glm_part2.m

% script glm_part2.m
% MATLAB script to constuct and visualize GLM models of spiking against 
% other covariates individually.  
% The script is initialized to visualize the raw data in the form of 
% occupancy normalized histograms.  Use this to constuct GLM models 
% and compare them to the histograms 

% load the rat trajectory and spiking data;
load('glm_data.mat');

% Find spike bin index for occupancy normalized histograms
ind = [];
for numspikes = 1:max(spikes_binned),
    ind = [ind; find(spikes_binned >= numspikes)];
end;

figure;
warning('off');
set(gcf,'Name','Occupancy Normalized Histograms');

% Histogram of spiking to x-velocity
subplot(2,2,1);
    velocities = -.04:.001:.04;
    bar(velocities,hist(vxN(ind),velocities)./hist(vxN,velocities));
    ylabel('normalized spike counts');
    xlabel('x velocity');
%    b=glmfit([ ??? ],spikes_binned,'poisson');
%    hold on;
%    plot(velocities,exp(???),'r');

% Histogram of spiking to y-velocity 
subplot(2,2,2);
    bar(velocities,hist(vyN(ind),velocities)./hist(vyN,velocities));
    xlabel('y velocity');
%    b=glmfit([ ??? ],spikes_binned,'poisson');
%    hold on;
%    plot(velocities,exp(???),'r');

% Histogram of spiking to movement speed 
subplot(2,2,3);
    rs = 0:.001:.04;
    bar(rs,hist(r(ind),rs)./hist(r,rs));
    xlabel('speed');
%    b=glmfit([ ??? ],spikes_binned,'poisson');    
%    hold on;
%    plot(rs,exp(???),'r');

% Histogram of spiking to movement direction
subplot(2,2,4);
    phis = -pi:.1:pi;
    bar(phis,hist(phi(ind),phis)./hist(phi,phis));
    xlabel('direction');
%    b=glmfit([ ??? ],spikes_binned,'poisson');    
%    hold on;
%    plot(phis,exp(???),'r');