68 lines
1.8 KiB
Matlab
68 lines
1.8 KiB
Matlab
clear
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clc
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%% load data
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load p-unit_spike_times.mat
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load p-unit_stimulus.mat
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sample_rate = 20000;
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%% calculate STA
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all_times = [];
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for i = 1:length(spike_times)
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all_times = cat(1, all_times, spike_times{i});
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end
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[st_average, sta_sd, num] = sta(stimulus(:,2), all_times, 1000, sample_rate);
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fig = figure();
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set(fig, 'PaperUnits', 'centimeters');
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set(fig, 'PaperSize', [11.7 9.0]);
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set(fig, 'PaperPosition',[0.0 0.0 11.7 9.0]);
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set(fig,'Color', 'white')
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plot(((1:length(st_average))-1000)/sample_rate, st_average)
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xlabel('time [s]')
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ylabel('stimulus')
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%% reverse reconstruction
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% make binary representation of the spike times
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binary_spikes = zeros(size(stimulus, 1), length(spike_times));
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estimated_stims = zeros(size(binary_spikes));
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for i = 1:length(spike_times)
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binary_spikes(round(spike_times{i}*sample_rate), i) = 1;
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estimated_stims(:,i) = conv(binary_spikes(:,i), st_average, 'same');
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end
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fig = figure();
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set(fig, 'PaperUnits', 'centimeters');
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set(fig, 'PaperSize', [11.7 9.0]);
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set(fig, 'PaperPosition',[0.0 0.0 11.7 9.0]);
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set(fig,'Color', 'white')
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plot(stimulus(:,1), stimulus(:,2), 'displayname','original')
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hold on
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plot(stimulus(:,1), mean(estimated_stims,2), 'r', 'displayname', 'reconstruction')
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xlabel('time [s]')
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ylabel('stimulus')
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legend show
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%% calculate STC
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% we need to downsample the data otherwise the covariance matrixs gets too
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% large 20Khz to 1kHz
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% downsampled_binary = zeros(size(stimulus, 1)/20, length(spike_times));
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downsampled_stim = zeros(size(stimulus,1)/20,1);
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% for i = 1:length(spike_times)
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% indices = round(spike_times{i}.*1000);
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% indices(indices < 1) = [];
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% downsampled_binary(indices, i) = 1;
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% end
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for i = 1:length(downsampled_stim)
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start_index = (i-1) * 20 + 1;
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downsampled_stim(i) = mean(stimulus(start_index:start_index+19,2));
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end
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[st_average, ~, ~] = sta(downsampled_stim, all_times, 50, 1000);
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