PyNN
====

PyNN (pronounced '*pine*') is a simulator-independent language for building
neuronal network models.

In other words, you can write the code for a model once, using the PyNN API and
the Python programming language, and then run it without modification on any
simulator that PyNN supports (currently NEURON, NEST and Brian) and
on a number of neuromorphic hardware systems.

The PyNN API aims to support modelling at a high-level of abstraction
(populations of neurons, layers, columns and the connections between them) while
still allowing access to the details of individual neurons and synapses when
required. PyNN provides a library of standard neuron, synapse and synaptic
plasticity models, which have been verified to work the same on the different
supported simulators. PyNN also provides a set of commonly-used connectivity
algorithms (e.g. all-to-all, random, distance-dependent, small-world) but makes
it easy to provide your own connectivity in a simulator-independent way.

Even if you don't wish to run simulations on multiple simulators, you may
benefit from writing your simulation code using PyNN's powerful, high-level
interface. In this case, you can use any neuron or synapse model supported by
your simulator, and are not restricted to the standard models.


- Home page: http://neuralensemble.org/PyNN/
- Documentation: http://neuralensemble.org/docs/PyNN/
- Mailing list: https://groups.google.com/forum/?fromgroups#!forum/neuralensemble
- Bug reports: https://github.com/NeuralEnsemble/PyNN/issues


:copyright: Copyright 2006-2020 by the PyNN team, see AUTHORS.
:license: CeCILL, see LICENSE for details.

.. image:: https://travis-ci.org/NeuralEnsemble/PyNN.png?branch=master
   :target: https://travis-ci.org/NeuralEnsemble/PyNN
   :alt: Unit Test Status

.. image:: https://coveralls.io/repos/NeuralEnsemble/PyNN/badge.svg?branch=master&service=github
   :target: https://coveralls.io/github/NeuralEnsemble/PyNN?branch=master
   :alt: Test coverage
