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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/]]
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31		-=== Descriptions ===
	31	+=== ===
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33		-==== 1) NEST Desktop ====
	33	+=== 1) NEST Desktop ===
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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]]
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39		-==== 2) NEST Simulator ====
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	40	+=== 2) NEST Simulator ===
	41	+
41	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:
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43	43	* neuronal dynamics
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46	46	* using plasticity
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49		-==== 3) NESTML ====
	50	+=== 3) NESTML ===
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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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