nengo

A Python library for creating and simulating large-scale brain models

https://github.com/nengo/nengo

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
✓
DOI references
Found 2 DOI reference(s) in README
○
Academic publication links
✓
Committers with academic emails
15 of 40 committers (37.5%) from academic institutions
○
Institutional organization owner
○
JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (14.5%) to scientific vocabulary

Keywords

nengo neural-networks neuroscience python

Keywords from Contributors

optimizing-compiler qt

Last synced: 6 months ago · JSON representation ·

Repository

A Python library for creating and simulating large-scale brain models

Basic Info

Host: GitHub
Owner: nengo
License: other
Language: Python
Default Branch: main
Homepage: https://www.nengo.ai/nengo
Size: 110 MB

Statistics

Stars: 875
Watchers: 75
Forks: 187
Open Issues: 137
Releases: 21

Topics

nengo neural-networks neuroscience python

Created almost 13 years ago · Last pushed 9 months ago

Metadata Files

Readme Changelog Contributing License Citation

README.rst

.. image:: https://img.shields.io/pypi/v/nengo.svg
  :target: https://pypi.org/project/nengo
  :alt: Latest PyPI version

.. image:: https://img.shields.io/pypi/pyversions/nengo.svg
  :target: https://pypi.org/project/nengo
  :alt: Python versions

********************************************
Nengo: Large-scale brain modelling in Python
********************************************

.. image:: https://www.nengo.ai/design/_images/general-nef-summary.svg
  :width: 100%
  :target: https://doi.org/10.3389/fninf.2013.00048
  :alt: An illustration of the three principles of the NEF

Nengo is a Python library for building and simulating
large-scale neural models.
Nengo can create sophisticated
spiking and non-spiking neural simulations
with sensible defaults in a few lines of code.
Yet, Nengo is highly extensible and flexible.
You can define your own neuron types and learning rules,
get input directly from hardware,
build and run deep neural networks,
drive robots, and even simulate your model
on a completely different neural simulator
or neuromorphic hardware.

Installation
============

Nengo depends on NumPy, and we recommend that you
install NumPy before installing Nengo.
If you're not sure how to do this, we recommend using
`Anaconda `_.

To install Nengo::

    pip install nengo

If you have difficulty installing Nengo or NumPy,
please read the more detailed
`Nengo installation instructions
`_ first.

If you'd like to install Nengo from source,
please read the `developer installation instructions
`_.

Nengo is tested to work on Python 3.6 and above.
Python 2.7 and Python 3.4 were supported up to and including Nengo 2.8.0.
Python 3.5 was supported up to and including Nengo 3.1.

Examples
========

Here are six of
`many examples `_
showing how Nengo enables the creation and simulation of
large-scale neural models in few lines of code.

1. `100 LIF neurons representing a sine wave
   `_
2. `Computing the square across a neural connection
   `_
3. `Controlled oscillatory dynamics with a recurrent connection
   `_
4. `Learning a communication channel with the PES rule
   `_
5. `Simple question answering with the Semantic Pointer Architecture
   `_
6. `A summary of the principles underlying all of these examples
   `_

Documentation
=============

Usage and API documentation can be found at
``_.

To build the documentation yourself, `see the Developer Guide
`_.

Development
===========

Information for current or prospective developers can be found
at ``_.

Getting Help
============

Questions relating to Nengo, whether it's use or it's development, should be
asked on the Nengo forum at ``_.

Owner

Name: Nengo
Login: nengo
Kind: organization

Website: https://nengo.ai/
Repositories: 30
Profile: https://github.com/nengo

Citation (CITATION.rst)

********
Citation
********

If you would like to cite Nengo in your research, please cite `this
paper <http://compneuro.uwaterloo.ca/files/publications/bekolay.2014.pdf>`_:

   Bekolay, Bergstra, Hunsberger, DeWolf, Stewart, Rasmussen, Choo,
   Voelker & Eliasmith. (2014) Nengo: a Python tool for building large-scale
   functional brain models. Frontiers in Neuroinformatics 7.

A BibTeX entry for LaTeX users is:

.. code-block:: tex

   @article{
     Bekolay2014,
     title = {Nengo: a {Python} tool for building large-scale functional brain models},
     author = {Bekolay, Trevor and Bergstra, James and Hunsberger, Eric
               and DeWolf, Travis and Stewart, Terrence and Rasmussen, Daniel
               and Choo, Xuan and Voelker, Aaron and Eliasmith, Chris},
     journal = {Frontiers in Neuroinformatics},
     pages = {1--13},
     volume = {7},
     number = {48},
     year = {2014},
     issn = {1662-5196},
     doi = {10.3389/fninf.2013.00048}
   }

GitHub Events

Total

Issues event: 5
Watch event: 60
Delete event: 1
Member event: 5
Issue comment event: 3
Push event: 23
Pull request event: 4
Fork event: 16
Create event: 6

Last Year

Issues event: 5
Watch event: 60
Delete event: 1
Member event: 5
Issue comment event: 3
Push event: 23
Pull request event: 4
Fork event: 16
Create event: 6

Committers

Last synced: 12 months ago

All Time

Total Commits: 1,756
Total Committers: 40
Avg Commits per committer: 43.9
Development Distribution Score (DDS): 0.666

Past Year

Commits: 0
Committers: 0
Avg Commits per committer: 0.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
Trevor Bekolay	t**y@g**m	586
Eric Hunsberger	e**s@g**m	364
Jan Gosmann	j**n@u**a	245
Daniel Rasmussen	d**n@a**m	151
James Bergstra	j**a@g**m	97
Aaron Voelker	a**e@g**m	48
Terry Stewart	t**r@u**a	43
Sean Aubin	s**n@u**a	38
hunse	h**e@c**n	29
xchoo	x**o@h**m	28
NengoBones	i**o@a**m	14
Eric Crawford	e**o@u**a	14
pblouw	p**w@u**a	13
travis dewolf	t**f@g**m	12
Chris	c**i@v**a	12
Genevieve Serafin	m**a@g**m	5
bmorcos	m**n@g**m	5
Chris Eliasmith	c**h@u**a	5
Youssef Zaky	y**y@g**m	5
Alex	a**t@i**g	4
Chris	c**i@v**a	4
Sugandha Sharma	s**4@g**m	3
Ivana Kajic	i**c@g**m	3
Chris	c**i@s**a	3
Chris Eliasmith	c**i@C**l	2
studywolf	t**f@u**a	2
WAEliasmith	a**h@a**m	2
Oliver Trujillo	o**p@h**m	2
AllenHW	a**g@g**m	2
AndrewMundy	a**y@i**g	2
and 10 more...

Committer Domains (Top 20 + Academic)

uwaterloo.ca: 9 appliedbrainresearch.com: 3 ieee.org: 2 contextlogic.com: 1 waterloo.ca: 1 gtri.gatech.edu: 1 tcd.ie: 1 eliasmiths.com: 1 subcortex.uwaterloo.ca: 1 v1020-wn-119-88.campus-dynamic.uwaterloo.ca: 1 v1020-wn-29-227.campus-dynamic.uwaterloo.ca: 1

Issues and Pull Requests

Last synced: 8 months ago

All Time

Total issues: 95
Total pull requests: 74
Average time to close issues: 7 months
Average time to close pull requests: about 2 months
Total issue authors: 7
Total pull request authors: 10
Average comments per issue: 2.77
Average comments per pull request: 2.18
Merged pull requests: 29
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 2
Pull requests: 2
Average time to close issues: 5 months
Average time to close pull requests: 11 days
Issue authors: 2
Pull request authors: 2
Average comments per issue: 0.5
Average comments per pull request: 0.0
Merged pull requests: 1
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

jaberg (28)
tbekolay (20)
hunse (3)
hanleyc01 (1)
daveweathers24 (1)
RJZS (1)
giamp66 (1)
tcstewar (1)
baswunderlich (1)
arvoelke (1)

Pull Request Authors

jaberg (19)
tbekolay (15)
hunse (7)
xchoo (3)
tcstewar (3)
LFRusso (2)
otrujill (1)
studywolf (1)
PeterSuma (1)
celiasmith (1)
carlosgmartin (1)

Top Labels

Issue Labels

bug (2)

Pull Request Labels

Packages

Total packages: 2
Total downloads:
- pypi 2,666 last-month

Total dependent packages: 5
(may contain duplicates)
Total dependent repositories: 45
(may contain duplicates)
Total versions: 43
Total maintainers: 4

pypi.org: nengo

Tools for building and simulating large-scale neural models

Homepage: https://www.nengo.ai/nengo
Documentation: https://nengo.readthedocs.io/
License: GNU General Public License, version 2
Latest release: 4.1.0
published 9 months ago

Versions: 21
Dependent Packages: 5
Dependent Repositories: 45
Downloads: 2,666 Last month

Rankings

Dependent packages count: 1.6%

Dependent repos count: 2.2%

Average: 2.9%

Downloads: 4.9%

Maintainers (4)

abr drasmuss tbekolay TerryStewart

Last synced: 6 months ago

proxy.golang.org: github.com/nengo/nengo

Documentation: https://pkg.go.dev/github.com/nengo/nengo#section-documentation
License: other
Latest release: v4.1.0+incompatible
published 9 months ago

Versions: 22
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 6.5%

Average: 6.7%

Dependent repos count: 6.9%

Last synced: 7 months ago

Dependencies

nengo/_vendor/requirements.txt pypi

portalocker ==1.1.0

.github/workflows/ci.yml actions

actions/checkout v3 composite
actions/upload-artifact v3 composite
nengo/nengo-bones/actions/coverage-report main composite
nengo/nengo-bones/actions/generate-and-check main composite
nengo/nengo-bones/actions/run-script main composite
nengo/nengo-bones/actions/setup main composite

pyproject.toml pypi

setup.py pypi