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1 -Swedish Node: NEST Tutorials
1 +EBRAINS Swedish Node Workshop 2025: NEST Tutorials
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2 2  (((
3 3  (% class="container" %)
4 4  (((
5 -= From single-cell modeling to large-scale network dynamics with NEST Simulator =
5 += NEST Tutorials =
6 6  
7 -NEST Tutorial for EBRAINS Swedish Node
7 +EBRAINS Swedish Node, Stockholm, 25/08/25 - 27/08/25
8 8  )))
9 9  )))
10 10  
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12 12  (((
13 13  (% class="col-xs-12 col-sm-8" %)
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16 -**Instructor**: Sebastian Spreizer, PhD  University of Trier and Research Center Jülich
15 +== From single-cell modeling to large-scale network dynamics with NEST Simulator ==
17 17  
17 +**Instructor**: Sebastian Spreizer, PhD, University of Trier and Research Center Jülich
18 18  
19 -(% class="wikigeneratedid" id="HWhatcanIfindhere3F" %)
20 -NEST is an established, open-source simulator for spiking neuronal networks, which can capture a high degree of detail of biological network structures while retaining high performance and scalability from laptops to HPC [1]. This tutorial offers hands-on experience in building and simulating neuron, synapse, and network models. It introduces several tools and front-ends to implement modeling ideas most effectively. Participants do not have to install software as all tools are accessible via the cloud.
19 +- [[Abstract>>url:https://wiki.ebrains.eu/bin/view/Collabs/swedish-node-nest-tutorials/About/]]
21 21  
22 -First, we look at NEST Desktop [2], a web-based graphical user interface (GUI), which allows the exploration of essential concepts in computational neuroscience without the need to learn a programming language. This advances both the quality and speed of teaching in computational neuroscience. To get acquainted with the GUI, we will create and analyze a balanced two-population network.
23 23  
24 -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 explore sophisticated use cases with NEST simulations. I will let the audience pick one or few of the provided examples, e.g. large scale networks, networks of spatial neurons or using plasticity [3].
22 +The tutorial is composed of three parts in which the user learns to simulate with NEST step by step.
25 25  
26 -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].
27 27  
28 -[1] [[https:~~/~~/nest-simulator.readthedocs.org/>>https://nest-simulator.readthedocs.org/]]
29 -[2] [[https:~~/~~/nest-desktop.readthedocs.org/>>https://nest-desktop.readthedocs.org/]]
30 -[3] [[https:~~/~~/nest-simulator.readthedocs.io/en/latest/examples/index.html>>https://nest-simulator.readthedocs.io/en/latest/examples/index.html]]
31 -[4] [[https:~~/~~/nestml.readthedocs.org/>>https://nestml.readthedocs.org/]]
25 +=== Time schedule ===
32 32  
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/]]
33 33  
34 -**Requirements**: Laptop with access to Internet. An account on EBRAINS would be optimal, otherwise I will create guest accounts for participants.
35 35  
36 -**Target audience**: Students and researchers who are interesting in computational neuroscience
32 +=== 1) NEST Desktop ===
33 +
34 +The first part of the tutorial, we look at NEST Desktop.  As a goal we will create and analyze a balanced two-population network.
35 +
36 +* [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/nest-desktop>>https://wiki.ebrains.eu/bin/view/Collabs/nest-desktop]]
37 +
38 +=== 2) NEST Simulator ===
39 +
40 +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:
41 +
42 +* neuronal dynamics
43 +* large scale networks,
44 +* networks of spatial neurons
45 +* using plasticity
46 +
47 +=== 3) NESTML ===
48 +
49 +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 +
51 +* [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/nestml-tutorials>>https://wiki.ebrains.eu/bin/view/Collabs/nestml-tutorials]]
52 +
53 +
37 37  )))
38 38  
39 39