Science Score: 23.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 2 DOI reference(s) in README -
○Academic publication links
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✓Committers with academic emails
4 of 10 committers (40.0%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (14.4%) to scientific vocabulary
Keywords
Repository
Basic Info
- Host: GitHub
- Owner: murraylab
- License: mit
- Language: Python
- Default Branch: master
- Homepage: https://psychrnn.readthedocs.io
- Size: 1.76 MB
Statistics
- Stars: 139
- Watchers: 6
- Forks: 42
- Open Issues: 5
- Releases: 4
Topics
Metadata Files
README.md
PsychRNN
Paper:
Ehrlich, D. B.*, Stone, J. T.*, Brandfonbrener, D., Atanasov, A., & Murray, J. D. (2021). PsychRNN: An Accessible and Flexible Python Package for Training Recurrent Neural Network Models on Cognitive Tasks. ENeuro, 8(1). [DOI]
Presentation:
Prefer listening to a 15 minute talk to see if PsychRNN is for you? Check out our talk at Neuromatch 3.0.
Overview
Full documentation is available at psychrnn.readthedocs.io.
This package is intended to help cognitive scientists easily translate task designs from human or primate behavioral experiments into a form capable of being used as training data for a recurrent neural network.
We have isolated the front-end task design, in which users can intuitively describe the conditional logic of their task from the backend where gradient descent based optimization occurs. This is intended to facilitate researchers who might otherwise not have an easy implementation available to design and test hypothesis regarding the behavior of recurrent neural networks in different task environements.
Release announcments are posted on the psychrnn mailing list and on GitHub
Code is written and upkept by: Daniel B. Ehrlich, Jasmine T. Stone, David Brandfonbrener, and Alex Atanasov.
Contact: psychrnn@gmail.com
Getting Started
Start with Hello World 
to get a quick sense of what PsychRNN does. Then go through the Simple Example to get a feel for how to customize PsychRNN. The rest of Getting Started will help guide you through using available features, defining your own task, and even defining your own model.
Install
Dependencies
- python = 2.7 or python >= 3.4
- numpy
tensorflow >= 1.13.1
For notebook demos, jupyter
For notebook demos, ipython
For plotting features, matplotlib
PsychRNN was developed to work with both Python 2.7 and 3.4+ using TensorFlow 1.13.1+. It is currently being tested on Python 2.7 and 3.4-3.8 with TensorFlow 1.13.1-2.2.
Note: TensorFlow 2.2 does not support Python < 3.5. Only TensorFlow 1.13.1-1.14 are compatible with Python 3.4. Python 3.8 is only supported by TensorFlow 2.2.
Installation
Normally, you can install with:
pip install psychrnn
Alternatively, you can download and extract the source files from the GitHub release. Within the downloaded PsychRNN folder, run:
python setup.py install
[THIS OPTION IS NOT RECOMMENDED FOR MOST USERS] To get the most recent (not necessarily stable) version from the github repo, clone the repository and install:
git clone https://github.com/murraylab/PsychRNN.git
cd PsychRNN
python setup.py install
Contributing
Please report bugs to https://github.com/murraylab/psychrnn/issues. This includes any problems with the documentation. Fixes (in the form of pull requests) for bugs are greatly appreciated.
Feature requests are welcome but may or may not be accepted due to limited resources. If you implement the feature yourself we are open to accepting it in PsychRNN. If you implement a new feature in PsychRNN, please do the following before submitting a pull request on GitHub:
- Make sure your code is clean and well commented
- If appropriate, update the official documentation in the
docs/directory - Write unit tests and optionally integration tests for your new
feature in the
tests/folder. - Ensure all existing tests pass (
pytestreturns without error)
For all other questions or comments, contact psychrnn@gmail.com.
License
All code is available under the MIT license. See LICENSE for more information.
Owner
- Name: murraylab
- Login: murraylab
- Kind: organization
- Repositories: 11
- Profile: https://github.com/murraylab
GitHub Events
Total
- Watch event: 5
- Fork event: 1
Last Year
- Watch event: 5
- Fork event: 1
Committers
Last synced: about 1 year ago
Top Committers
| Name | Commits | |
|---|---|---|
| syncrostone | j****e@g****m | 41 |
| Jasmine Stone | j****e@g****m | 29 |
| plhijk | 5****k | 4 |
| Daniel | d****h@y****u | 4 |
| johndmurray | j****y@y****u | 3 |
| Alex Atanasov | s****n@g****m | 3 |
| isagarnreiter | 5****r | 1 |
| Paxton Fitzpatrick | P****R@D****u | 1 |
| PLHXYZ | 5****Z | 1 |
| Gary Kane | g****e@b****u | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 11 months ago
All Time
- Total issues: 17
- Total pull requests: 32
- Average time to close issues: 9 months
- Average time to close pull requests: 29 days
- Total issue authors: 5
- Total pull request authors: 9
- Average comments per issue: 0.59
- Average comments per pull request: 1.28
- Merged pull requests: 28
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- syncrostone (12)
- ahwillia (2)
- amchagas (1)
- morteza (1)
- esigalas (1)
Pull Request Authors
- syncrostone (18)
- plhijk (5)
- ABAtanasov (3)
- isagarnreiter (1)
- paxtonfitzpatrick (1)
- bergem1t (1)
- mwshinn (1)
- gkane26 (1)
- carolinehaoud (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 221 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 3
- Total maintainers: 2
pypi.org: psychrnn
Easy-to-use package for the modeling and analysis of neural network dynamics, directed towards cognitive neuroscientists.
- Homepage: https://github.com/murraylab/PsychRNN
- Documentation: https://psychrnn.readthedocs.io/
- License: MIT
-
Latest release: 1.0.0
published over 5 years ago
Rankings
Maintainers (2)
Dependencies
- autodocsumm *
- ipykernel *
- nbsphinx *
- numpy *
- sphinx >=3.0.4
- sphinx_copybutton *
- sphinxcontrib.napoleon *
- tensorflow *
- python_version >=
- tensorflow *
- actions/checkout v3 composite
- actions/setup-python v4 composite
- codecov/codecov-action v1 composite