NEST Tutorials
EBRAINS Swedish Node, Stockholm, 25/08/25 - 27/08/25
From single-cell modeling to large-scale network dynamics with NEST Simulator
Instructor: Sebastian Spreizer, PhD, University of Trier and Research Center Jülich
- Abstract
The tutorial is composed of three parts in which the user learns to model neuronal networks step by step.
| 9 - 11 | NEST Desktop | https://nest-desktop.readthedocs.org/ |
| 12 - 15 | NEST in Jupyter Lab | https://nest-simulator.readthedocs.org/ |
| 15 - 17 | NESTML | https://nestml.readthedocs.org/ |
1) The first part of the tutorial, we look at NEST Desktop. As a goal we will create and analyze a balanced two-population network.
2) The tutorial will then turn to Jupyter (Python) notebooks where we will start by creating a spiking network. Here, we learn advanced steps to write code with NEST Simulation syntax. The scripting codes allow us to customize sophisticated use cases with NEST simulations. Examples are:
- large scale networks,
- networks of spatial neurons
- using plasticity
3) The last part is using NESTML to create custom neuron and synapse models for NEST Simulator. A functional plasticity rule will then be introduced into the balanced E/I network to implement a biologically realistic version of reinforcement learning. This will be done by formulating the learning model in the NESTML language syntax, and using the associated toolchain to generate code for NEST [4].