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https://github.com/murraylab/PsychRNN

cognitive-neural-processes neural-networks recurrent-neural-networks tensorflow

Last synced: 1 day ago
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Host: GitHub
URL: https://github.com/murraylab/PsychRNN
Owner: murraylab
License: mit
Created: 2018-10-11T20:13:41.000Z (about 6 years ago)
Default Branch: master
Last Pushed: 2023-11-05T20:01:23.000Z (about 1 year ago)
Last Synced: 2024-11-11T11:07:49.333Z (2 days ago)
Topics: cognitive-neural-processes, neural-networks, recurrent-neural-networks, tensorflow
Language: Python
Homepage: https://psychrnn.readthedocs.io
Size: 1.76 MB
Stars: 134
Watchers: 7
Forks: 42
Open Issues: 5
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # PsychRNN

[![tests](https://github.com/murraylab/PsychRNN/workflows/tests/badge.svg)](https://github.com/murraylab/PsychRNN/actions?query=workflow%3Atests)

[![codecov](https://codecov.io/gh/murraylab/PsychRNN/branch/master/graph/badge.svg)](https://codecov.io/gh/murraylab/PsychRNN)

[![Documentation Status](https://readthedocs.org/projects/psychrnn/badge/?version=latest)](https://psychrnn.readthedocs.io/en/latest/?badge=latest)

**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). [\[DOI\]](https://doi.org/10.1523/ENEURO.0427-20.2020)

**Presentation:**

Prefer listening to a 15 minute talk to see if PsychRNN is for you? Check out our [talk at Neuromatch 3.0](https://www.youtube.com/watch?v=OY_Ix-QwB4Y).

## Overview

Full documentation is available at [psychrnn.readthedocs.io](https://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](https://www.freelists.org/list/psychrnn) and on [GitHub](https://github.com/murraylab/PsychRNN)

Code is written and upkept by: [Daniel B. Ehrlich](https://github.com/dbehrlich>), [Jasmine T. Stone](https://github.com/syncrostone/), [David Brandfonbrener](https://github.com/davidbrandfonbrener), and [Alex Atanasov](https://github.com/ABAtanasov).

Contact: [email protected] 

## Getting Started

Start with [Hello World](https://psychrnn.readthedocs.io/en/latest/notebooks/Minimal_Example.html) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/murraylab/PsychRNN/blob/master/docs/notebooks/Minimal_Example.ipynb) to get a quick sense of what PsychRNN does. Then go through the [Simple Example](https://psychrnn.readthedocs.io/en/latest/notebooks/PerceptualDiscrimination.html) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/murraylab/PsychRNN/blob/master/docs/notebooks/PerceptualDiscrimination.ipynb) to get a feel for how to customize PsychRNN. The rest of [Getting Started](https://psychrnn.readthedocs.io/en/quickstart.html) 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](http://www.numpy.org/)

- [tensorflow](https://www.tensorflow.org/) >= 1.13.1

- For notebook demos, [jupyter](https://jupyter.org/)

- For notebook demos, [ipython](https://ipython.org/)

- For plotting features, [matplotlib](https://matplotlib.org/)

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](https://github.com/murraylab/psychrnn/releases/). 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 (``pytest`` returns without

  error)

For all other questions or comments, contact [email protected].

## License

All code is available under the MIT license. See LICENSE for more information.