This repository contains the code used for analysis in Insanally, et al 2017.

All packages and scripts used to analyze our data were custom written in python (2.7.13) using jupyter (1.0.0) and ipython (5.3.0). Parallelization for multi-core processors was accomplished using ipyparallel (6.0.2). The RNN script is written in matlab

Additional packages required are:

• numpy (1.13.1)
• scipy (0.19.1)
• matplotlib (2.0.2)
• h5py (2.7.0)
• scikit-learn (0.19.0)
• statsmodels (0.8.0)

• Readme.md: This file
• data/: Directory containing two examples each from ACtx and FR2, one responsive and one non-responsive.
• animal_info.py: Python file containing a dictionary ANIMALS with relelvant infomation about each recording session needed to load the data.
• bayseian_neural_decoding/: this python packge contains the analysis tools for all decoders used in the papers.
• MI_beh_plots.py: Python module containing the plotting functions used to generate all figures.
• Defining non-responsiveness.ipynb: Jupyter notebook containing the scripts used to identify non-responsive cells.
• Calculating cell firing statistics and receptive field.ipynb: Jupyter notebook containing the scripts used to calucalte all cell firing statistics.
• Decoding responses.ipynb: Script for decoding all recording sessions referenced in animal_info.py.