Last modified by spreizer on 2025/08/26 09:19
From version 18.2
edited by spreizer
on 2025/07/30 11:06
Change comment: There is no comment for this version
To version 20.2
edited by spreizer
on 2025/07/30 11:09
Change comment: There is no comment for this version

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16 16  
17 17  **Instructor**: Sebastian Spreizer, PhD, University of Trier and Research Center Jülich
18 18  
19 -- [[Abstract>>url:https://wiki.ebrains.eu/bin/view/Collabs/swedish-node-nest-tutorials/About/]]
19 +- [[Tutorial abstract>>url:https://wiki.ebrains.eu/bin/view/Collabs/swedish-node-nest-tutorials/About/]]
20 20  
21 21  
22 22  The tutorial is composed of three parts in which the user learns to simulate with NEST step by step.
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28 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 29  |(% style="width:84px" %)15 - 18|(% style="width:235px" %)NESTML|(% style="width:541px" %)[[https:~~/~~/nestml.readthedocs.org/>>https://nestml.readthedocs.org/]]
30 30  
31 -=== ===
32 -
33 33  === 1) NEST Desktop ===
34 34  
35 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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36 36  
37 37  * [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/nest-desktop>>https://wiki.ebrains.eu/bin/view/Collabs/nest-desktop]]
38 38  
39 -
40 40  === 2) NEST Simulator ===
41 41  
42 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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46 46  * networks of spatial neurons
47 47  * using plasticity
48 48  
49 -
50 50  === 3) NESTML ===
51 51  
52 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].