NEST is a simulator for spiking neural network models that focuses on the dynamics, size and structure of neural systems rather than on the exact morphology of individual neurons. The development of NEST is coordinated by the NEST Initiative. General information on the NEST Initiative can be found at its homepage at https://www.nest-initiative.org.

NEST is ideal for networks of spiking neurons 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.

For copyright information please refer to the LICENSE file and to the information header in the source files.

You can use NEST either via Python (PyNEST) or as a stand-alone application (nest). PyNEST provides a set of commands to the Python interpreter which give you access to NEST's simulation kernel. With these commands, you describe and run your network simulation. You can also complement PyNEST with PyNN, a simulator-independent set of Python commands to formulate and run neural simulations. While you define your simulations in Python, the actual simulation is executed within NEST's highly optimized simulation kernel which is written in C++.

A NEST simulation tries to follow the logic of an electrophysiological experiment that takes place inside a computer with the difference, that the neural system to be investigated must be defined by the experimenter.

The neural system is defined by a possibly large number of neurons and their connections. In a NEST network, different neuron and synapse models can coexist. Any two neurons can have multiple connections with different properties. Thus, the connectivity can in general not be described by a weight or connectivity matrix but rather as an adjacency list.

To manipulate or observe the network dynamics, the experimenter can define so-called devices which represent the various instruments (for measuring and stimulation) found in an experiment. These devices write their data either to memory or to file.

NEST is extensible and new models for neurons, synapses, and devices can be added.

To get started with NEST, please see the Documentation Page for Tutorials.

To learn more about the capabilities of NEST, please read the complete feature summary.

• NEST provides over 50 neuron models many of which have been published. Choose from simple integrate-and-fire neurons with current or conductance based synapses, over the Izhikevich or AdEx models, to Hodgkin-Huxley models.
• NEST provides over 10 synapse models, including short-term plasticity (Tsodyks & Markram) and different variants of spike-timing dependent plasticity (STDP).
• NEST provides many examples that help you getting started with your own simulation project.
• NEST offers convenient and efficient commands to define and connect large networks, ranging from algorithmically determined connections to data-driven connectivity.
• NEST lets you inspect and modify the state of each neuron and each connection at any time during a simulation.
• NEST is fast and memory efficient. It makes best use of your multi-core computer and compute clusters with minimal user intervention.
• NEST runs on a wide range of UNIX-like systems, from MacBooks to BlueGene supercomputers.
• NEST has minimal dependencies. All it really needs is a C++ compiler. Everything else is optional.
• NEST developers are using agile continuous integration-based workflows in order to maintain high code quality standards for correct and reproducible simulations.
• NEST has one of the largest and most experienced developer communities of all neural simulators. NEST was first released in 1994 under the name SYNOD and has been extended and improved ever since.

NEST is open source software and is licensed under the GNU General Public License v2 or later.

Generic installation instructions can be found in the INSTALL file that you received in the NEST sources.

• You can run the help command in the NEST interpreter to find documentation and learn more about available commands.
• For queries regarding NEST usage, please use the NEST users mailing list.
• Information on the Python bindings to NEST can be found in ${prefix}/share/doc/nest/README.md.
• For those looking to extend NEST a developer manual is available.

Please cite NEST if you use it in your work.

If you use NEST 2.20.0, please cite it as Fardet, Tanguy et al. (2020) NEST 2.20.0. Zenodo. 10.5281/zenodo.3605514. The full citation is available in different formats on Zenodo.

If you use NEST 2.18.0, please cite it as Jordan, Jakob et al. (2019) NEST 2.18.0. Zenodo. 10.5281/zenodo.2605422. The full citation is available in different formats on Zenodo.

If you use NEST 2.16.0, please cite it as Linssen, Charl et al. (2018) NEST 2.16.0. Zenodo. 10.5281/zenodo.1400175. The full citation is available in different formats on Zenodo.

If you use NEST 2.14.0, please cite it as Peyser, Alexander et al. (2017). NEST 2.14.0. Zenodo. 10.5281/zenodo.882971. The full citation is available in different formats on Zenodo.

If you use NEST 2.12.0, please cite it as Kunkel, Susanne et al. (2017). NEST 2.12.0. Zenodo. 10.5281/zenodo.259534. The full citation is available in different formats on Zenodo.

If you use NEST v2.10.0, please cite it as Bos, Hannah et al. (2015). NEST 2.10.0. Zenodo. 10.5281/zenodo.44222. The full citation is available in different formats on Zenodo.

If you use NEST v2.8.0, please cite it as Eppler, Jochen Martin et al. (2015). NEST 2.8.0. Zenodo. 10.5281/zenodo.32969. The full citation is available in different formats on Zenodo.

For all versions below NEST v2.8.0 and for citing NEST without referring to a specific version, please use: Gewaltig M-O & Diesmann M (2007) NEST (Neural Simulation Tool) Scholarpedia 2(4):1430.

Here is a suitable BibTeX entry:

@ARTICLE{Gewaltig:NEST,
  author  = {Marc-Oliver Gewaltig and Markus Diesmann},
  title   = {NEST (NEural Simulation Tool)},
  journal = {Scholarpedia},
  year    = {2007},
  volume  = {2},
  pages   = {1430},
  number  = {4}
}

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