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README.md

Code for Task Engagement Prediction from CSD profiles

The following files contain the code used for CSD estimation from the LFPs from Allen Brain Observatory and the task engagement prediction using Linear Discriminant Analysis.

Jupyter Notebooks

Stim_to_LFP.ipynb

This notebook contains the first steps for Allen data obtention. It saves the desired sessions and the LFPs, and aligns them to the novel stimuli presentations.

get_cortical_layers.ipynb

This notebook upgrades the channel dataframes from the previous notebook by adding the labels on Cortical Layers using the Common Coordinates Framework from Allen Brain Map.

get_CSD.ipynb

This notebook computes the Current Source Densities for all the LFP data

get_power_spectra.ipynb

The same as get_CSD.ipynb but for the obtention of Power Spectra

CSD_Analysis.ipynb

This notebook performs the data discretization and prepares it for task engagement prediction with LDA

Spectral_Analysis.ipynb

The same as get_CSD.ipynb but from Power Spectra

Python Scripts

LFP_functions.py

This script contains several functions used in the notebooks (LFP data alignment, Energy Normalization, CSD estimation, etc.)

datacite.yml
Title Layer and frequency-specific task engagement in primary visual cortex
Authors Ferro,Demetrio;Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona (Spain), 08003;;ORCID:0000-0003-4969-1415
Boncompte Carré,Pau;Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona (Spain), 08003
Moreno Bote,Rubén;Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona (Spain), 08003;;ORCID:0000-0003-0398-014X
Description This resource contains software for the analysis of local field potential signals recorded in primary visual cortex and publicly availabe by Allen Brain Insitute (Visual Behavior Neuropixels). The data are recorded by using multi-contact laminar probes, hence span through cortical depths, during image change detection task.
License Creative Commons Attribution-NonCommercial-ShareAlike-4.0 International (https://creativecommons.org/licenses/by-nc-sa/4.0/)
References Morrison, Christopher. Allen Institute - Visual Behavior - Neuropixels. DANDI Archive. [doi:10.48324/DANDI.000713/0.240702.1725] (IsSupplementTo)
Potworowski, J., Jakuczun, W., Łęski, S. & Wójcik, D. (2012) Kernel current source density method. Neural Comput 24(2), 541-575. [doi:10.1162/neco_a_00236] (IsSupplementTo)
Pettersen, K. H., Devor, A., Ulbert, I., Dale, A. M. & Einevoll, G. T. (2006) Current-source density estimation based on inversion of electrostatic forward solution: effects of finite extent of neuronal activity and conductivity discontinuities. J Neurosci Methods 154(1-2), 116-133. [doi:10.1016/j.jneumeth.2005.12.005] (IsSupplementTo)
Funding
Keywords Visual Neuroscience
Task Engagement
Laminar Layers
Resource Type Software