CNS*2026 Halifax

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.

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.

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.

For more details and materials related to this tutorial, please see the tutorial website: https://clinssen.github.io/NEST-workshop/

NEST 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.

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.

As 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.

[1] https://nest-simulator.readthedocs.org/
[2] https://github.com/INM-6/microcircuit-PD14-model
[3] https://nest-simulator.readthedocs.io/en/latest/auto_examples/eprop_plasticity/index.html
[4] https://nestml.readthedocs.org/

Detecting and sorting available extracellular neuronal action potentials remains challenging in neuroscience. This bottleneck stems from uncertainty about the origin of a substantial fraction of the signals present in electrophysiological recordings, leading to systematic removal of a fraction of the recordings. Aiming for generalizable solutions, this workshop will approach the bottleneck by separating the extracellular contributions from dendrites, soma, and axons. Biophysics predicts that each of these compartments generates distinct extracellular waveforms that collectively, yet in varying proportions, contribute to extracellularly recorded spikes. The central aim is to disentangle the contribution from these three compartments to enable more inclusive, and more precise interpretations of in vivo electrophysiological data.

There is a strong push to create more reliable, more automated approaches that bypass the need for manual curation. Machine learning methods show promise to replace manual curation, yet they will require reliable ground-truth. We hypothesize here that the origin of exotic waveforms can be reframed as putative combination(s) from different neuronal compartments with the known sequential activation. A systematic reorganization of existing datasets, conditioned to libraries of waveform based on the three ubiquitous neural compartments, might already be sufficient to lay the foundation for tomorrow’s automated tools.

The 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.

Speakers:

09:00 – 09:15 Jérémie Sibille (Charité University Hospital, Berlin, Germany)
Introduction

09:15 – 09:40 Rishikesh Narayanan (Indian Institute of Science, Bangalore, India)
Local field potentials: Active dendritic and gap junctional contributions

09:40 – 10:05 Alexandra Tzilivaki (Charité University Hospital, Berlin, Germany)
Mapping Waveforms from the Inside Out: Dendritic and Topological Control by PV+ Interneurons

10:05 – 10:30 Paula Kuokkanen (Humboldt-Universität zu Berlin, Germany)
Axonal field contributions – a rule or an exception?

10:30 – 11:00 Pause

11:00 – 11:25 Sharon Crook (Arizona State University, AZ, US)
Discovering features for neuron-type identification from extracellular recordings

11:25 – 11:45 Costas Anastassiou (Cedars-Sinai and Caltech, Los Angeles, CA, US)
In vivo identification of human cortical cell types in human electrophysiological recordings 

11:45 – 12:15 Nick Steinmetz (University of Washington in Seattle, WA, US)
Insights and puzzles about high density extracellular waveforms from Neuropixels Ultra

12:15 – 12:30 Discussion

The final schedule is yet to be confirmed