Wiki source code of EBRAINS Swedish Node Workshop 2025: NEST Tutorials

Version 16.7 by spreizer on 2025/07/30 11:04

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11.1	5	= NEST Tutorials =
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11.1	7	EBRAINS Swedish Node, Stockholm, 25/08/25 - 27/08/25
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14.1	15	== From single-cell modeling to large-scale network dynamics with NEST Simulator ==
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14.2	17	Instructor: Sebastian Spreizer, PhD, University of Trier and Research Center Jülich
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14.2	19	- [[Abstract>>url:https://wiki.ebrains.eu/bin/view/Collabs/swedish-node-nest-tutorials/About/]]
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16.3	22	The tutorial is composed of three parts in which the user learns to simulate with NEST step by step.
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16.3	25	==== Time schedule ====
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14.2	27	\|(% style="width:84px" %)9 - 11\|(% style="width:235px" %)NEST Desktop\|(% style="width:541px" %)[[https:~~/~~/nest-desktop.readthedocs.org/>>https://nest-desktop.readthedocs.org/]]
16.5	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/]]
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16.3	32	==== Descriptions ====
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14.2	34	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.
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16.6	36	* [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/nest-desktop>>https://wiki.ebrains.eu/bin/view/Collabs/nest-desktop]]
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16.4	39	2) 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:
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16.4	41	* neuronal dynamics
	42	* large scale networks,
	43	* networks of spatial neurons
	44	* using plasticity
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14.2	48	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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16.3	50	* [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/nestml-tutorials>>https://wiki.ebrains.eu/bin/view/Collabs/nestml-tutorials]]
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	58	{{box title="Contents"}}
	59	{{toc/}}
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