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Welcome to the NEST simulator documentation!¶
If you use NEST for your project, don’t forget to cite NEST!
NEST is a simulator for spiking neural network models, ideal for networks of any size, for example:
Models of information processing e.g., in the visual or auditory cortex of mammals,
Models of network activity dynamics, e.g., laminar cortical networks or balanced random networks,
Models of learning and plasticity.
- New to NEST?
Start here at our Getting Started page
- Know which model you need?
NEST comes packaged with a large collection of neuron and synaptic plasticity models. You can find a list of all available models in our model directory, or select a model category by clicking one of the images:
Create complex networks using the Microcircuit Model:
- Need a different model?
To customize or combine features of neuron and synapse models, we recommend using the NESTML modeling language.
- Have a question or issue with NEST?
See our Getting Help page.
Where to find what¶
Tutorials show you step by step instructions using NEST. If you haven’t used NEST before, the PyNEST tutorial is a good place to start.
Example Networks demonstrate the use of dozens of the neural network models implemented in NEST.
Reference Material provides a quick look up of definitions, functions and terms.
Interested in contributing?¶
If you have any comments or suggestions, please share them on our Mailing List.
Want to contribute code? Visit out our Contributing pages to get started!
Interested in creating or editing documentation? Check out our Documentation workflows.
For more info about our larger community and the history of NEST check out the NEST Initiative website
NEST is available under the GNU General Public License 2 or later. This means that you can
use NEST for your research,
modify and improve NEST according to your needs,
distribute NEST to others under the same license.
NEST development has been supported by many organisations, programs, and individuals since 1993. The following list of support received is therefore necessarily incomplete.
This project has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreement:
No. 945539 (Human Brain Project SGA3),
No. 785907 (Human Brain Project SGA2),
No. 720270 (Human Brain Project SGA1),
No. 754304 (DEEP-EST), and
No. 800858 (ICEI).
The NEST developers gratefully acknowledge the support and funding received from:
Jülich Aachen Research Alliance (JARA),
computing time granted by the JARA-HPC Vergabegremium and provided on the JARA-HPC Partition part of the supercomputers JUQUEEN and JURECA at Forschungszentrum Jülich (VSR computation time grant JINB33),
Priority Program (SPP 2041 “Computational Connectomics”) of the Deutsche Forschungsgemeinschaft [S.J. van Albada: AL 2041/1-1],
Next-Generation Supercomputer Project of MEXT, Japan,
Helmholtz Association through the Helmholtz Portfolio Theme “Supercomputing and Modeling for the Human Brain”,
European Union 7th Framework Program under grant agreement no. 269921 (BrainScaleS),
European Union 7th Framework Programme ([FP7/2007-2013]) under grant agreement no. 604102 (Human Brain Project, HBP),
European Union 6th and 7th Framework Program under grant agreement no. 15879 (FACETS),
Excellence Initiative of the German federal and state governments,
Helmholtz young investigator’s group VH-NG-1028 “Theory of multi-scale neuronal networks”,
compute time provided by UNINETT Sigma2 - the National Infrastructure for High Performance Computing and Data Storage in Norway and its predecessors,
eScience program of the Research Council of Norway under grant 178892/V30 (eNeuro).