PyNN (pronounced 'pine') is a 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, PCSIM and Brian).
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, either using the Connection Set Algebra ( Djurfeldt, 2010) or by writing your own Python code.
The low-level API is good for small networks, and perhaps gives more flexibility. The high-level API is good for hiding the details and the book-keeping, allowing you to concentrate on the overall structure of your model.
The other thing that is required to write a model once and run it on multiple simulators is standard cell models. PyNN translates standard cell-model names and parameter names into simulator-specific names, e.g. standard model IF_curr_alpha is iaf_neuron in NEST and StandardIF in NEURON, while SpikeSourcePoisson is a poisson_generator in NEST and a NetStim in NEURON. Only a few cell models have been implemented so far.
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.
PyNN is a work in progress, but is already being used for several large-scale simulation projects.
The code is released under the CeCILL licence.
- Download and Install
- Running simulations with PyNN
- Standardised neuron models
- Standardised current sources
- Synaptic plasticity
- Using non-standard/native cell and synapse models
- Representing spatial structure and calculating distances
- Running parallel simulations
- File formats
- The procedural API
- Simulator-specific options
- Simulator-specific optimizations
- API documentation
If you publish work using or mentioning PyNN, we would appreciate it if you would cite the following paper:
Davison AP, Brüderle D, Eppler JM, Kremkow J, Muller E, Pecevski DA, Perrinet L and Yger P (2008) PyNN: a common interface for neuronal network simulators. Front. Neuroinform. doi:10.3389/neuro.11.011.2008
Presentations and Posters
also see the list of publications about, relating to or using PyNN.
Questions / Bugs / Enhancements
If you find a bug in PyNN, or wish to request a new feature, please click on "New Ticket" in the menu above and fill in the form.
If you have questions or comments about PyNN, please post a message in the NeuralEnsemble Google group. You can also e-mail Andrew Davison, but it is better to post on the forums. If you are not a member of NeuralEnsemble, go to http://groups.google.com/group/neuralensemble and click on the "Apply for membership" link.
Information about this Trac server:
- TracGuide -- Built-in Documentation
- The Trac project -- Trac Open Source Project
- Trac FAQ -- Frequently Asked Questions
- TracSupport -- Trac Support
- For a complete list of local wiki pages, see TitleIndex.
The Neural Ensemble Ring
Related projects at http://neuralensemble.org:
- NeuroTools -- a set of tools written in Python to manage, store and analyze computational neuroscience simulations.
PyNN development is supported in part by the EU under the grant IST-2005-15879 ( FACETS) .