This repository contains the data and code to reproduce the results of "Efficient neural decoding of self-location with a deep recurrent network" (see initial draft in https://www.biorxiv.org/content/early/2018/01/05/242867, final draft still under review).
To run the MLE code and "Bayesian with memory" code, see Bayesian folder (and read its README).
To train Recurrent Neural Networks, you need to run ratcvfit.py (located in the 1D and 2D folders). How to use this Python script is exemplified in window_scan.sh.
All figures from the article are included as .png images, but can also be generated anew by running the following notebooks:
plots/article_plots.ipynb Figures 1 and 3
2D/results.ipynb more figures
2D/gradients.ipynb figures relating to gradients
2D/Activity_tSNE.ipynb figures from SI about using T-sne