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Generate a matrix of predictors corresponding to different behavioral variables and process a regression-based encoding model to obtain the relative contributions of the different behavioral variables to the activity of each neuron

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Ben Engelhard, Princeton University (2019).

This package is provided free without any warranty; you can redistribute it and/or modify it under the terms of the GNU General Public License version 3 as published by the Free Software Foundation. If this code is used, please cite: B Engelhard et al. Specialized coding of sensory, motor, and cognitive variables in VTA dopamine neurons. Nature, 2019.

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Encoding Model

This package preprocesses joint behavioral and neuronal data and then processes it through an encoding model in order to obtain a quantitative measure of the contributions of the behavioral variables to the activity of single neurons, as described in the following paper: B Engelhard et al. Specialized coding of sensory, motor, and cognitive variables in midbrain dopamine neurons. Nature, 2019. Please see the paper for details on the encoding model.

Function list: make_predictor_matrix_generalcase.m process_encoding_model.m find_non_empty_cells.m get_CV_R2.m

Data files list: spline_basis30_int.mat

Instructions: First, the data has to be formatted to be used in the make_predictor_matrix_generalcase function. See the function header for specific details. After the predictor matrix is generated, it can be directly processed with the process_encoding_model function. In order to obtain a measure of significance for the relationship between behavioral variables and the neural activity, the obtained F-statistic for each behavioral variable should then be compared to a distribution of F-statistics obtained from a (reasonably large) number of shuffled data instantiations (i.e. the neural activity shuffled but with the same matrix of predictors).

Notes: Currently, event variables are convolved with a basis set composed of 7 splines and 30 timepoints. If you wish to change this, you may use the following package to generate a different spline basis set: Ramsay JO (2014). fdaM: Functional Data Analysis. MATLAB package, URL http://www. psych.mcgill.ca/misc/fda/downloads/FDAfuns/Matlab/. The generated spline basis set should be named spline_basis and saved in a matfile named 'spline_basis30_int.mat'.

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Generate a matrix of predictors corresponding to different behavioral variables and process a regression-based encoding model to obtain the relative contributions of the different behavioral variables to the activity of each neuron

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