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README.md

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NEST is a simulator for spiking neural network models that focuses on the
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dynamics, size and structure of neural systems rather than on the exact
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morphology of individual neurons. The development of NEST is coordinated by the
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NEST Initiative. General information on the NEST Initiative can be found at
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its homepage at https://www.nest-initiative.org.
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morphology of individual neurons.
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A NEST simulation tries to follow the logic of an electrophysiological
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experiment that takes place inside a computer with the difference that the
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neural system to be investigated must be defined by the experimenter.
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NEST is ideal for networks of spiking neurons of any size, for example:
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- Models of information processing e.g. in the visual or auditory cortex of
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- Models of information processing, e.g., in the visual or auditory cortex of
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mammals,
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- Models of network activity dynamics, e.g. laminar cortical networks or
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- Models of network activity dynamics, e.g., laminar cortical networks or
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balanced random networks,
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- Models of learning and plasticity.
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For copyright information please refer to the `LICENSE` file and to the
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information header in the source files.
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## Key features of NEST
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## How do I use NEST?
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* NEST provides a Python interface or a stand-alone application
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* NEST provides a large collection of [neurons and synapse models](https://nest-simulator.org/documentation/models/index.html)
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* NEST provides numerous [example network scripts](https://nest-simulator.org/documentation/examples/index.html) along with
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[tutorials and guides](https://nest-simulator.org/documentation/get-started_index.html) to help you develop your simulation
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* NEST has a large community of experienced developers and users; NEST was first released in 1994 under the name SYNOD, and has been extended and improved ever since
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* NEST is extensible: you can extend NEST by adding your own modules
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* NEST is scalable: Use NEST on your laptop or the largest supercomputers
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* NEST is memory efficient: It makes the best use of your multi-core computer and compute clusters with minimal user intervention
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* NEST is an open source project and is licensed under the GNU General Public License v2 or later
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* NEST employs continuous integration workflows in order to maintain high code quality standards for correct and reproducible simulations
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You can use NEST either via Python (PyNEST) or as a stand-alone application
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(nest). PyNEST provides a set of commands to the Python interpreter which give
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you access to NEST's simulation kernel. With these commands, you describe and
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run your network simulation. You can also complement PyNEST with PyNN, a
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simulator-independent set of Python commands to formulate and run neural
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simulations. While you define your simulations in Python, the actual simulation
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is executed within NEST's highly optimized simulation kernel which is written
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in C++.
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A NEST simulation tries to follow the logic of an electrophysiological
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experiment that takes place inside a computer with the difference, that the
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neural system to be investigated must be defined by the experimenter.
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## Documentation
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The neural system is defined by a possibly large number of neurons and their
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connections. In a NEST network, different neuron and synapse models can
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coexist. Any two neurons can have multiple connections with different
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properties. Thus, the connectivity can in general not be described by a weight
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or connectivity matrix but rather as an adjacency list.
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To manipulate or observe the network dynamics, the experimenter can define
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so-called devices which represent the various instruments (for measuring and
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stimulation) found in an experiment. These devices write their data either to
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memory or to file.
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NEST is extensible and new models for neurons, synapses, and devices can be
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added.
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To get started with NEST, please see the [Documentation Page for
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Tutorials](https://www.nest-simulator.org/documentation/).
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## Why should I use NEST?
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To learn more about the capabilities of NEST, please read the complete [feature
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summary](https://www.nest-simulator.org/features/).
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- NEST provides over 50 neuron models many of which have been published. Choose
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from simple integrate-and-fire neurons with current or conductance based
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synapses, over the Izhikevich or AdEx models, to Hodgkin-Huxley models.
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- NEST provides over 10 synapse models, including short-term plasticity
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(Tsodyks & Markram) and different variants of spike-timing dependent
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plasticity (STDP).
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- NEST provides many examples that help you getting started with your own
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simulation project.
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- NEST offers convenient and efficient commands to define and connect large
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networks, ranging from algorithmically determined connections to data-driven
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connectivity.
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- NEST lets you inspect and modify the state of each neuron and each connection
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at any time during a simulation.
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- NEST is fast and memory efficient. It makes best use of your multi-core
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computer and compute clusters with minimal user intervention.
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- NEST runs on a wide range of UNIX-like systems, from MacBooks to
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supercomputers.
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- NEST has minimal dependencies. All it really needs is a C++ compiler.
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Everything else is optional.
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- NEST developers are using agile continuous integration-based workflows in
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order to maintain high code quality standards for correct and reproducible
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simulations.
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- NEST has one of the largest and most experienced developer communities of all
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neural simulators. NEST was first released in 1994 under the name SYNOD and
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has been extended and improved ever since.
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Please visit our [online documentation](https://nest-simulator.org/documentation) for details on installing and using NEST.
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## License
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NEST is open source software and is licensed under the [GNU General Public
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License v2](https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html) or
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later.
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## Cite NEST
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If you use NEST as part of your research, please cite the *version* of NEST you used.
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The full citation for each release can be found on [Zenodo](https://zenodo.org/search?q=title%3ANEST%20AND%20-description%3Agraphical%20AND%20simulator&l=list&p=1&s=10&sort=publication-desc)
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For general citations, please use
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`Gewaltig M-O & Diesmann M (2007) NEST (Neural Simulation Tool) Scholarpedia 2(4):1430.`
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## Contact
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## Installing NEST
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If you need help or would like to discuss an idea or issue,
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join our [maling list](https://nest-simulator.org/documentation/developer_space/guidelines/mailing_list_guidelines.html),
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where we encourage active participation from our developers and users to share their knowledge and experience with NEST.
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Please see the online [NEST Installation Instructions](http://www.nest-simulator.org/installation)
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to find out how to install NEST.
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## Getting help
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You can find other [ways to get in touch here](https://nest-simulator.org/documentation/community.html).
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- You can run the `help` command in the NEST interpreter to find documentation
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and learn more about available commands.
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- For queries regarding NEST usage, please use the [NEST users mailing
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list](https://www.nest-initiative.org/mailinglist/).
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- Information on the Python bindings to NEST can be found in
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`${prefix}/share/doc/nest/README.md`.
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- For those looking to extend NEST, developer documentation on [Contributing to
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NEST](https://nest-simulator.readthedocs.io/en/latest/contribute/index.html) is available.
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## Citing NEST
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## Contribute
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Please cite NEST if you use it in your work.
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NEST is built on an active community and we welcome contributions to our code and documentation.
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- You can find all the information for [citing NEST here](https://nest-simulator.readthedocs.io/en/latest/citing-nest.html)
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For bug reports, feature requests, documentation improvements, or other issues,
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you can create a [GitHub issue](https://github.com/nest/nest-simulator/issues/new/choose),
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For working with NEST code and documentation, you can find guidelines for contributions
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[in our documentation](https://nest-simulator.org/documentation/developer_space/index.html#contribute-to-nest)
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## Publications
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You can find a list of NEST [related publications here](https://www.nest-simulator.org/publications/).
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## License
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NEST is open source software and is licensed under the [GNU General Public
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License v2](https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html) or
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later.
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General information on the NEST Initiative can be found at
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its homepage at https://www.nest-initiative.org.

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