create_neuron_trains.m

% This file indiscriminately concatenates all trials for a single neuron.
% The result is a 16-cell array with only 1 concatenated train for each
% neuron. The trials for each neuron are sorted by increasing TNR
% intensity. In particular, for any neuron, there is 1 spike train, where
% the earliest times are from trials with TNR intensity 0 and the latest
% times are from trials with TNR intensity 85 (which is the highest intensity).

% Load sorted trains
% sorted_trains = load('sorted_trains.mat');
% sorted_trains = sorted_trains.sorted_trains;
% load('sorted_stimuli.mat');
% 
% num_neurons = 16;
% num_tnrs = 7;
% 
% % Preallocate a 16-cell array with 1 train for each neuron
% neuron_trains = cell(16,1);
% feature_intensity = cell(16,1);
% 
% % For each neuron...
% for i = 1:num_neurons
%     % Create empty vector for concatenated train
%     A = [];
%     % For each TNR intensity...
%     for j = 1:num_tnrs
%         % Concatenate the train associated with that intensity
%         A = [A sorted_trains{i,j}];
%     end
%     neuron_trains{i,1} = A;
%     B = [];
%     for j=1:num_tnrs
%         B = [B sorted_stimuli{i,j}];
%     end
%     feature_intensity{i,1} = B;
%     % Print i
%     i
% end

load('spike_trains.mat');
load('filtered_stimulus.mat');
spike_trains = spike_array;
% Remove first column (TNR intensity)
spike_trains(:,:,1) = [];

% All bins with spikes become +1
spike_trains(spike_trains > 0) = 1;
% All bins with spikes become -1
spike_trains(spike_trains == 0) = -1;
neuron_trains = reshape(permute(spike_trains, [1 3 2]), size(spike_trains, 1), []);
feature_intensity = reshape(permute(filtered_stimulus, [1 3 2]), size(filtered_stimulus, 1), []);

save('neuron_trains.mat', 'neuron_trains');
save('feature_intensity.mat', 'feature_intensity');