CNS*2026 Halifax

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.

The tutorial will feature two main components:

1. Graph Analysis of Neuronal Populations

Participants 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].

2. Foundation Models for Neural Data and Brain Networks

This 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.

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.

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.

References

[1] Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186-198.

[2] Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. NeuroImage, 52(3), 1059-1069.

[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.

[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.

[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.

[6] Cortical Labs: https://corticallabs.com/

[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.

[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.

[9] Nag, S., & Uludag, K. (2024). Transformer-aided dynamic causal model for scalable estimation of effective connectivity. Imaging Neuroscience, 2, 1-22.

[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.

[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.

[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.

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.

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.

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.

Attendees are expected to bring a computer equipped with Python, a C compiler, and git.

Website: https://numericalbrain.org/en/neulite/

Reference:

[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

The schedule and abstracts are available at: https://brady.cs.cas.cz/events/ocns-2026-workshop

Ion 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.

The 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.

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.

Computational 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.  

CNS 2026 Workshop  - July 15th - Halifax
Computational modeling of neuromodulation technologies

• 9:00 – 9:20                Jan Antolik (Charles University, Czech Republic) 
  Modelling spatio-temporal optogenetic stimulation of primary visual cortex
  
• 9:20 – 9:40                 Laila Weyn (Ghent University, Belgium) 
  Modelling potassium based optogenetic approaches for seizure suppression
• 9:40 – 10:00               Joaquín Gázquez (Ghent University, Belgium) 
  Modeling of Ultrasound-Induced Intramembrane Cavitation in Realistic Neuronal Morphologies
• 10:00 – 10:20             Thomas Knösche (Max Planck Institute for Human Cognitive and Brain Sciences, Germany) 
  The effective electric field of TMS - the gap between microscopic and macroscopic models
• 10:20 – 10:40              Break
• 10:40 – 11:00              Erik Müller (Max Planck Institute for Human Cognitive and Brain Sciences, Germany)
  Coupling TMS induced electric field into motor cortex circuits - dosing, direction dependency, and I-wave generation
• 11:00 – 11:20              Bettina Schwab (University of Twente, the Netherlands) 
  Computational evidence for direct entrainment of cortical neurons by weak E-fields of deep brain stimulation
  
• 11:20 – 11:40              Eleonora Bernasconi (Institute of Computer Science, The Czech Academy of Sciences, Czech Republic) 
  TMS targeting the cerebellum: a multi-scale modelling approach
  
• 11:40 – 12:00              Alberto Mazzoni (Scuola Superiore Sant'Anna, Italy)
  Network models for adaptive deep brain stimulation design 
• 12:00 – 12:20              Esra Neufeld (IT’IS foundation, Switzerland)
  New Perspectives on Neural Mass Modeling and Sleep