Changes for page EBRAINS Swedish Node Workshop 2025: NEST Tutorials
Last modified by spreizer on 2025/08/26 09:19
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... ... @@ -2,9 +2,9 @@ 2 2 ((( 3 3 (% class="container" %) 4 4 ((( 5 -= Fromsingle-cell modelingtolarge-scalenetworkdynamicswith NEST Simulator=5 += NEST Tutorials for EBRAINS Swedish Node = 6 6 7 - NEST TutorialsforEBRAINSSwedish Node7 +Stockholm, 25/08/25 - 27/08/25 8 8 ))) 9 9 ))) 10 10 ... ... @@ -13,27 +13,7 @@ 13 13 (% class="col-xs-12 col-sm-8" %) 14 14 ((( 15 15 (% class="wikigeneratedid" %) 16 -**Instructor**: Sebastian Spreizer, PhD University of Trier and Research Center Jülich 17 - 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. 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 - 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]. 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 - 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/]] 32 - 33 - 34 -**Requirements**: Laptop with access to Internet. An account on EBRAINS would be optimal, otherwise I will create guest accounts for participants. 35 - 36 -**Target audience**: Students and researchers who are interesting in computational neuroscience 16 + 37 37 ))) 38 38 39 39