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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 Tutorials 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 +- [[Tutorial 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.
31 +=== 1) NEST Desktop ===
35 35  
36 -**Target audience**: Students and researchers who are interesting in computational neuroscience
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 +
35 +* [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/nest-desktop>>https://wiki.ebrains.eu/bin/view/Collabs/nest-desktop]]
36 +
37 +=== 2) NEST Simulator ===
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:
40 +
41 +* neuronal dynamics
42 +* large scale networks,
43 +* networks of spatial neurons
44 +* using plasticity
45 +
46 +=== 3) NESTML ===
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].
49 +
50 +* [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/nestml-tutorials>>https://wiki.ebrains.eu/bin/view/Collabs/nestml-tutorials]]
51 +
52 +
37 37  )))
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39 39