Paper and code for High-level cognition during story listening is reflected in high-order dynamic correlations in neural activity patterns
This repository contains data and code used to produce the paper High-level cognition during story listening is reflected in high-order dynamic correlations in neural activity patterns by Lucy L.W. Owen, Thomas H. Chang, and Jeremy R. Manning. You may also be interested in our timecorr Python toolbox for calculating high-order dynamic correlations in timeseries data; the methods implemented in our timecorr toolbox feature prominently in our paper.
This repository is organized as follows:
root
└── code : all code used in the paper
├── notebooks : jupyter notebooks for paper analyses and instructions for downloading the data
└── scripts : python scripts used to run analyses on a computing cluster
└── figs : pdf and png copies of figures
└── data : create this folder by extracting the following zip archive into your clone of this repository's folder: https://drive.google.com/file/d/1CZYe8eyAkZFuLqfwwlKoeijgkjdW6vFs
└── paper : all files to generate paper
└── figs : pdf copies of each figure
Content of the data folder is provided here.
We also include a Dockerfile to reproduce our computational environment. Instruction for use are below:
docker build -t timecorr_paper .
/mnt
within the container, so that location is shared between your host OS and the container. The command will also share port 9999
with your host computer so any jupyter notebooks launched from within the container will be accessible in your web browser.docker run -it -p 9999:9999 --name Timecorr_paper -v $PWD:/mnt timecorr_paper
root@
prefix in your terminal, if so you’ve successfully created a container and are running a shell from inside!jupyter notebook
docker start --attach Timecorr_paper
root@
prefix, you’re inside the containerctrl + c
ctrl + d
or exit
from the same terminal window you used to launch the container, or docker stop Timecorr_paper
from any other terminal window