An online tutorial at the 30th Annual Computational Neuroscience Meeting, July 3rd, 2021
NEST is established community software for the simulation of spiking neuronal network models capturing the full detail of biological network structures . The simulator runs efficiently on a range of architectures from laptops to supercomputers . Many peer-reviewed neuroscientific studies have used NEST as a simulation tool over the past 20 years. More recently, it has become a reference code for research on neuromorphic hardware systems .
This tutorial provides hands-on experience with recent improvements of NEST. In the past, starting out with NEST could be challenging for computational neuroscientists, as models and simulations had to be programmed using SLI, C++ or Python. NEST Desktop changes this: It is an entirely graphical approach to the construction and simulation of neuronal network models. It runs installation-free in the browser and has proven its value in several university courses. This opens the domain of NEST to the teaching of neuroscience for students with little programming experience.NESTML complements this new interface by enhancing the development process of neuron and synapse models. Advanced researchers often want to study specific features not provided by models already available in NEST. Instead of having to turn to C++, using NESTML they can write down differential equations and necessary state transitions in the mathematical notation they are used to. These descriptions are then automatically processed to generate machine-optimised code.
After a quick overview of the current status of NEST and upcoming new functionality, the tutorial works through a the construction of a balanced network to show how the combination of NEST Desktop and NESTML can be used in the modern workflow of a computational neuroscientist.
 Jordan J., Ippen T., Helias M., Kitayama I., Sato M., Igarashi J., Diesmann M., Kunkel S. (2018) Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers. Frontiers in Neuroinformatics 12: 2
 Gutzen R., von Papen, M., Trensch G., Quaglio P. Grün S., Denker M. (2018) Reproducible Neural Network Simulations: Statistical Methods for Model Validation on the Level of Network Activity Data. Frontiers in Neuroinformatics 12 (90)
|Welcome and introduction to NEST Simulator|
|Introduction and hands-on with NEST Desktop|
|Introduction to synaptic plasticity|
|Synaptic plasticity models in NESTML|
|Neural network models in NEST Simulator|
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 conference at https://www.cnsorg.org/cns-2021.
To allow for interactive sessions, tutorials will run as “virtual rooms” (i.e. video calls) in CNS*2021. The platform is Zoom. It can run in your browser, and no account or installation is required. In some cases, installing the software on your local computer can improve the quality of the video and audio.
Tutorials are not recorded and are not livestreamed events on YouTube.
The link for the tutorial video stream will been announced on the Sched instance for CNS*2021
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 JupyterHub 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/deployer/deploy-docker.html.
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 (JARA-Institute, Jülich, Germany), Barna Zajzon (Jülich Research Centre, Jülich, and RWTH Aachen University, Aachen, Germany), Sebastian Spreizer (University of Trier, Germany), Jasper Albers (Jülich Research Centre, Jülich, and RWTH Aachen University, Aachen, Germany), and Dennis Terhorst (Jülich Research Centre, Jülich, and RWTH Aachen University, Aachen, Germany).
For general inquiries, please contact Charl at firstname.lastname@example.org.
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.