Version 18.2 by spreizer on 2025/07/30 11:06
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5 = NEST Tutorials =
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7 EBRAINS Swedish Node, Stockholm, 25/08/25 - 27/08/25
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15 == From single-cell modeling to large-scale network dynamics with NEST Simulator ==
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17 **Instructor**: Sebastian Spreizer, PhD, University of Trier and Research Center Jülich
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19 - [[Abstract>>url:https://wiki.ebrains.eu/bin/view/Collabs/swedish-node-nest-tutorials/About/]]
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22 The tutorial is composed of three parts in which the user learns to simulate with NEST step by step.
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24
25 === Time schedule ===
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27 |(% style="width:84px" %)9 - 11|(% style="width:235px" %)NEST Desktop|(% style="width:541px" %)[[https:~~/~~/nest-desktop.readthedocs.org/>>https://nest-desktop.readthedocs.org/]]
28 |(% style="width:84px" %)12 - 15|(% style="width:235px" %)NEST Simulator|(% style="width:541px" %)[[https:~~/~~/nest-simulator.readthedocs.org/>>https://nest-simulator.readthedocs.org/]]
29 |(% style="width:84px" %)15 - 18|(% style="width:235px" %)NESTML|(% style="width:541px" %)[[https:~~/~~/nestml.readthedocs.org/>>https://nestml.readthedocs.org/]]
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31 === ===
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33 === 1) NEST Desktop ===
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35 The first part of the tutorial, we look at NEST Desktop.  As a goal we will create and analyze a balanced two-population network.
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37 * [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/nest-desktop>>https://wiki.ebrains.eu/bin/view/Collabs/nest-desktop]]
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39
40 === 2) NEST Simulator ===
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42 The tutorial will then turn to Jupyter (Python) notebooks where we will start by creating a spiking neurons. 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:
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44 * neuronal dynamics
45 * large scale networks,
46 * networks of spatial neurons
47 * using plasticity
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49
50 === 3) NESTML ===
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52 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].
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54 * [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/nestml-tutorials>>https://wiki.ebrains.eu/bin/view/Collabs/nestml-tutorials]]
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62 {{box title="**Contents**"}}
63 {{toc/}}
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