Metadata-Version: 2.1
Name: PsychRNN
Version: 1.0.0
Summary: Easy-to-use package for the modeling and analysis of neural network dynamics, directed towards cognitive neuroscientists.
Home-page: https://github.com/murraylab/PsychRNN
Author: Daniel Ehrlich, Jasmine Stone, Alex Atanasov, David Brandfonbrener
Author-email: psychrnn@gmail.com
License: MIT
Project-URL: Documentation, https://psychrnn.readthedocs.io/
Project-URL: Mailing List, https://www.freelists.org/list/psychrnn
Description: # PsychRNN
        [![Build Status](https://api.travis-ci.com/murraylab/PsychRNN.svg?branch=master)](https://api.travis-ci.com/murraylab/PsychRNN)
        [![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.<sup>\*</sup>, Stone, J. T.<sup>\*</sup>, 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)
        
        ## 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: psychrnn@gmail.com 
        
        ## 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 psychrnn@gmail.com.
        
        ## License
        
        All code is available under the MIT license. See LICENSE for more information.
        
Keywords: neuroscience,modeling,analysis,neural networks
Platform: UNKNOWN
Description-Content-Type: text/markdown
