NEST simulator

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Centres

Forschungszentrum Juelich

Contributing organisations

NEST-initiative, Norwegian University of Life Sciences (NMBU)

Keywords

modeling simulation spiking neural networks

Research field

Information

Scientific community

computational neuroscience, neurorobotics, theoretical neuroscience, advanced compute architectures / neuromorphic computing

Funding

  • Horizon 2020
  • ICEI
  • ACA
  • ARA-CSD

Programming Languages

Python, C++

License

GPL-2.0-or-later

Costs

free and open

Cite

10.5281/zenodo.6368024

Contact

info@nest-initiative.org

Resources

NEST: A scalable spiking neural network simulator

NEST is used in computational neuroscience to model and study behavior of large networks of neurons. The models describe single neuron and synapse behavior and their connections. Different mechanisms of plasticity can be used to investigate artificial learning and help to shed light on the fundamental principles of how the brain works.

NEST offers convenient and efficient commands to define and connect large networks, ranging from algorithmically determined connections to data-driven connectivity. Create connections between neurons using numerous synapse models from STDP to gap junctions.

Features

  • Extensive model catalog: NEST offers numerous state-of-the art neuron and synapse models. Textbook standards like integrate-and-fire and Hodgkin-Huxley type models are available alongside high quality implementations of models published by the neuroscience community. We also offer many examples that showcase how to use them!
  • Fast-prototyping: NESTML provides a framework to create models without the use of C++, with a flexible processing toolchain, written in Python.
  • Scalable: NEST works on your laptop and also on the world’s largest supercomputers.
  • Efficient: NEST makes the best use of your multi-core computer or compute cluster. NEST can seamlessly scale to your needs.
  • Well-tested: The simulator is developed and continuously improved by the NEST community. NEST developers are using continuous-integration based workflows in order to maintain high code quality standards for correct and reproducible simulations.
  • Community-driven: NEST has fostered a large community of experienced developers and amazing users, who actively contribute to the project. Our community extends to related projects, like the teaching tool NEST Desktop, cross-simulator languages like PyNN and neural activity analysis tools like Elephant.
Visualization of NEST. NEST simulator


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