Summary

• Page properties (1 modified, 0 added, 0 removed)

Details

Page properties

Content

...	...	@@ -28,17 +28,16 @@
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		-=== ===
	31	+=== Descriptions ===
32	32
33		-=== 1) NEST Desktop ===
	33	+==== 1) NEST Desktop ====
34	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.
36	36
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 ====
39	39
40		-=== 2) NEST Simulator ===
41		-
42	42	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:
43	43
44	44	* neuronal dynamics
...	...	@@ -47,7 +47,7 @@
47	47	* using plasticity
48	48
49	49
50		-=== 3) NESTML ===
	49	+==== 3) NESTML ====
51	51
52	52	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].
53	53