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 2006-2019 by the PyNN team, see AUTHORS. |
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| license: | CeCILL, see LICENSE for details. |
