Readme for Insanally 2017

This repository contains the code used for analysis in Insanally, et al 2019, "Spike-timing-dependent ensemble encoding by non-classically responsive cortical neurons". eLife, 2019. http://dx.doi.org/10.7554/eLife.42409

Requirements

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)

Files and Directories

• 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.