This software computes mutual information between stimuli and neural response both after discounting the state variability and without considering the state variability. This software also provides a simulation of Poisson neurons that responds to stimuli and that has an additive state dependent component. This simulation may be useful to users to try out the software and test some ideas. These methods developed and used in papers:
H. Safaai, R. Neves, O. Eschenko, N. K. Logothetis, Stefano Panzeri, A dynamical model of the effect of Locus Coeruleus firing on single-trial cortical state dynamics and sensory, PNAS 2015, ; published ahead of print September 28, 2015, doi:10.1073/pnas.1516539112
Any person downloading this software accepts to acknowledge this study by citing it in every publication or report arising from the use of this material.
The folder contains the following set of functions to compute the information of the discounted trial-to-trial variability.
STATE_INFORMATION.m: The function computes the following three information values
Information between the stimulus and response I(S,R)
Information between the stimulus and discounted variability response I(S;R’) where the trial-to-trial variability V is subtracted from the response as R’=R-V.
The joint information between the stimulus and the response and state I(S;R,theta)
State_Variability_Simulator.m: The functions simulates responses of a poisson process with the trial-to-trial variability which is a linearly related to the phase of the ongoing activity at the moment of stimulus onset. The function first simulates the data, then estimates the trial-to-trial variability by fitting the data to the linearly additive state model and then apply the fit to a cross-validated test set. This function uses the functions lint.m and fit_lintr.m functions to fit the linear additive state model to the data.
Main.m: This is the main file which can be used to either simulate the trial-to-trial variability or use the DATA.mat file as the input of the STATE_INFORMATION.m function. The output will be a bar plot of different information values.