An on-site tutorial at the 31st Annual Computational Neuroscience Meeting, July 16-20, 2022
An online tutorial as a CNS satellite event, July 1st, 2022
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 . This tutorial provides hands-on experience in building and simulating neuron, synapse, and network models. It introduces several tools and front-ends to implement modeling ideas most efficiently. Participants do not have to install software as all tools can be accessed via the cloud.
First, we look at NEST Desktop , 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. The model is then exported to a Jupyter notebook and endowed with a data-driven spatial connectivity profile of the cortex, enabling us to study the propagation of activity. Then, we make the synapses in the network plastic and let the network learn a reinforcement learning task, whereby the learning rule goes beyond pre-synaptic and post-synaptic spikes by adding a dopamine signal as a modulatory third factor. NESTML  makes it easy to express this and other advanced synaptic plasticity rules and neuron models, and automatically translates them into fast simulation code. More morphologically detailed models, with a large number of compartments and custom ion channels and receptor currents, can also be defined using NESTML. We first implement a simple dendritic layout and use it to perform a sequence discrimination task. Next, we implement a compartmental layout representing semi-independent subunits and recurrently connect several such neurons to elicit an NMDA-spike driven network state.
Schedule (on-site tutorial)
The tutorial will start on Saturday, July 16, 09:30.
Overview and introduction to NEST Simulator
Interactive network design with NEST Desktop
Jens Bruchertseifer, Sebastian Spreizer
Data-driven spatial plastic networks
Jasper Albers, Agnes Korcsak-Gorzo
Modeling dopamine-modulated STDP synapses with NESTML
Pooja Babu, Charl Linssen
Morphologically detailed models with NEST
Joshua Böttcher, Willem Wybo
NEST Simulator is a spiking neuron simulator which specialises in point neurons and neurons with few comparments. It can simulate synaptic plasticity, structural plasticity, gap junctions and countless other features on machines ranging from home PCs to high-performance computing systems.
NEST Desktop is a web-based GUI application for NEST Simulator. It enables the rapid construction, parametrization, and instrumentation of neuronal network models.
NESTML is a domain-specific modeling language and code-generation toolchain. It supports the specification of neuron models in an intuitive and concise syntax. Optimised code generation for the target simulation platform couples a highly accessible language with good simulation performance.
Please don’t forget to register for the on-site conference at https://www.cnsorg.org/cns-2022.
Free online satellite tutorials are given as part of CNS*2022 between June 27th and July 1st. Registration is free but required at https://ocns.github.io/SoftwareWG/pages/software-wg-satellite-tutorials-at-cns-2022.html
Tutorials are not recorded and are not livestreamed events on YouTube.
We will provide login details for virtual machines on Human Brain Project (EBRAINS) infrastructure to registered participants. You will be able to access the required software directly from your browser, without requiring any installation. Access is provided to a NEST Desktop instance, as well as a JupyterLab environment that includes NEST Simulator and NESTML.
You can also run the software on a local computer. We suggest using two Docker images that we provide:
Launches a Jupyter Notebook server on localhost at port 7003. The password is: nest25years
The image is available via DockerHub. To install:
docker pull clifzju/nest-nestml-jupyterlab-ocns-tutorial
Then run the image while forwarding the port:
docker run -i -d -p 7003:7003 -t clifzju/nest-nestml-jupyterlab-ocns-tutorial
You can then access the server in your browser by navigating to the URL http://localhost:7003.
The Docker container can be started in interactive mode (giving you a shell prompt) by omitting the
For local installation, we recommend to use the official NEST Desktop Docker image and instructions. Full instructions can be found at: https://nest-desktop.readthedocs.io/en/latest/user/setup.html#via-docker-compose-linux-windows-apple.
If you participated in (any part) of this tutorial, we value your feedback! Please take a moment to fill in our short feedback form at https://forms.gle/yv9MwmAKJugTs2mR9.
This tutorial is organised by Charl Linssen (Jülich Research Centre, Germany), Agnes Korcsak-Gorzo (Jülich Research Centre, Germany), Jasper Albers (Jülich Research Centre, Germany), Pooja Babu (Jülich Research Centre, Germany), Joshua Böttcher (Jülich Research Centre, Germany), Jessica Mitchell (Jülich Research Centre, Germany), Willem Wybo (Jülich Research Centre, Germany), Jens Bruchertseifer (University of Trier, Germany), Sebastian Spreizer (University of Trier, Germany) and Dennis Terhorst (Jülich Research Centre, Germany).
For general inquiries, please contact Charl at email@example.com.
We acknowledge the use of Fenix Infrastructure resources, which are partially funded from the European Union’s Horizon 2020 research and innovation programme through the ICEI project under the grant agreement No. 800858.