Data set for "Distributed and specific encoding of sensory, motor and decision information in the mouse neocortex during goal-directed behavior"

Data set for: Oryshchuk A, Sourmpis C, Weverbergh J, Asri R, Esmaeili V, Modirshanechi A, Gerstner W, Petersen CCH, Crochet S (2024) Distributed and specific encoding of sensory, motor and decision information in the mouse neocortex during goal-directed behavior. Cell Reports 43: 113618. https://doi.org/10.1016/j.celrep.2023.113618

There are 2 files in this upload:

1. The file named "2024_Oryshchuk_CellReports.pdf" is the Open Access pdf of the online publication in Cell Reports.

2. The file named " Oryshchuk _data_code.zip" (~1.8 GB) is a zipped version of a folder "Oryshchuk _data_code" (~2.3 GB), which contains the preprocessed data analyzed in the study along with the Matlab and Python codes used to generate the published figures. To access the data and codes, first unzip the file.

·       The subfolder “Atlas” contains templates from the Allen Mouse Brain Reference Altas of anatomical brain sections used to map the location of the silicon probes (Supplementary Figure S1).

·       The subfolder “Clustering-master” contains the Matlab codes used for the clustering on neuronal activity (Figure 1). The output is the data structure ‘Data_Clustering.mat’ file already provided in the folder ‘Data’.

·       The subfolder “Code” contains the main Matlab codes used to analyze the data and plot the figures. The ouput from the clustering and decoding analyses are provided in the ‘Data’ folder, thus the Matlab codes can be run independently, without running the ‘clustering’ or ‘decoding’ codes first.

·       The subfolder “Data” contains the Matlab  data structures containing the electrophysiological and behavioral data from whisker rewarded (‘DataWR.mat’) and non-rewarded (‘DataWnonR.mat’) mice, the behavioral data for optogenetic inactivation in rewarded mice, the clustering results (‘Data_Clustering.mat’) and a subfolder containing the results from the decoding analyses (“Decoding”).

·       The subfolder “decoding” contains the Python codes used for the decoding analyses. The required configuration can be found in the file ‘requirements.txt’. To run the codes, follow instructions from the ‘README.md’ file.

·       The subfolder “Figures” will be populated with figures saved in .png and .eps formats as well as a ‘Methods.txt’ files when running the main Matlab codes.

·       The subfolder “Functions” contains subfunctions used by the main Matlab codes to analyze the data and plot the figures.

·       The subfolder “Results” will be populated with Matlab data structures as well as a ‘.xlsx’ files when running the main Matlab codes.

When running the code, you need to set the Matlab file path to be "Oryshchuk _data_code". In addition, you should add the folder "Oryshchuk_data_code" with subfolders to the Matlab path. Some parts of the code rely upon previous results, and need to be executed sequentially in the order of the figure panels in the journal publication. Please note that some of the code can take several hours to execute.