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...	...	@@ -28,14 +28,15 @@
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	31
32		-=== 1) NEST Desktop ===
	33	+==== 1) NEST Desktop ====
33	33
34	34	The first part of the tutorial, we look at NEST Desktop.  As a goal we will create and analyze a balanced two-population network.
35	35
36	36	* [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/nest-desktop>>https://wiki.ebrains.eu/bin/view/Collabs/nest-desktop]]
37	37
38		-=== 2) NEST Simulator ===
	39	+==== 2) NEST Simulator ====
39	39
40	40	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:
41	41
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44	44	* networks of spatial neurons
45	45	* using plasticity
46	46
47		-=== 3) NESTML ===
48	48
	49	+==== 3) NESTML ====
	50	+
49	49	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].
50	50
51	51	* [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/nestml-tutorials>>https://wiki.ebrains.eu/bin/view/Collabs/nestml-tutorials]]