610 NEURON. This study has received support from BMBF grant 01GQ2201.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:392562b71050461789725df689e9d93e
URL:http://cns2026.sched.com/event/392562b71050461789725df689e9d93e
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SUMMARY:P135: A Dendritic Brunel Network for Studying NMDA-Driven Working Memory
DESCRIPTION:Introduction\nMaintaining a working memory of sensory inputs is a fundamental neural computation\, widely thought to rely on synaptic plasticity or dedicated attractor dynamics [1\,2]. A complementary substrate is provided by dendritic subunits equipped with NMDA receptors\, whose slowly decaying\, voltage-gated conductances allow information to persist within individual neurons beyond the membrane timescale. To study how NMDA-driven dendritic dynamics contribute to working memory in a recurrent network\, we introduce a dendritic extension of the classical Brunel network [3]\, yielding a minimal system with realistic NMDA kinetics that preserves the well-characterized asynchronous irregular dynamics of the original model.\n\n\nMethods\nExcitatory neurons are modeled with one somatic and five dendritic compartments incorporating AMPA/NMDA receptor kinetics. In this neuron model\, NMDA receptors provide voltage-gated\, slowly decaying conductances. Excitatory neurons are organized into clusters with tunable inter- and intra-cluster connection probabilities\; inhibitory neurons provide global inhibition targeting random dendritic compartments. MNIST digit images are presented by converting pixel intensities to Poisson spike train firing rates. Network activity serves as a reservoir\, and a linear readout trained on spike rates classifies digit identity. Decoding accuracy is evaluated across post-stimulus time windows to quantify the temporal persistence of stimulus information.\n\n\nResults\nSimulations are ongoing\; early observations suggest that excitatory clustering modulates the emergence of NMDA-driven activity in the dendritic compartments and extends the temporal persistence of stimulus-specific population dynamics beyond the timescales seen in the standard Brunel network. Preliminary decoding results indicate that stimulus information is retained over longer post-stimulus intervals\, with above-chance classification accuracy persisting at delays where the standard network falls to chance.\n\n\nDiscussion\n\nReferences\n[1] Mongillo\, G.\, Barak\, O.\, & Tsodyks\, M. (2008). Synaptic theory of working memory. Science.&nbsp\;https://www.science.org/doi/10.1126/science.1150769\n[2] Amit\, D. J.\, & Brunel\, N. (1997). Model of Global Spontaneous Activity and Local Structured Activity During Delay Periods in the Cerebral Cortex. Cerebral Cortex.&nbsp\;https://doi.org/10.1093/cercor/7.3.237\n[3] Brunel\, N. (2000). Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking Neurons. Journal of Computational Neuroscience.&nbsp\;https://doi.org/10.1023/A:1008925309027\n\nAcknowledgement\nNeuroSys as part of the initiative “Clusters4Future” is funded by the Federal Ministry of Education and Research BMBF (03ZU2106CB).\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:ccc7c30ed8c6572d899fe90504f15d4e
URL:http://cns2026.sched.com/event/ccc7c30ed8c6572d899fe90504f15d4e
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SUMMARY:P136: Implementation of Dendritic Hierarchial Scheduling for the Neulite kernel
DESCRIPTION:Introduction\nNeulite is a lightweight simulator designed for biophysically detailed neuron and network models. It consists of a frontend module\, Bionetlite\, and a numerical kernel. While the original kernel was optimized for Japan's flagship Supercomputer Fugaku\, a CPU-based system\, most contemporary supercomputers use CPU/GPU hybrid architectures. To adapt to these environments\, we have developed a new kernel specifically for GPUs.\n\nMethods\nWe implemented Dendritic Hierarchical Scheduling (DHS)\, a GPU-based scheduling algorithm designed to solve linear equations on tree structures\, such as dendritic trees [2]. To evaluate its performance\, we utilized a balanced random network model\, varying both the number of neurons and the threads per neuron. The excitatory-to-inhibitory ratio was maintained at 4:1. Benchmarks were conducted on a desktop system equipped with an Intel Core i5-14600KF CPU and an NVIDIA GeForce RTX 3070 Ti GPU.\n\n\nResults\nInitially\, we evaluated the effect of thread count per neuron by fixing the total number of neurons at 8\,192. Under this condition\, the optimal performance was achieved with 16 threads per neuron. Subsequently\, using this optimal thread count\, we varied the number of neurons to assess scalability. The results demonstrated that the GPU-based version consistently outperformed the CPU version as the network size increased. Specifically\, at a scale of 8\,192 neurons\, the GPU implementation achieved a 16-fold speedup compared to the CPU version.\n\n\nDiscussion\nThese results suggest that DHS is an efficient algorithm for simulating biophysically detailed neuron models\, offering significant performance gains on GPU architectures.&nbsp\;As a next step\, we plan to evaluate the benchmark performance on more complex and biologically realistic network structures\, such as cortical column models.\n\nReferences\n\nAcknowledgement\nPart of this study was supported by AMED Brain/MINDS 2.0 (JP25wm0625406). We would like to thank Drs. Kaaya Akira and Rin Kuriyama for technical discussions.\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
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UID:c35d814518fc4465a7b751acb7fb3d86
URL:http://cns2026.sched.com/event/c35d814518fc4465a7b751acb7fb3d86
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SUMMARY:P137: A Spiking Neural Network Model of Hierarchical Reinforcement Learning in a Maze Navigation Task
DESCRIPTION:Introduction\nReinforcement learning (RL) is a learning mechanism that allows animals to acquire behaviors through trial and error\, and it is thought to be implemented in the brain's neural circuits. Hierarchical reinforcement learning (HRL)\, in which multiple RL systems are organized hierarchically\, has been hypothesized to improve learning efficiency by decomposing long action sequences into shorter subgoal-directed processes [1]. However\, whether such hierarchical organization improves learning efficiency in spiking neural network models has not been fully examined. In this study\, we constructed an HRL model based on spiking neurons [2]\, and examined whether hierarchical organization improves learning efficiency in a two-dimensional maze task.\n\n\nMethods\nThe hierarchical model consisted of a meta-controller as the higher-level process and a controller as the lower-level process. The meta-controller selected a subgoal from multiple candidates\, whereas the controller generated actions to reach it. These two processes were associated with the basal ganglia and cerebellum\, respectively\, based on their functional roles in action selection and motor control. External and internal rewards were given for reaching the final goal and subgoal\, respectively. To evaluate the effect of hierarchy\, we compared this model with a non-hierarchical condition in which only the controller learned to reach the final goal.\n\nResults\nThe hierarchical model solved the continuous two-dimensional maze task more efficiently than the non-hierarchical model. In the hierarchical condition\, the agent gradually acquired subgoal-directed behavior and reduced the number of steps required to reach the final goal. In contrast\, in the non-hierarchical condition\, the controller alone had to learn actions toward the final goal from sparse external rewards\, resulting in inefficient exploration and slower improvement. These results indicate that introducing hierarchy enabled the agent to learn an effective route to the goal more efficiently.\n\n\nDiscussion\nThe improved performance of the hierarchical model suggests that decomposing a long navigation task into shorter subgoal-directed processes can facilitate learning in a spiking RL framework. By selecting intermediate subgoals\, the higher-level process reduced the difficulty of learning long action sequences from sparse rewards\, while the lower-level process learned actions for reaching each subgoal. This supports the hypothesis that hierarchical organization may contribute to efficient behavior acquisition in the brain. The model also provides a computational framework for examining how brain-inspired action-selection and control processes can support hierarchical learning.\n\n\nReferences\n[1] Kulkarni\, T. D.\, Narasimhan\, K. R.\, Saeedi\, A.\, & Tenenbaum\, J. B. (2016).&nbsp\;Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation&nbsp\;(arXiv:1604.06057). arXiv.&nbsp\;https://doi.org/10.48550/arXiv.1604.06057\n\n[2] Frémaux\, N.\, Sprekeler\, H.\, & Gerstner\, W. (2013). Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons.&nbsp\;PLoS Computational Biology\,&nbsp\;9(4)\, e1003024.&nbsp\;https://doi.org/10.1371/journal.pcbi.1003024\n\nAcknowledgement\nThis study was supported by MEXT/JSPS KAKENHI Grant Number 22H05161.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:f1403658bb1f2f023028abaf50c0cab9
URL:http://cns2026.sched.com/event/f1403658bb1f2f023028abaf50c0cab9
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DTEND:20260714T220000Z
SUMMARY:P138: The Functional Contribution of Synaptic Plasticity in the Deep Cerebellar Nuclei to Real-Time Adaptive Robot Control
DESCRIPTION:Introduction\nAlthough considerable progress has been made\, robotic motor control still lacks the adaptability of biological motor systems in dynamic environments [1]. Replicating the flexibility of biological systems remains a challenge in neurorobotics. The cerebellum is crucial for motor control [2]\, suggesting it can inform the design of adaptive robotic controllers. Since Marr’s seminal theory [3]\, most cerebellar models focus on parallel fibre (PF)–Purkinje cell (PC) plasticity. However\, other forms of cerebellar plasticity exist [4] and remain underexplored in real-time robotic implementations. We investigated the functional contribution of synaptic plasticity in the deep cerebellar nuclei (DCN) in real-time cerebellum-based robotic control.\n\n\nMethods\nWe extended a validated cerebellar spiking network model for robotic control [5]. The model comprises 61\,200 leaky integrate-and-fire neurons: 60\,000 granule cells\, 600 PCs and 600 DCN neurons. Mossy fibres (MFs) encode desired and actual kinematic signals\, while climbing fibres convey error signals. The controller operates in real time with a compliant Baxter robot via intermediate modules converting analogue signals into spikes and vice versa. The robot acquires the target trajectory through activity-dependent plasticity at PF–PC synapses. As experimental studies suggest interactions between PF–PC learning and plasticity in the deep cerebellar nuclei\, we implemented synaptic plasticity at MF–DCN and PC–DCN synapses.\n\n\nResults\nWe compared the mean absolute error (MAE) of different target trajectories across several network configurations: a homosynaptic learning rule at MF–DCN synapses\, a heterosynaptic learning rule at MF–DCN synapses\, and a homosynaptic learning rule at PC–DCN synapses. These were evaluated against a baseline network in which only PF–PC plasticity was active. The three experimental conditions were assessed during the acquisition of a circle-like trajectory. The results show that incorporating plasticity in the DCN improves performance when the initial MF–DCN or PC–DCN synaptic weights are not optimally tuned. Networks with DCN plasticity achieved lower MAE without requiring pre-optimisation of synaptic weights.\n\n\nDiscussion\nThese results suggest that plasticity in the deep cerebellar nuclei contributes to motor adaptation alongside cortical learning mechanisms. When DCN plasticity is absent\, achieving low error requires careful pre-optimisation of synaptic weights to match the robot’s joint dynamics and actuator properties. In contrast\, enabling DCN learning allows the cerebellar controller to adapt synaptic weights online\, reducing the need for manual parameter tuning. This supports the hypothesis that cerebellar nuclear plasticity plays a functional role in adaptive motor control.\n\n\nReferences\n[1] Husbands\, P.\, Shim\, Y.\, Garvie\, M.\, Dewar\, A.\, Domcsek\, N.\, Graham\, P.\, ... & Philippides\, A. (2021). Recent advances in evolutionary and bio-inspired adaptive robotics: Exploiting embodied dynamics.&nbsp\;Applied Intelligence\,&nbsp\;51(9)\, 6467-6496.\n[2] Glickstein\, M.\, & Doron\, K. (2008). Cerebellum: connections and functions. The Cerebellum\, 7(4)\, 589-594.\n[3] Marr\, D. (1969). A theory of cerebellar cortex. The Journal of physiology\, 202(2)\, 437-470.\n[4] Gao\, Z.\, Van Beugen\, B. J.\, & De Zeeuw\, C. I. (2012). Distributed synergistic plasticity and cerebellar learning. Nature Reviews Neuroscience\, 13(9)\, 619-635.\n[5] Abadía\, I.\, Naveros\, F.\, Garrido\, J. A.\, Ros\, E.\, & Luque\, N. R. (2019). On robot compliance: A cerebellar control approach. IEEE Transactions on Cybernetics\, 51(5)\, 2476-2489.\n\nAcknowledgement\nNY was funded by a grant from the Academy of Medical Sciences.\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:f8ff853d702e6a0262ff47bddf04d500
URL:http://cns2026.sched.com/event/f8ff853d702e6a0262ff47bddf04d500
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260714T200000Z
DTEND:20260714T220000Z
SUMMARY:P139: Spatial neuroaesthetics in perception of topological and visual characteristics of natural landscapes and in route decision-making
DESCRIPTION:Introduction\nA comprehensive behavioural decision can be made based on a preliminary aesthetic assessment that harmonizes a variety of factors. Subjective prediction can cover at least 20-30% of brain regions\, from sensory and motor areas to the cerebral cortex [1]. To study such processes\, neuroaesthetic tools are being developed that combine computational rating models with neurophysiological methods [2].\n\nIn our work\, we analysed how an aesthetic assessment of a location affects the choice of route for a traveller through natural landscapes. We have shown that computational neuroaesthetics\, supplemented by spatial trajectory analysis\, can be used to identify significant landscape factors and to predict attractiveness of tourist destinations.\n\nMethods\nOur goal was to identify indicators that determine the aesthetic appeal of natural attractions. We calculated topological and visual characteristics for three different tourist sites near Bishkek\, Kyrgyzstan\, with different Google Maps ratings and different frequencies of mentions on Internet (FM) (Figure 1):\n\nA) topological features of routes and average densities of tourist flows (attendance) based on GPS tracks of activity tracking service (https://www.strava.com)\; \nB) visibility pools and visual perception trails\, softness of relief lines and spot colour balance based on remote sensing data. \nThe analysis was performed using QGIS spatial tools (https://qgis.org).\n\nResults\nWe performed calculations for three tourist locations and compared the results with 6 non-tourist locations near Bishkek. Then we identified topological and visual indicators that differed by at least 25% between tourist sites and other locations.\n\nBased on our analysis\, we determined the "comfortable" properties of the locations\, such as: visual openness of space\, softness of lines\, neutral natural colours. \nSimilar quality metrics of visual walkability perception in urban pedestrians have been found by Li Y et al. using panoramic street view images\, virtual reality\, and deep learning [3].\nThe applied methods and identified indicators can be used in machine learning tasks of artificial neural networks to detect high-rated tourist areas.\n\nDiscussion\nWhen receiving information from different sensory systems\, the brain processes it\, forming a complex response to multi-layered data sets.\n\nAesthetic ratings of natural scenes can correlate with empirical data on visual comfort\, while aesthetic preferences may be driven by optimization of decision-making processes in favour of lower-energy states of brain activity [4].\nThis is consistent with tourists\' reviews of natural attractions near Bishkek as "soothing" and "relaxing" locations.\nIn future studies\, we plan to continue studying the influence of aesthetic characteristics of complex spatial stimuli on route selection\, including comparing data on brain activity and data on movement trajectories.\n\nFigure 1.&nbsp\;A1\, A2: GPS tracks and photo of the location "Ala-Archa" (FM – 1.6 million\, attendance 215 people/hour\, rating 4.7)\, B1\, B2: GPS tracks and photo of the location "Sky Bridge" (FM – 39.8 thousand\, attendance 36 people/hour\, rating 4.5). C1\, C2: GPS tracks and a photo of the location "Raspberry Gorge" (FM 2.9 thousand\, attendance 4 people/hour\, rating 4.0).​References\n1. Findling\, C.\, et al. (2025). Brain-wide representations of prior information in mouse decision-making. Nature\, 645\, 192–200. https://doi.org/10.1038/s41586-025-09226-1\n\n2. Li\, R.\, & Zhang\, J. (2020). Review of computational neuroaesthetics: bridging the gap between neuroaesthetics and computer science. Brain Informatics\, 7\, 16. https://doi.org/10.1186/s40708-020-00118-w\n3. Li\, Y.\, et al. (2022). Measuring visual walkability perception using panoramic street view images\, virtual reality\, and deep learning. Sustain Cities Soc\, 86\, 104140. https://doi.org/10.1016/j.scs.2022.104140\n4. Tang\, Y.\, et al. (2025). Less is more: Aesthetic liking is inversely related to metabolic expense by the visual system. PNAS Nexus\, 4\, pgaf347. https://doi.org/10.1093/pnasnexus/pgaf347\n\n\n\nAcknowledgement\nWe gratefully acknowledge the applications and tools provided by QGIS. This platform is constantly evolving\, expanding the possibilities of spatial analysis for all fields of knowledge.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:d73d48074b6549d9db0f8f808de03249
URL:http://cns2026.sched.com/event/d73d48074b6549d9db0f8f808de03249
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260714T200000Z
DTEND:20260714T220000Z
SUMMARY:P140: Unsupervised Machine Learning Analysis of Extracellular Waveforms
DESCRIPTION:Introduction\nAdvances in transcriptomic\, morphological\, and electrophysiological techniques have led to more comprehensive characterization of cortical cell types. However\, in the absence of “ground truth” labels\, it is difficult to classify single units into neuron types based on extracellular action potentials (EAPs) recorded from high-density electrode arrays. Previously published classifiers rely on extracted features\, machine learning (ML) algorithms\, and dimensionality reduction approaches that produce a range of EAP type classes (Haynes\, 2024\; Jia\, 2019\; Lee\, 2021). We extend these approaches by developing an unsupervised ML framework that relies on spatiotemporal patterns of multi-channel waveforms.\n\n\nMethods\nWe developed an unsupervised ML framework to analyze spiking events from extracellular recordings. For each event\, a spatiotemporal kernel waveform is constructed with a 1.5 ms temporal window that is centered on a spike time\, based on waveforms from channels 200 um above and below the channel with the largest peak amplitude. We analyzed thousands of spiking events and applied KMeans clustering on two different representations of the data: 1) extracted spatiotemporal features and 2) vectorized multi-channel waveforms. We applied our framework to extracellular recordings in the auditory cortex of an awake common marmoset (Callithrix jacchus). We compare these results to previously published studies that cluster EAPs from rodent (Jia\, 2019).\n\n\nResults\nBoth the feature-based and the vectorized data representations produced well-defined separation of clusters of spiking events recorded from marmoset auditory cortex. For the feature based approach\, EAP duration and vertical spread across channels were the most distinguishing features across clusters. Clustering based on vectorized multi-channel waveforms resulted in greater variability in feature distributions\, cortical depth profiles\, and spatiotemporal patterns across cluster groups.\n\n\nDiscussion\nTogether\, these findings suggest the presence of diverse neural subtypes in the marmoset auditory cortex that exhibit distinct extracellular signatures. Our data-driven framework demonstrates that unsupervised machine learning approaches reveal physiologically meaningful variation in EAP waveforms\, offering a scalable approach to comparative analysis of cortical organization and function. Moreover\, this framework is applied to extracellular recordings from other species to identify distinct waveform features and identify which aspects of&nbsp\; spatiotemporal patterns are conserved across species.\n\n\nReferences\n\nHaynes\, V. R.\, Zhou\, Y.\, & Crook\, S. M. (2024). Discovering optimal features for neuron-type identification from extracellular recordings. Frontiers in Neuroinformatics\, 18\, 1303993. https://doi.org/10.3389/fninf.2024.1303993\nJia\, X.\, Siegle\, J. H.\, Bennett\, C.\, Gale\, S. D.\, Denman\, D. J.\, Koch\, C.\, & Olsen\, S. R. (2019). High-density extracellular probes reveal dendritic backpropagation and facilitate neuron classification. Journal of Neurophysiology\, 121(5)\, 1831–1847. https://doi.org/10.1152/jn.00680.2018\nLee\, E. K.\, Balasubramanian\, H.\, Tsolias\, A.\, Anakwe\, S. U.\, Medalla\, M.\, Shenoy\, K. V.\, & Chandrasekaran\, C. (2021). Non-linear dimensionality reduction on extracellular waveforms reveals cell type diversity in premotor cortex. eLife\, 10\, e67490. https://doi.org/10.7554/eLife.67490\n\n\nAcknowledgement\nThis research is support by the NIDCD (R01DC019278).\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:8e6215f1bec89ddd3b5bf12f58e7682b
URL:http://cns2026.sched.com/event/8e6215f1bec89ddd3b5bf12f58e7682b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260714T200000Z
DTEND:20260714T220000Z
SUMMARY:P141: Different training paradigms yield distinct learned structures in recurrent neural network models
DESCRIPTION:Introduction\nRNNs trained with backpropagation through time (BPTT) solve tasks through reliance on global error signals [1]\, while evolutionary algorithms and activity-dependent plasticity rules are gradient-free alternatives to solve the same tasks. How the choice of training paradigm biases the solutions that models arrive at remains unclear. Here we compare four paradigms—BPTT\, evolution strategies (ES) [2]\, a genetic algorithm (GA)\, and GA with Oja’s Hebbian plasticity (GA+Oja)—on n-back recall\, in which the network reports which of five symbols appeared n steps earlier. Beyond task accuracy\, we analyze the connectivity changes that occur under each paradigm\, finding that training method shapes structure far more than it determines performance.\n\n\nMethods\nWe trained RNNs (32\, 128\, 256 units) on a 5-symbol n-back task. BPTT employed gradient descent with the Adam optimizer (2000 iterations). ES estimated gradients from 128 perturbed copies of the network (perturbation σ = 0.02\, 500 generations). GA used tournament selection with neuron-level crossover and self-adaptive mutation (128 individuals\, 500 generations). GA+Oja co-evolved two Oja's rule parameters (learning rate and weight bound) which update recurrent weights via correlated activity\, enabling within-lifetime adaptation atop evolved connectivity. We tested n-back 1–4 steps\, measuring accuracy (chance accuracy = 20%)\, per-layer weight-change fractions\, effective rank of the recurrent weight matrix\, and total weight-change magnitude.\n\nResults\nAll methods converged to ~100% at 2-back\, although ES showed high variance at 1-back (~85%\; Fig. 1A). At 4-back\, ES maintained ~100%\, whereas GA declined to ~80% and GA+Oja to ~89%\; BPTT remained at ~100%. Connectivity analysis revealed a strikingly different structure despite comparable accuracy. BPTT produced low effective rank recurrent matrices (~8 at 1-back) that increased with difficulty (~26 at 4-back). In contrast\, evolutionary methods clustered at a higher rank (~16) with less variation across levels (Fig. 1B). BPTT increasingly allocated weight changes to the output layer with difficulty (~29% to ~43%). In comparison\, all evolutionary methods remained flat at ~21% (Fig. 1C).\n\n\nDiscussion\n\nGradient-based and evolutionary training produce comparably accurate networks with fundamentally different connectivity. BPTT finds low-rank recurrent solutions and progressively shifts learning toward output weights as difficulty increases\, consistent with efficient credit assignment [3]. Evolutionary methods converge on distributed\, high-rank solutions with uniform layer allocation — a qualitatively different regime. These results caution that studies inferring biological principles from trained RNNs may reflect the training algorithm rather than computational necessity. BPTT’s concentration of learning in output weights could mislead analyses of how recurrent dynamics support memory if the readout is not examined separately.\n\nFigure 1.&nbsp\;(A) Accuracy vs. n-back level: methods converge at 2-back\; ES maintains ~100% at 4-back\, whereas GA declines to ~80%. (B) BPTT effective rank increases from ~8 to ~26 with difficulty\; evolutionary methods cluster at ~16. (C) BPTT shifts learning to the output layer (+14%)\; evolutionary methods stay flat. 32 neurons\, mean ± std\, 3 seeds.​\n\nReferences\n\n1. Neftci\, E. O.\, Mostafa\, H.\, & Zenke\, F. (2019). Surrogate gradient learning in spiking neural networks. IEEE Signal Processing Magazine\, 36(6)\, 51–63. https://doi.org/10.1109/MSP.2019.2931595&nbsp\;\n2. Salimans\, T.\, Ho\, J.\, Chen\, X.\, Sidor\, S.\, & Sutskever\, I. (2017). Evolution strategies as a scalable alternative to reinforcement learning. arXiv:1703.03864. https://arxiv.org/abs/1703.03864&nbsp\;\n3. Izhikevich\, E. M. (2007). Solving the distal reward problem through linkage of STDP and dopamine signaling. Cerebral Cortex\, 17(10)\, 2443–2452. https://doi.org/10.1093/cercor/bhl152&nbsp\;\n\n\nAcknowledgement\nWe thank Dora Jiayue Li and the Pomona College Summer Undergraduate Research Program (SURP) for supporting early exploration on this project\, as well as the Pomona College Department of Neuroscience for their senior thesis support.\n\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:86e5eac021d18958cf71a8f95e525a8e
URL:http://cns2026.sched.com/event/86e5eac021d18958cf71a8f95e525a8e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260714T200000Z
DTEND:20260714T220000Z
SUMMARY:P142: Building neural manifolds from membrane biophysics and circuit topology
DESCRIPTION:Introduction\nNeural population activity is often described as evolving on low-dimensional manifolds that structure neural computation and behaviour. These manifolds are typically identified empirically using dimensionality reduction techniques applied to large-scale neural recordings (Gallego et al.\, 2017). While such approaches successfully reveal structured population dynamics\, they are largely agnostic to the underlying biophysical mechanisms that generate neural activity. Consequently\, the relationship between neural manifolds and the building blocks of neural tissue – morphology\, ion channel\, connectivity\, synapse physiology\, etc. – remains undefined.\n\nMethods\nHere\, we propose a framework that constructs neural population dynamics from biophysical elements. Combing cable theory with Hodgkin-Huxley membrane dynamics\, axial current along neuronal processes can be balanced by total membrane current across each membrane segment. Linearizing over axial and membrane kinetic elements&nbsp\;governed by experimentally measurable conductances and gating variables\, the total membrane current is expressed by contributions from voltage-gated ion channels\, ligand-gated synaptic channels\, electrical synapses\, and electrogenic transport mechanisms\, including ion pumps and exchangers. Thus longer-timescale membrane dynamics may be explicitly taken into account.\n\nResults\nWe validate this framework in the compact\, stereotypic\, and connectomically-defined nervous system of C. elegans&nbsp\;(White et al.\, 1986). Our analysis focuses on the locomotor rich club neurons\, which coordinate transitions between forward and reverse states (Fieseler et al.\, 2025). Biophysical parameters are estimated from current-clamp electrophysiological recordings that constrain passive membrane properties and voltage-gated conductances. Population activity in this circuit is further constrained using whole-brain Ca²⁺ imaging measurements of the same neurons during behaviour (Fieseler et al.\, 2025). Together\, these data provide experimentally grounded estimates of the membrane and coupling parameters governing the dynamical system.\n\n\nDiscussion\nBy deriving network dynamics directly from experimentally measurable cellular and circuit properties\, this framework provides a mechanistic bridge between membrane biophysics and population-level neural dynamics. In this view\, neural manifolds are not only empirical low-dimensional decompositions of neural activity\, but also naturally emerging physical organizations of neural tissue: a graphical interaction between membrane dynamics and neuronal morphology. This approach therefore links cellular physiology\, circuit topology\, and population dynamics within a unified dynamical system to provide a principled foundation for interpreting neural manifolds as geometric consequences of biophysical mechanisms.\n\n\nReferences\nGallego\, J. A.\, Perich\, M. G.\, Miller\, L. E.\, & Solla\, S. A. (2017). Neural Manifolds for the Control of Movement. Neuron\, 94(5)\, 978-984. https://doi.org/10.1016/j.neuron.2017.05.025White\, J. G.\, Southgate\, E.\, Thomson\, J. E.\, & Brenner\, S. (1986). The structure of the nervous system of the nematode Caenorhabditis elegans. Philosophical Transactions of the Royal Society of London. B\, Biological Sciences\, 314(1165)\, 1-340. https://doi.org/10.1098/rstb.1986.0056Fieseler\, C.\, Lev\, I.\, Rey\, U.\, Hille\, L.\, Brenner\, H.\, & Zimmer\, M. (2025). An intrinsic neuronal manifold underlies brain-wide hierarchical organization of behavior in C. elegans. bioRxiv\, 2025.2003.2009.642241. https://doi.org/10.1101/2025.03.09.642241\nAcknowledgement\nWe would like to thank the European Research Commission (Advanced Grant) and Austrian Science Fund Cluster of Excellence (Neuronal Circuits in Health and Disease) for funding.\n
CATEGORIES:POSTERS
LOCATION:Ballroom B2\, Halifax\, NS\, Canada
SEQUENCE:0
UID:f86a2ffdfe9b7897a13f6017734950ee
URL:http://cns2026.sched.com/event/f86a2ffdfe9b7897a13f6017734950ee
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260715T003000Z
DTEND:20260715T025900Z
SUMMARY:Party
DESCRIPTION:\n
CATEGORIES:RECEPTION/PARTY
LOCATION:TBA\, Halifax\, NS\, Canada
SEQUENCE:0
UID:cd23dc985604576b87b0bcbb072fee0f
URL:http://cns2026.sched.com/event/cd23dc985604576b87b0bcbb072fee0f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260715T111500Z
DTEND:20260715T200000Z
SUMMARY:Registration
DESCRIPTION:\n
CATEGORIES:REGISTRATION/ORGANIZATION
LOCATION:TBA\, Halifax\, NS\, Canada
SEQUENCE:0
UID:10bad755aadd34f25ee07acb00fc6a4a
URL:http://cns2026.sched.com/event/10bad755aadd34f25ee07acb00fc6a4a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260715T120000Z
DTEND:20260715T153000Z
SUMMARY:Computational modeling of neuromodulation technologies
DESCRIPTION:Various neuromodulation modalities have been developed to gain understanding of brain function and to treat a wide range of neurological disorders. Examples of brain stimulation technologies that use electric or magnetic fields are deep brain stimulation\, vagus nerve stimulation\, transcranial direct or alternating current stimulation\, transcranial magnetic stimulation and temporal interference neuromodulation. Furthermore\, transcranial focused ultrasound neuromodulation targets brain or peripheral nervous system regions with ultrasonic pressure waves. Finally\, optogenetics relies on light to control neuronal activity\, after expressing light-sensitive ion channels or pumps in the targetted neuronal cells. Each of these neuromodulation modalities has its advantages and drawbacks\, e.g.\, in terms of spatial and temporal resolution\, energy efficiency\, invasiveness\, efficacy\, penetration depth\, cell type selectivity\, etc.\n\nComputational models have been developed for each of these techniques\, to improve our understanding of the underlying mechanisms of the various neuromodulation technologies and to enable simulation-based optimization and exploration of the parameter space in neural engineering studies (e.g.\, temporal protocols\, optimal opsin properties\, transducer and electrode designs\, etc.). In this workshop the similarities and differences in computational methodology to investigate the various neuromodulation modalities is discussed. &nbsp\;\n\nCNS 2026 Workshop &nbsp\;- July 15th - Halifax\nComputational modeling of neuromodulation technologies\n9:00 – 9:20&nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; Jan Antolik (Charles University\, Czech Republic)&nbsp\;\nModelling spatio-temporal optogenetic stimulation of primary visual cortex\n9:20 – 9:40 &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\;&nbsp\;Laila Weyn (Ghent University\, Belgium)&nbsp\;\nModelling potassium based optogenetic approaches for seizure suppression9:40 – 10:00 &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; Joaquín Gázquez (Ghent University\, Belgium)&nbsp\;\nModeling of Ultrasound-Induced Intramembrane Cavitation in Realistic Neuronal Morphologies10:00 – 10:20&nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\;Thomas Knösche (Max Planck Institute for Human Cognitive and Brain Sciences\, Germany)&nbsp\;\nThe effective electric field of TMS - the gap between microscopic and macroscopic models10:20 – 10:40&nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; Break10:40 – 11:00&nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; Erik Müller (Max Planck Institute for Human Cognitive and Brain Sciences\, Germany)\nCoupling TMS induced electric field into motor cortex circuits - dosing\, direction dependency\, and I-wave generation11:00 – 11:20&nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; Bettina Schwab (University of Twente\, the Netherlands)&nbsp\;\nComputational evidence for direct entrainment of cortical neurons by weak E-fields of deep brain stimulation\n11:20 – 11:40&nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; Eleonora Bernasconi&nbsp\;(Institute of Computer Science\, The Czech Academy of Sciences\, Czech Republic)&nbsp\;\nTMS targeting the cerebellum: a multi-scale modelling approach\n11:40 – 12:00 &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\;Alberto Mazzoni (Scuola Superiore Sant'Anna\, Italy)\nNetwork models for adaptive deep brain stimulation design&nbsp\;12:00 – 12:20 &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\;Esra Neufeld (IT’IS foundation\, Switzerland)\nNew Perspectives on Neural Mass Modeling and Sleep \n\n
CATEGORIES:WORKSHOP
LOCATION:Room 502\, Halifax\, NS\, Canada
SEQUENCE:0
UID:32e9b8886e6ac1964dab52989d0e05ce
URL:http://cns2026.sched.com/event/32e9b8886e6ac1964dab52989d0e05ce
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260715T120000Z
DTEND:20260715T153000Z
SUMMARY:Detailed models of brain microcircuit activity and signals in clinical applications
DESCRIPTION:Brain function is mediated by neuronal microcircuits with specific connectivity between neuron types\, and it is increasingly evident that altered microcircuitry underlies deficits in brain disorders such as depression and schizophrenia\, and in aging and Alzheimer’s. However\, our ability to monitor the microcircuitry in living humans is limited\, meriting the use of detailed computational simulations to overcome experimental limitations in linking the microcircuit mechanisms with altered cortical activity\, functional deficits and biomarkers in clinically-relevant brain signals.\nThis workshop will showcase the latest efforts from leading groups in large-scale neuronal microcircuit modeling of spiking activity and signals such as EEG and MEG. The workshop will present diverse data-driven microcircuit modeling approaches that integrate neuronal\, synaptic and functional data\, and the different simulation tools used to generate activity and brain signals from the models. The workshop will highlight clinical applications of the models in establishing target mechanisms for treatment\, and estimating microcircuit mechanisms from clinical signals in patient data with machine learning to improve diagnosis. This workshop aims to foster the exchange of methods and ideas\, and offer new insights on clinical applications of simulated large-scale cortical microcircuits.\n\nSpeakers:\nEtay Hay\nAssistant Professor\, Centre for Addiction and Mental Health\, University of Toronto\n\nHeng Kan Yao\nPostdoc\, Centre for Addiction and Mental Health\, University of Toronto\n\nSalvador Dura-Bernal\nAssociate Professor\, State University of New York (SUNY) Downstate\n\nKatharina Duecker\nPostdoc\, Brown University\n\nAlexandre Guet-McCreight\nPostdoc\, Centre for Addiction and Mental Health\, University of Toronto
CATEGORIES:WORKSHOP
LOCATION:Room 501\, Halifax\, NS\, Canada
SEQUENCE:0
UID:de174792100f4e43ce9f170dd8d58e09
URL:http://cns2026.sched.com/event/de174792100f4e43ce9f170dd8d58e09
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260715T120000Z
DTEND:20260715T153000Z
SUMMARY:Evolution\, Computation and the Origins of Nervous Systems: from Animal Models to Neuromorphic Engineering
DESCRIPTION:\n
CATEGORIES:WORKSHOP
LOCATION:Room 505\, Halifax\, NS\, Canada
SEQUENCE:0
UID:d38c1517e7097cd33000b78632223d9d
URL:http://cns2026.sched.com/event/d38c1517e7097cd33000b78632223d9d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260715T120000Z
DTEND:20260715T210000Z
SUMMARY:The 4D Connectome: Development\, Structure\, Function and Dynamics
DESCRIPTION:Please consult the Workshop Website for the detailed program.\nhttps://sites.google.com/view/cns2026workshop/home\n\nMorning Session: 9 AM -- 1230 PM\n0900 -- Jeremie Lefebvre\n0945 -- Lida Kanari\nCoffee Break\n1100 -- Tatyana Sharpee\n1145 -- Hermann Cuntz\n\nAfternoon Session: 2 PM -- 6 PM\n1400 -- Sven Dorkenvald\n1445 -- Alex Bird\n1530 -- Paolo Bonifazi\n1615 -- Gabriel Benigno\n1645 -- Katharina Duecker\n1715 -- Alberto Mazzoni
CATEGORIES:WORKSHOP
LOCATION:Room 503\, Halifax\, NS\, Canada
SEQUENCE:0
UID:0d50511e92497388a85de9fda853ddb9
URL:http://cns2026.sched.com/event/0d50511e92497388a85de9fda853ddb9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260715T120000Z
DTEND:20260715T203000Z
SUMMARY:Workshop on Methods of Information Theory in Computational Neuroscience
DESCRIPTION:\n
CATEGORIES:WORKSHOP
LOCATION:Room 506/7\, Halifax\, NS\, Canada
SEQUENCE:0
UID:12ff52f8957f18ff656ee62f2f2ba283
URL:http://cns2026.sched.com/event/12ff52f8957f18ff656ee62f2f2ba283
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260715T133000Z
DTEND:20260715T140000Z
SUMMARY:Coffee Break
DESCRIPTION:\n
CATEGORIES:BREAK
LOCATION:Ballroom Salon\, Halifax\, NS\, Canada
SEQUENCE:0
UID:905e2a0e4d0b8e9e99d132bb7acad0b8
URL:http://cns2026.sched.com/event/905e2a0e4d0b8e9e99d132bb7acad0b8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260708T114850Z
DTSTART:20260715T183000Z
DTEND:20260715T190000Z
SUMMARY:Coffee break
DESCRIPTION:\n
CATEGORIES:BREAK
LOCATION:Ballroom Salon\, Halifax\, NS\, Canada
SEQUENCE:0
UID:202fc2a3ba4cfb720d41af344e82b11e
URL:http://cns2026.sched.com/event/202fc2a3ba4cfb720d41af344e82b11e
END:VEVENT
END:VCALENDAR