Skip to content

Code for "Trained recurrent neural networks develop phase-locked limit cycles in a working memory task" - Matthijs Pals (@Matthijspals) , Jakob Macke and Omri Barak.

License

Notifications You must be signed in to change notification settings

mackelab/phase-limit-cycle-RNNs

Repository files navigation

Code for Trained recurrent neural networks develop phase-locked limit cycles in a working memory task.

click here to go to the paper

First install the conda environment: conda env create -f phase_env_ubu.yml, if you are on Ubuntu, or conda env create -f phase_env_mac.yml if you are on MacOS, then activate it.

You can train an RNN models by running python rnn_scripts/run_training.py All the paper figures can be recreated with the notebooks inside the generate_figures folder

To create the 3D plots you need to allow the Mayavi plug-in: $ jupyter nbextension install --py mayavi --user $ jupyter nbextension enable --py mayavi --user

Paper written by: Matthijs Pals, Jakob Macke and Omri Barak, code written by: Matthijs Pals

About

Code for "Trained recurrent neural networks develop phase-locked limit cycles in a working memory task" - Matthijs Pals (@Matthijspals) , Jakob Macke and Omri Barak.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published