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/
