Last modified by spreizer on 2025/08/26 09:19
From version 5.1
edited by spreizer
on 2025/07/29 10:15
Change comment: There is no comment for this version
To version 7.1
edited by spreizer
on 2025/07/29 10:18
Change comment: There is no comment for this version

Summary

Details

Page properties
Content
... ... @@ -2,9 +2,9 @@
2 2  (((
3 3  (% class="container" %)
4 4  (((
5 -= My Collab's Extended Title =
5 += From single-cell modeling to large-scale network dynamics with NEST Simulator =
6 6  
7 -My collab's subtitle
7 +NEST Tutorial for EBRAINS Swedish Node
8 8  )))
9 9  )))
10 10  
... ... @@ -12,15 +12,28 @@
12 12  (((
13 13  (% class="col-xs-12 col-sm-8" %)
14 14  (((
15 -= What can I find here? =
15 +(% class="wikigeneratedid" %)
16 +**Instructor**: Sebastian Spreizer, PhD  University of Trier and Research Center Jülich
16 16  
17 -* Notice how the table of contents on the right
18 -* is automatically updated
19 -* to hold this page's headers
20 20  
21 -= Who has access? =
19 +(% class="wikigeneratedid" id="HWhatcanIfindhere3F" %)
20 +NEST is an established, open-source simulator for spiking neuronal networks, which can capture a high degree of detail of biological network structures while retaining high performance and scalability from laptops to HPC [1]. This tutorial offers hands-on experience in building and simulating neuron, synapse, and network models. It introduces several tools and front-ends to implement modeling ideas most effectively. Participants do not have to install software as all tools are accessible via the cloud.
22 22  
23 -Describe the audience of this collab.
22 +First, we look at NEST Desktop [2], a web-based graphical user interface (GUI), which allows the exploration of essential concepts in computational neuroscience without the need to learn a programming language. This advances both the quality and speed of teaching in computational neuroscience. To get acquainted with the GUI, we will create and analyze a balanced two-population network.
23 +
24 +The tutorial will then turn to Jupyter (Python) notebooks where we will start by creating a spiking network. Here, we learn advanced steps to write code with NEST Simulation syntax. The scripting codes allow us to explore sophisticated use cases with NEST simulations. I will let the audience pick one or few of the provided examples, e.g. large scale networks, networks of spatial neurons or using plasticity [3].
25 +
26 +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].
27 +
28 +[1] [[https:~~/~~/nest-simulator.readthedocs.org/>>https://nest-simulator.readthedocs.org/]]
29 +[2] [[https:~~/~~/nest-desktop.readthedocs.org/>>https://nest-desktop.readthedocs.org/]]
30 +[3] [[https:~~/~~/nest-simulator.readthedocs.io/en/latest/examples/index.html>>https://nest-simulator.readthedocs.io/en/latest/examples/index.html]]
31 +[4] [[https:~~/~~/nestml.readthedocs.org/>>https://nestml.readthedocs.org/]]
32 +
33 +
34 +**Requirements**: Laptop with access to Internet. An account on EBRAINS would be optimal, otherwise I will create guest accounts for participants.
35 +
36 +**Target audience**: Students and researchers who are interesting in computational neuroscience
24 24  )))
25 25  
26 26  
XWiki.XWikiRights[4]
Allow/Deny
... ... @@ -1,0 +1,1 @@
1 +Allow
Groups
... ... @@ -1,0 +1,1 @@
1 +XWiki.XWikiAllGroup
Levels
... ... @@ -1,0 +1,1 @@
1 +view