A spatial code for temporal information is necessary for efficient sensory learning

Here we provide a large dataset of neuronal responses in the mouse auditory system to a range of simple sounds. This data is initially published in the paper Bagur, Lebourg et al

The "Data" files are organized as follows : 

Data_XX.mat : one file with the responses all ROIs/units to all 140 sounds for each area. AC data was uploaded in two files with two halves of the neural data set due to file size limitations

Clusters_XX.mat : one file with the responses of the clustered ROIS from calcium imaging data go all 140 sounds

AnatInfo_AC/ICE.mat : localisation of each ROI from AC and ICE data

Correlation_Decoding.mat : the results of core analysis from the paper, to accelerate plotting using the "BasicCorrelationDecodingAnalysis.mlx" code

The "Codes" folder contains matlab code showing how to access the data and illustrating how it is organized as well as code to perform basic population level analysis from Bagur, Lebourg et al, illustrating how to calculate noise-free correlation between popultion vector

"Packages" are open source code from github written by other members of the scientific community and saved here as a reference :

AutoCell : https://github.com/thomasdeneux/Autocell

CortexLab suite : https://github.com/cortex-l

Phy : https://github.com/cortex-lab/phy

Network from Kell et al : https://github.com/mcdermottLab/kelletal2018