Last modified by spreizer on 2025/08/26 09:19
From version 20.2
edited by spreizer
on 2025/07/30 11:09
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To version 18.1
edited by spreizer
on 2025/07/30 11:05
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16 16  
17 17  **Instructor**: Sebastian Spreizer, PhD, University of Trier and Research Center Jülich
18 18  
19 -- [[Tutorial abstract>>url:https://wiki.ebrains.eu/bin/view/Collabs/swedish-node-nest-tutorials/About/]]
19 +- [[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 -=== 1) NEST Desktop ===
31 +=== Descriptions ===
32 32  
33 +==== 1) NEST Desktop ====
34 +
33 33  The first part of the tutorial, we look at NEST Desktop.  As a goal we will create and analyze a balanced two-population network.
34 34  
35 35  * [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/nest-desktop>>https://wiki.ebrains.eu/bin/view/Collabs/nest-desktop]]
36 36  
37 -=== 2) NEST Simulator ===
39 +==== 2) NEST Simulator ====
38 38  
39 39  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:
40 40  
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43 43  * networks of spatial neurons
44 44  * using plasticity
45 45  
46 -=== 3) NESTML ===
47 47  
49 +==== 3) NESTML ====
50 +
48 48  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].
49 49  
50 50  * [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/nestml-tutorials>>https://wiki.ebrains.eu/bin/view/Collabs/nestml-tutorials]]