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17	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/]]
	19	+- [[Tutorial abstract>>url:https://wiki.ebrains.eu/bin/view/Collabs/swedish-node-nest-tutorials/About/]]
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21	21
22	22	The tutorial is composed of three parts in which the user learns to simulate with NEST step by step.
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28	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	29	|(% style="width:84px" %)15 - 18|(% style="width:235px" %)NESTML|(% style="width:541px" %)[[https:~~/~~/nestml.readthedocs.org/>>https://nestml.readthedocs.org/]]
30	30
31		-=== Descriptions ===
	31	+=== 1) NEST Desktop ===
32	32
33		-==== 1) NEST Desktop ====
34		-
35	35	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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37	37	* [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/nest-desktop>>https://wiki.ebrains.eu/bin/view/Collabs/nest-desktop]]
38	38
39		-==== 2) NEST Simulator ====
	37	+=== 2) NEST Simulator ===
40	40
41		-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:
	39	+The tutorial will then turn to Jupyter (Python) notebooks where we will start by creating 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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43	43	* neuronal dynamics
44	44	* large scale networks,
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45	45	* networks of spatial neurons
46	46	* using plasticity
47	47
	46	+=== 3) NESTML ===
48	48
49		-==== 3) NESTML ====
50		-
51	51	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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53	53	* [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/nestml-tutorials>>https://wiki.ebrains.eu/bin/view/Collabs/nestml-tutorials]]