Last modified by spreizer on 2025/08/26 09:19
From version 21.1
edited by spreizer
on 2025/07/30 11:10
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To version 30.1
edited by spreizer
on 2025/07/30 12:24
Change comment: There is no comment for this version

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12 12  (((
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14 14  (((
15 -== From single-cell modeling to large-scale network dynamics with NEST Simulator ==
15 +>(((
16 +== **From single-cell modeling to large-scale network dynamics
17 +with NEST Simulator** ==
18 +)))
16 16  
20 +
17 17  **Instructor**: Sebastian Spreizer, PhD, University of Trier and Research Center Jülich
18 18  
19 19  - [[Tutorial abstract>>url:https://wiki.ebrains.eu/bin/view/Collabs/swedish-node-nest-tutorials/About/]]
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32 32  
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  
39 +* [[https:~~/~~/nest-desktop.apps.ebrains.eu/>>https://nest-desktop.apps.ebrains.eu/]]
35 35  * [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/nest-desktop>>https://wiki.ebrains.eu/bin/view/Collabs/nest-desktop]]
36 36  
37 37  === 2) NEST Simulator ===
38 38  
39 -The tutorial will then turn to Jupyter (Python) notebooks where we will start by creating 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:
44 +The tutorial will then turn to Jupyter (Python) notebooks where we will start by creating 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. How to run examples on EBRAINS lab can be found here:
40 40  
41 -* neuronal dynamics
42 -* large scale networks,
43 -* networks of spatial neurons
44 -* using plasticity
46 +* [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/nest-simulator-examples/>>https://wiki.ebrains.eu/bin/view/Collabs/nest-simulator-examples/]]
45 45  
46 46  === 3) NESTML ===
47 47  
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].
50 +The last part is using NESTML to create custom neuron and synapse models for NEST Simulator. This will be done by formulating the learning model in the NESTML language syntax, and using the associated toolchain to generate code for NEST Simulator.
49 49  
50 50  * [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/nestml-tutorials>>https://wiki.ebrains.eu/bin/view/Collabs/nestml-tutorials]]
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52 52  
53 53  )))
54 54  
57 +[[image:https://www.ebrains.eu/flags/4x3/se.svg||alt="Sweden's flag" data-xwiki-image-style-alignment="center" height="225" width="300"]]
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57 57  (((