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 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.
- 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 2006-2024 by the PyNN team, see AUTHORS.
:license: CeCILL, see LICENSE for details.
.. image:: https://github.com/NeuralEnsemble/PyNN/actions/workflows/full-test.yml/badge.svg
:target: https://github.com/NeuralEnsemble/PyNN/actions/workflows/full-test.yml
: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
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).
Raw data
{
"_id": null,
"home_page": null,
"name": "PyNN",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": "The PyNN team <pynn-maintainers@protonmail.com>",
"keywords": "computational neuroscience, simulation, neuron, nest, brian2, neuromorphic",
"author": null,
"author_email": "The PyNN team <pynn-maintainers@protonmail.com>",
"download_url": "https://files.pythonhosted.org/packages/2d/f1/87be1610a71f21349d2e299d6cf92d55893c6aeb2c1730dd758bec2671d9/PyNN-0.12.3.tar.gz",
"platform": null,
"description": "PyNN\n====\n\nPyNN (pronounced '*pine*') is a simulator-independent language for building\nneuronal network models.\n\nIn other words, you can write the code for a model once, using the PyNN API and\nthe Python programming language, and then run it without modification on any\nsimulator that PyNN supports (currently NEURON, NEST and Brian 2) and\non a number of neuromorphic hardware systems.\n\nThe PyNN API aims to support modelling at a high-level of abstraction\n(populations of neurons, layers, columns and the connections between them) while\nstill allowing access to the details of individual neurons and synapses when\nrequired. PyNN provides a library of standard neuron, synapse and synaptic\nplasticity models, which have been verified to work the same on the different\nsupported simulators. PyNN also provides a set of commonly-used connectivity\nalgorithms (e.g. all-to-all, random, distance-dependent, small-world) but makes\nit easy to provide your own connectivity in a simulator-independent way.\n\nEven if you don't wish to run simulations on multiple simulators, you may\nbenefit from writing your simulation code using PyNN's powerful, high-level\ninterface. In this case, you can use any neuron or synapse model supported by\nyour simulator, and are not restricted to the standard models.\n\n\n- Home page: http://neuralensemble.org/PyNN/\n- Documentation: http://neuralensemble.org/docs/PyNN/\n- Mailing list: https://groups.google.com/forum/?fromgroups#!forum/neuralensemble\n- Bug reports: https://github.com/NeuralEnsemble/PyNN/issues\n\n\n:copyright: Copyright 2006-2024 by the PyNN team, see AUTHORS.\n:license: CeCILL, see LICENSE for details.\n\n.. image:: https://github.com/NeuralEnsemble/PyNN/actions/workflows/full-test.yml/badge.svg\n :target: https://github.com/NeuralEnsemble/PyNN/actions/workflows/full-test.yml\n :alt: Unit Test Status\n\n.. image:: https://coveralls.io/repos/NeuralEnsemble/PyNN/badge.svg?branch=master&service=github\n :target: https://coveralls.io/github/NeuralEnsemble/PyNN?branch=master\n :alt: Test coverage\n\nFunding\n-------\n\nDevelopment of PyNN has been partially funded by the European Union Sixth Framework Program (FP6) under\ngrant agreement FETPI-015879 (FACETS), by the European Union Seventh Framework Program (FP7/2007\u00ad-2013)\nunder grant agreements no. 269921 (BrainScaleS) and no. 604102 (HBP),\nand by the European Union\u2019s Horizon 2020 Framework Programme for\nResearch and Innovation under the Specific Grant Agreements No. 720270 (Human Brain Project SGA1)\n, No. 785907 (Human Brain Project SGA2) and No. 945539 (Human Brain Project SGA3).\n",
"bugtrack_url": null,
"license": "CeCILL http://www.cecill.info",
"summary": "A Python package for simulator-independent specification of neuronal network models",
"version": "0.12.3",
"project_urls": {
"changelog": "http://neuralensemble.org/docs/PyNN/release_notes.html",
"documentation": "http://neuralensemble.org/docs/PyNN/",
"download": "http://pypi.python.org/pypi/PyNN",
"homepage": "http://neuralensemble.org/PyNN/",
"repository": "https://github.com/NeuralEnsemble/PyNN"
},
"split_keywords": [
"computational neuroscience",
" simulation",
" neuron",
" nest",
" brian2",
" neuromorphic"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "decb7c40d059361ec1d27d53d51c9dc6e0b8d08986f4f910faddda1cbcb8fd0a",
"md5": "86d3aa9992a5723ace0cc991c22c3b88",
"sha256": "ac9d661521db89d16e64820ee76db2ceb495ba0b766970087dc79390d0d1253f"
},
"downloads": -1,
"filename": "PyNN-0.12.3-py3-none-any.whl",
"has_sig": false,
"md5_digest": "86d3aa9992a5723ace0cc991c22c3b88",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 344310,
"upload_time": "2024-04-17T15:34:38",
"upload_time_iso_8601": "2024-04-17T15:34:38.349339Z",
"url": "https://files.pythonhosted.org/packages/de/cb/7c40d059361ec1d27d53d51c9dc6e0b8d08986f4f910faddda1cbcb8fd0a/PyNN-0.12.3-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "2df187be1610a71f21349d2e299d6cf92d55893c6aeb2c1730dd758bec2671d9",
"md5": "8e8977adf75464cb9c6d3cd7f7497f0c",
"sha256": "e196f9055c46fe5c0e23f491815d16dca8db9be599a226ee11fa67605cab153d"
},
"downloads": -1,
"filename": "PyNN-0.12.3.tar.gz",
"has_sig": false,
"md5_digest": "8e8977adf75464cb9c6d3cd7f7497f0c",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 521282,
"upload_time": "2024-04-17T15:38:43",
"upload_time_iso_8601": "2024-04-17T15:38:43.527137Z",
"url": "https://files.pythonhosted.org/packages/2d/f1/87be1610a71f21349d2e299d6cf92d55893c6aeb2c1730dd758bec2671d9/PyNN-0.12.3.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-04-17 15:38:43",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "NeuralEnsemble",
"github_project": "PyNN",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "pynn"
}
JSON
