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34	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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	36	+* [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/nest-desktop>>https://wiki.ebrains.eu/bin/view/Collabs/nest-desktop]]
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37	37	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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39	39	* neuronal dynamics
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42	42	* using plasticity
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45	45	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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47	47	* [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/nestml-tutorials>>https://wiki.ebrains.eu/bin/view/Collabs/nestml-tutorials]]
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49		-
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51	51	)))
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