update of solutions for sta
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@ -12,7 +12,7 @@ for i = 1:numel(spike_times)
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valid_spikes = valid_spikes + 1;
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end
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snippets(end-(end-valid_spikes):end,:) = [];
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snippets(valid_spikes:end,:) = [];
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st_avg = mean(snippets, 1);
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std_sta = std(snippets,[],1);
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@ -25,11 +25,11 @@ 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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estimated_stims = zeros(size(stimulus, 1), length(spike_times));
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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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binary_spikes = zeros(size(stimulus, 1));
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binary_spikes(round(spike_times{i}*sample_rate)) = 1;
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estimated_stims(:,i) = conv(binary_spikes, st_average, 'same');
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end
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fig = figure();
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@ -44,24 +44,3 @@ 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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