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1		-NEST Tutorials
	1	+NEST Tutorials for EBRAINS Swedish Node

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5		-= NEST Tutorials =
	5	+= From single-cell modeling to large-scale network dynamics with NEST Simulator =
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7		-EBRAINS Swedish Node, Stockholm, 25/08/25 - 27/08/25
	7	+NEST Tutorials for EBRAINS Swedish Node
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15		-== From single-cell modeling to large-scale network dynamics with NEST Simulator ==
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	16	+**Instructor**: Sebastian Spreizer, PhD  University of Trier and Research Center Jülich
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17		-**Instructor**: Sebastian Spreizer, PhD, University of Trier and Research Center Jülich
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19		-- [[Abstract>>url:https://wiki.ebrains.eu/bin/view/Collabs/swedish-node-nest-tutorials/About/]]
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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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22		-The tutorial is composed of three parts in which the user learns to model neuronal networks step by step.
	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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25		-|(% style="width:84px" %)9 - 11|(% style="width:235px" %)NEST Desktop|(% style="width:541px" %)[[https:~~/~~/nest-desktop.readthedocs.org/>>https://nest-desktop.readthedocs.org/]]
26		-|(% style="width:84px" %)12 - 15|(% style="width:235px" %)NEST in Jupyter Lab|(% style="width:541px" %)[[https:~~/~~/nest-simulator.readthedocs.org/>>https://nest-simulator.readthedocs.org/]]
27		-|(% style="width:84px" %)15 - 17|(% style="width:235px" %)NESTML|(% style="width:541px" %)[[https:~~/~~/nestml.readthedocs.org/>>https://nestml.readthedocs.org/]]
	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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30		-1) 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	+**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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32		-
33		-2) 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 customize sophisticated use cases with NEST simulations. Examples are:
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35		-- large scale networks,
36		-- networks of spatial neurons
37		-- using plasticity
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40		-3) 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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	36	+**Target audience**: Students and researchers who are interesting in computational neuroscience
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