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This commit is contained in:
Jan Grewe 2014-10-30 17:46:29 +01:00
parent 40c847e694
commit 730e819abe
3 changed files with 21 additions and 16 deletions
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BIN
programming/exercises/STA/p-unit_stimulus.mat
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6
programming/exercises/STA/sta.m
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@ -1,11 +1,11 @@
function [sta, std_sta, valid_spikes]= sta(stimulus, spike_times, count, sampling_rate)
function [st_avg, std_sta, valid_spikes]= sta(stimulus, spike_times, count, sampling_rate)
snippets = zeros(numel(spike_times), 2*count);
valid_spikes = 1;
for i = 1:numel(spike_times)
t = spike_times(i);
index = round(t*sampling_rate);
if index < count || (index + count) > length(stimulus)
if index <= count || (index + count) > length(stimulus)
continue
end
snippets(valid_spikes,:) = stimulus(index-count:index+count-1);
@ -14,5 +14,5 @@ end
snippets(end-(end-valid_spikes):end,:) = [];
sta = mean(snippets, 1);
st_avg = mean(snippets, 1);
std_sta = std(snippets,[],1);

31
programming/exercises/STA/sta_script.m
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@ -12,24 +12,24 @@ for i = 1:length(spike_times)
all_times = cat(1, all_times, spike_times{i});
end
[sta, sta_sd, num] = sta(stimulus_strong(:,2), all_times, 1000, sample_rate);
[st_average, sta_sd, num] = sta(stimulus(:,2), all_times, 1000, sample_rate);
fig = figure();
set(fig, 'PaperUnits', 'centimeters');
set(fig, 'PaperSize', [11.7 9.0]);
set(fig, 'PaperPosition',[0.0 0.0 11.7 9.0]);
set(fig,'Color', 'white')
plot(((1:length(sta))-1000)/sample_rate, sta)
plot(((1:length(st_average))-1000)/sample_rate, st_average)
xlabel('time [s]')
ylabel('stimulus')
%% reverse reconstruction
% make binary representation of the spike times
binary_spikes = zeros(size(stimulus_strong, 1), length(spike_times));
binary_spikes = zeros(size(stimulus, 1), length(spike_times));
estimated_stims = zeros(size(binary_spikes));
for i = 1:length(spike_times)
binary_spikes(round(spike_times{i}*sample_rate), i) = 1;
estimated_stims(:,i) = conv(binary_spikes(:,i), sta, 'same');
estimated_stims(:,i) = conv(binary_spikes(:,i), st_average, 'same');
end
fig = figure();
@ -37,9 +37,9 @@ set(fig, 'PaperUnits', 'centimeters');
set(fig, 'PaperSize', [11.7 9.0]);
set(fig, 'PaperPosition',[0.0 0.0 11.7 9.0]);
set(fig,'Color', 'white')
plot(stimulus_strong(:,1), stimulus_strong(:,2), 'displayname','original')
plot(stimulus(:,1), stimulus(:,2), 'displayname','original')
hold on
plot(stimulus_strong(:,1), mean(estimated_stims,2), 'r', 'displayname', 'reconstruction')
plot(stimulus(:,1), mean(estimated_stims,2), 'r', 'displayname', 'reconstruction')
xlabel('time [s]')
ylabel('stimulus')
legend show
@ -49,14 +49,19 @@ legend show
% we need to downsample the data otherwise the covariance matrixs gets too
% large 20Khz to 1kHz
downsampled_binary = zeros(size(stimulus_strong, 1)/20, length(spike_times));
downsampled_stim = zeros(size(downsampled_binary,1),1);
% downsampled_binary = zeros(size(stimulus, 1)/20, length(spike_times));
downsampled_stim = zeros(size(stimulus,1)/20,1);
for i = 1:length(spike_times)
binary_spikes(round(spike_times{i}*1000), i) = 1;
end
% for i = 1:length(spike_times)
% indices = round(spike_times{i}.*1000);
% indices(indices < 1) = [];
% downsampled_binary(indices, i) = 1;
% end
for i = 1:length(downsampled_stim)
start_index = (i-1) * 1000 + 1;
downsampled_stim(i) = mean(stimulus_strong()*20))
start_index = (i-1) * 20 + 1;
downsampled_stim(i) = mean(stimulus(start_index:start_index+19,2));
end
[st_average, ~, ~] = sta(downsampled_stim, all_times, 50, 1000);