Skip to main content

A Python package for simulator-independent specification of neuronal network models

Project description

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.

copyright:

Copyright 2006-2016 by the PyNN team, see AUTHORS.

license:

CeCILL, see LICENSE for details.

Unit Test Status Test coverage

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

PyNN-0.8.1.tar.gz (307.4 kB view details)

Uploaded Source

File details

Details for the file PyNN-0.8.1.tar.gz.

File metadata

  • Download URL: PyNN-0.8.1.tar.gz
  • Upload date:
  • Size: 307.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for PyNN-0.8.1.tar.gz
Algorithm Hash digest
SHA256 ce94246284588414d1570c1d5d697805f781384e771816727c830b01ee30fe39
MD5 7fb165ed5af35a115cb9c60991645ae6
BLAKE2b-256 4330291dab827c99068ca33a7d3dbfe5927bcfe0338556054bf850334e723931

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page