项目作者: ContextLab

项目描述 :
Paper and code for High-level cognition during story listening is reflected in high-order dynamic correlations in neural activity patterns
高级语言: Jupyter Notebook
项目地址: git://github.com/ContextLab/timecorr-paper.git
创建时间: 2018-10-15T19:21:31Z
项目社区:https://github.com/ContextLab/timecorr-paper

开源协议:MIT License

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What is the neural code?

DOI

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:

  1. root
  2. └── code : all code used in the paper
  3. ├── notebooks : jupyter notebooks for paper analyses and instructions for downloading the data
  4. └── scripts : python scripts used to run analyses on a computing cluster
  5. └── figs : pdf and png copies of figures
  6. └── 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
  7. └── paper : all files to generate paper
  8. └── 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 setup

  1. Install Docker on your computer using the appropriate guide below:
  2. Launch Docker and adjust the preferences to allocate sufficient resources (e.g. > 4GB RAM)
  3. Build the docker image by opening a terminal in this repo folder and enter docker build -t timecorr_paper .
  4. Use the image to create a new container
    • The command below will create a new container that will map your local copy of the repository to /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
    • You should now see the root@ prefix in your terminal, if so you’ve successfully created a container and are running a shell from inside!
  5. To launch any of the notebooks: jupyter notebook

Using the container after setup

  1. You can always fire up the container by typing the following into a terminal
    • docker start --attach Timecorr_paper
    • When you see the root@ prefix, you’re inside the container
  2. Stop a running jupyter notebook server with ctrl + c
  3. Close a running container with 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
sim_heatmaps_features_1648323243757.pdf
sim_heatmaps_time_1648323243830.pdf
stats_heatmaps_1648323243949.pdf
supp_15_intact_1648323244229.pdf
supp_15_paragraph_1648323244294.pdf
supp_15_rest_1648323244363.pdf
supp_15_word_1648323244445.pdf
synthetic_data_1648323244517.pdf
timecorr_pipeline_1648323244745.pdf
main_1648323244917.pdf
cover_letter_1648323245682.pdf
cover_letter_and_reviewer_responses_rev2_1648323245741.pdf
nat_comms_response_letter_1648323245899.pdf
requested_changes_1648323245977.pdf
supplementary_materials_1648323246127.pdf
isfc_eigenvector_centrality_gaussian_50_line_level_analysis_optimized_param_search_1648323239206.pdf
isfc_eigenvector_centrality_gaussian_5_line_level_analysis_optimized_param_search_1648323239245.pdf
pca_700_nodes_level_0_1648323239367.pdf
pca_700_nodes_level_1_1648323239419.pdf
pca_700_nodes_level_1_PCA_1648323239459.pdf
pca_700_nodes_level_1_eigenvector_centrality_1648323239533.pdf
pca_700_nodes_level_2_1648323239599.pdf
pca_700_nodes_level_2_PCA_1648323239667.pdf
pca_700_nodes_level_2_eigenvector_centrality_1648323239734.pdf
pca_700_nodes_level_3_1648323239780.pdf
pca_700_nodes_level_3_PCA_1648323239844.pdf
pca_700_nodes_level_3_eigenvector_centrality_1648323239955.pdf
pca_700_nodes_level_4_1648323240003.pdf
pca_700_nodes_level_4_PCA_1648323240031.pdf
pca_700_nodes_level_4_eigenvector_centrality_1648323240092.pdf
pca_700_nodes_level_Legend_1648323240131.pdf
ramping_recovery_averaged_1648323240252.pdf
random_recovery_averaged_1648323240393.pdf
sim_heatmaps_features_1648323240496.pdf
sim_heatmaps_time_1648323240559.pdf
stats_heatmaps_PCA_1648323240742.pdf
stats_heatmaps_eigenvector_1648323240800.pdf
diff_1648323241244.pdf
decode_level_1648323242114.pdf
decode_levels_kernels_1648323242205.pdf
decode_levels_widths_1648323242367.pdf
discussion_1648323242530.pdf
higher_order_sims_1648323242626.pdf
kernels_1648323242715.pdf
methods_fig_1648323242918.pdf
most_abs_1648323243094.pdf
patterns_1648323243278.pdf
pca_1648323243539.pdf
Delta_1648323233543.pdf
Delta_example_1648323233605.pdf
Gaussian_1648323233651.pdf
Gaussian_example_1648323233724.pdf
Laplace_1648323233757.pdf
Laplace_example_1648323233853.pdf
MexicanHat_1648323234019.pdf
Mexican_hat_example_1648323234185.pdf
PCA_norm_ave_level_analysis_optimized_param_search_1648323234254.pdf
PCA_norm_weights_ave_level_analysis_optimized_param_search_1648323234344.pdf
PCA_rel_ave_kernel_gaussian_1648323234542.pdf
PCA_rel_ave_kernel_hat_1648323234645.pdf
PCA_rel_ave_kernel_laplace_1648323234744.pdf
PCA_rel_ave_level_analysis_optimized_param_search_1648323234828.pdf
PCA_rel_ave_width_10_1648323234860.pdf
PCA_rel_ave_width_20_1648323234956.pdf
PCA_rel_ave_width_5_1648323234990.pdf
PCA_rel_ave_width_50_1648323235084.pdf
PCA_weights_ave_level_analysis_optimized_param_search_1648323235123.pdf
PCA_z_ave_level_analysis_optimized_param_search_1648323235188.pdf
Uniform_1648323235282.pdf
block_recovery_averaged_1648323235395.pdf
constant_recovery_averaged_1648323235566.pdf
eigenvector_norm_ave_level_analysis_optimized_param_search_1648323235811.pdf
eigenvector_norm_weights_ave_level_analysis_optimized_param_search_1648323235856.pdf
eigenvector_rel_ave_kernel_gaussian_1648323235934.pdf
eigenvector_rel_ave_kernel_hat_1648323236009.pdf
eigenvector_rel_ave_kernel_laplace_1648323236054.pdf
eigenvector_rel_ave_level_analysis_optimized_param_search_1648323236111.pdf
eigenvector_rel_ave_width_10_1648323236179.pdf
eigenvector_rel_ave_width_20_1648323236217.pdf
eigenvector_rel_ave_width_5_1648323236289.pdf
eigenvector_rel_ave_width_50_1648323236345.pdf
eigenvector_weights_ave_level_analysis_optimized_param_search_1648323236412.pdf
eigenvector_z_ave_level_analysis_optimized_param_search_1648323236492.pdf
high_order_sim_block_1648323236526.pdf
high_order_sim_constant_1648323236628.pdf
high_order_sim_ramping_1648323236702.pdf
high_order_sim_random_1648323236796.pdf
high_order_simblock_1648323236871.pdf
high_order_simconstant_1648323237036.pdf
high_order_simramping_1648323237106.pdf
high_order_simrandom_1648323237206.pdf
isfc_PCA_gaussian_10_line_level_analysis_optimized_1648323237283.pdf
isfc_PCA_gaussian_10_normalized_line_level_analysis_optimized_1648323237317.pdf
isfc_PCA_gaussian_10_relative_line_level_analysis_optimized_1648323237350.pdf
isfc_PCA_gaussian_20_line_level_analysis_optimized_1648323237416.pdf
isfc_PCA_gaussian_20_normalized_line_level_analysis_optimized_1648323237516.pdf
isfc_PCA_gaussian_20_relative_line_level_analysis_optimized_1648323237604.pdf
isfc_PCA_gaussian_50_line_level_analysis_optimized_1648323237706.pdf
isfc_PCA_gaussian_5_line_level_analysis_optimized_1648323237795.pdf
isfc_PCA_gaussian_5_normalized_line_level_analysis_optimized_1648323237873.pdf