Last modified by spreizer on 2025/08/26 09:19
From version 10.1
edited by spreizer
on 2025/07/29 10:24
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To version 9.1
edited by spreizer
on 2025/07/29 10:20
Change comment: There is no comment for this version

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5 -= NEST Tutorials for EBRAINS Swedish Node =
5 += From single-cell modeling to large-scale network dynamics with NEST Simulator =
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7 -Stockholm, 25/08/25 - 27/08/25
7 +NEST Tutorials for EBRAINS Swedish Node
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16 +**Instructor**: Sebastian Spreizer, PhD  University of Trier and Research Center Jülich
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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.
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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.
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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].
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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].
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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/]]
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34 +**Requirements**: Laptop with access to Internet. An account on EBRAINS would be optimal, otherwise I will create guest accounts for participants.
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36 +**Target audience**: Students and researchers who are interesting in computational neuroscience
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