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12	12	(((
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	15	+== From single-cell modeling to large-scale network dynamics with NEST Simulator ==
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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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	22	+The tutorial is composed of three parts in which the user learns to model neuronal networks step by step.
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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/]]
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	29	+
	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.
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	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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	39	+
	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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