Welcome to PsychRNN’s documentation!

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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.

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.

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