Library for fitting neural encoding models
Imagine we measures the response of a neuron to several simultaneous stimulus and behavioral variables. This toolbox fits a family of cascade models with both linear and nonlinear stages, including NL, LN, and NLN, using maximum likelihood.
The fitting routines require an input structure d, which contains at the minimum, the following fields:
t - time points per trial
k - number of trials (can be 1 if no trial structure)
u - upsampling factor for stimulus
c - number of stimuli (or behavioral variables)
samp - temporal sampling of stimulus / response ('same" or 'up')
S_ctk - matrix of stimuli / behaviors x time points x trials
R_ntk - matrix neurons x time points x trials
Fits are generated by calling:
fit = fitInit(d,'NL','loglik',20);
d = prepareStim(d,fit);
[train test] = prepareRoi(d,fit,1);
fit = fitDo(train,test,fit);
incorporate an output nonlinearity (fitG)