This code is used to generate the images that comprise the Figure 7 of the paper Dura-Bernal et al 2023 "Multiscale model of primary motor cortex circuits predicts in vivo cell type-specific, behavioral state-dependent dynamics" (Cell Reports). It is developed in Python using the NetPyNE package and the NEURON simulator
The code provided includes all the scripts and the processed Spike Histogram dataset used to make the figures.
The processed dataset is provided for file size reasons, but if you wish to download the original files and re-build the analysis, you should add the required files in the ./data folder and run the code using these flags set to True in the runAnalysis.py file:
updateConnectivity = TrueupdateHistDict = TrueThe updateConnectivity flag will build connectivity .pkl files for each cell in the folder ./data/conn_info, named as PopName_CellGID (e.g. PT5B_5132.pkl). It contains the individual connections for each cell, which are loaded to buid the Spike Histograms.
The updateHistDict will generate the Spike Histogram for each population, which is already provided in the repository as the processed dataset.
The data will be saved in the ./figs/histogram_figures/windowAnalysis//hist_window__ms/9_post_analysis folder.
We also provide the code for other analysis and figures developed during the study, which were omitted in the publication for simplicity.
The code requires the NetPyNE package and the NEURON simulator, along with common data processing and plotting packages, such as matplotlib and numpy.
A full list of required packages can be found in the headed or the runAnalysis.py and AnalyzeData.py files.
It also requires scikit-learn and umap for multivariate analysis.
The project requires no installation other than the necessary packages.
Joao Moreira - ResearchGate - LinkedIn - joao.moreira@downstate.edu
Salvador Dura-Bernal - salvador.dura-bernal@downstate.edu
Project Link: M1_NetPyNE_CellReports_2023
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