diff --git a/programming/exercises/STA/p-unit_stimulus.mat b/programming/exercises/STA/p-unit_stimulus.mat
index 1e88d5e..b6d0b25 100644
Binary files a/programming/exercises/STA/p-unit_stimulus.mat and b/programming/exercises/STA/p-unit_stimulus.mat differ
diff --git a/programming/exercises/STA/sta.m b/programming/exercises/STA/sta.m
index 50df6ff..0b4e3cb 100644
--- a/programming/exercises/STA/sta.m
+++ b/programming/exercises/STA/sta.m
@@ -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);
\ No newline at end of file
diff --git a/programming/exercises/STA/sta_script.m b/programming/exercises/STA/sta_script.m
index a5b6799..432f971 100644
--- a/programming/exercises/STA/sta_script.m
+++ b/programming/exercises/STA/sta_script.m
@@ -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);
+
+