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 2) 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 2006-2024 by the PyNN team, see AUTHORS.

license:

CeCILL, see LICENSE for details.

Unit Test Status Test coverage

Funding

Development of PyNN has been partially funded by the European Union Sixth Framework Program (FP6) under grant agreement FETPI-015879 (FACETS), by the European Union Seventh Framework Program (FP7/2007­-2013) under grant agreements no. 269921 (BrainScaleS) and no. 604102 (HBP), and by the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreements No. 720270 (Human Brain Project SGA1) , No. 785907 (Human Brain Project SGA2) and No. 945539 (Human Brain Project SGA3).

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-0.12.3.tar.gz (521.3 kB view details)

Uploaded Source

Built Distribution

PyNN-0.12.3-py3-none-any.whl (344.3 kB view details)

Uploaded Python 3

File details

Details for the file PyNN-0.12.3.tar.gz.

File metadata

  • Download URL: PyNN-0.12.3.tar.gz
  • Upload date:
  • Size: 521.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.11.4

File hashes

Hashes for PyNN-0.12.3.tar.gz
Algorithm Hash digest
SHA256 e196f9055c46fe5c0e23f491815d16dca8db9be599a226ee11fa67605cab153d
MD5 8e8977adf75464cb9c6d3cd7f7497f0c
BLAKE2b-256 2df187be1610a71f21349d2e299d6cf92d55893c6aeb2c1730dd758bec2671d9

See more details on using hashes here.

File details

Details for the file PyNN-0.12.3-py3-none-any.whl.

File metadata

  • Download URL: PyNN-0.12.3-py3-none-any.whl
  • Upload date:
  • Size: 344.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.11.4

File hashes

Hashes for PyNN-0.12.3-py3-none-any.whl
Algorithm Hash digest
SHA256 ac9d661521db89d16e64820ee76db2ceb495ba0b766970087dc79390d0d1253f
MD5 86d3aa9992a5723ace0cc991c22c3b88
BLAKE2b-256 decb7c40d059361ec1d27d53d51c9dc6e0b8d08986f4f910faddda1cbcb8fd0a

See more details on using hashes here.

Supported by

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