Add files via upload

.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

get_peaks.asv

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+%after saving the data with new_data_set.m, we isolate the peaks for the
+%new analysis of the refined data
+
+newdatadir = 'C:\Users\maier\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\';
+channelfilename = [newdatadir 'refined_dataset']; 
+data_file = load(channelfilename);
+
+%exclude 160517, (first unit, left empty, it is a K neuron)
+%Reject 180806 p1 uclust17, M cell, as doesn't seem well triggered (46)
+%Reject 181207 (B) uclust22, M cell, as doesn't seem well triggered (55)
+ layer = {'K','M','P','K','K','K','M','P','P','','M','M','','','M','','','P','','M','','M','M','','P','M','','P', ...
+'P','','','K','P','M','M','M','P','','P','K','P','P','','P','P','M','','P','M','P','M','P','','P','M','M','P','','M','M','P','M', ...
+'','','M','M','M','P','M','M','M','M','P','P'};
+layer([1,46,55]) = [];
+ f = {'DE0_NDE50','DE50_NDE0','DE50_NDE50'};
+
+ %% find peaks in order to analyze them on R and fit a LMER
+
+channum = 1: length(data_file.new_data);
+xabs = -199:1300;
+nyq = 500;
+mean_filtered_dSUA = struct();
+
+mean_blank_power = nan(length(channum),1);
+mean_fourhzpowerhighcontrast = nan(length(channum),1);
+
+all_locsdSUA_filtered = nan(1,length(channum));
+norm_data_peaks = struct();
+
+ for i = channum
+ data = squeeze(data_file.new_data(i).channel_data.hypo{1,2}.cont_su(401:1900,:,:));
+
+ filename = [data_file.new_data(i).channel_data.filename, f{2}];
+ filename = erase(filename, '.mat');
+
+ all_norm_pks = nan(4,length(data(1,:)));
+
+
+   blankcontrast = data_file.new_data(i).channel_data.contrast ==  0 & data_file.new_data(i).channel_data.fixedc ==  0;
+   highcontrast = data_file.new_data(i).channel_data.contrast >=  0.5 & data_file.new_data(i).channel_data.fixedc ==  0; 
+
+    trialidx = 1:length(data_file.new_data(i).channel_data.sdftr_chan(1,:));
+    raw_bs = nan(length(xabs), length(trialidx));
+    filtered_dSUA = nan(length(xabs), length(trialidx));
+    all_norm_lpdSUA= nan(length(xabs),length(trialidx));
+
+
+    powerstim = nan(length(trialidx),1025);
+    freqstim = nan(length(trialidx),1025);
+    fourhzpowerstim =nan(length(trialidx),1);
+    bsl = nan(1, length(trialidx));
+    mean_wnd1 = nan(1,length(trialidx));
+     for tridx = trialidx
+
+    all_data = data_file.new_data(i).channel_data.sdftr_chan(401:1900,tridx);
+
+    bsl(tridx) = mean(all_data(1:200));
+    raw_bs(:,tridx) = all_data(1:end)- bsl(tridx);
+
+    lpc       = 4.5; %low pass cutoff
+    lWn       = lpc/nyq;
+    [bwb,bwa] = butter(4,lWn,'low');
+    lpdSUA      = filtfilt(bwb,bwa, raw_bs(:,tridx));
+
+
+    filtered_dSUA(:,tridx) = lpdSUA;
+    all_norm_lpdSUA(:,tridx) = (lpdSUA - min(lpdSUA))/(max(lpdSUA)- min(lpdSUA));
+    mean_wnd1(tridx) = mean(lpdSUA(231:480));
+
+    %%% power
+
+
+    [powerstim(tridx,:), freqstim(tridx,:)] = calcFFT(all_data(200:1350));
+
+    %find the index of the frequency vector closest to 4hz and point to the
+    %power value of this index for every trial, and store the value in
+    %fourhzpower
+    [val,index] = min(abs(4-freqstim(tridx,:)));
+    fourhzpowerstim(tridx,1) = powerstim(tridx,index);
+
+     end
+   %power related variables
+   power0 = fourhzpowerstim(blankcontrast);
+   power5 = fourhzpowerstim(highcontrast);
+   mean_blank_power(i,1) = mean(power0);
+   mean_fourhzpowerhighcontrast(i,1) = mean(power5);
+
+   %spiking activity related variables
+   mean_wnd1_5 =mean_wnd1(highcontrast);
+   filtered_dSUA_high = filtered_dSUA(:, highcontrast);
+   all_norm_lpdSUA_high = all_norm_lpdSUA(:, highcontrast);
+   %first peak location related variables 
+    sua_bsl =  mean(filtered_dSUA_high(1:200,:),1);
+
+
+  %%%%%%%%%%% %reject trials below the 95%CI in the blank condition %%%%%%
+   for tr = 1:length(power5)
+       if power5(tr) > mean(power0)+1.96*std(power0) && mean_wnd1_5(tr) > mean(sua_bsl) + 1.96*std(sua_bsl)
+
+           filtered_dSUA_high(:,tr) = filtered_dSUA_high(:,tr);
+
+       else
+
+           filtered_dSUA_high(:,tr) = nan(length(filtered_dSUA_high(:,tr)),1);
+       end
+   end
+   filtered_dSUA_high = filtered_dSUA_high(:,~all(isnan(filtered_dSUA_high))); % for nan - cols
+
+
+   all_locsdSUA_trials = nan(6,length(filtered_dSUA_high(1,:)));
+   if length(filtered_dSUA_high(1,:)) >=10
+
+    %determine the first peak location for each trial of a given single
+    %unit
+
+
+    for trial = 1:length(filtered_dSUA_high(1,:))
+
+         for ln = 30:550
+             if filtered_dSUA_high(200+ln,trial) < filtered_dSUA_high(200+ln+1,trial)
+
+
+             locsdSUA_trial = findpeaks(filtered_dSUA_high(200+ln:1350,trial));
+             %store 4 peaks locations 
+             all_locsdSUA_trials(1:length(locsdSUA_trial.loc),trial) = locsdSUA_trial.loc+200+ln;
+             break 
+             end 
+         end
+
+        if length(locsdSUA_trial.loc) >= 4
+             %adjust location to the first data point of lpsu (+len),
+    lpsulocs = locsdSUA_trial.loc(1:4) + 200+ ln;
+    all_norm_pks(:,trial) = all_norm_lpdSUA_high(lpsulocs(1:4), trial);
+
+         end 
+    end
+    elseif length(filtered_dSUA_high(1,:)) <=10  || length(locsdSUA_trial.loc) <= 4
+      all_locsdSUA_trials(:,:) = nan(length(all_locsdSUA_trials(:,1)),);
+      all_norm_pks(:,trial) = nan(length(all_norm_pks(:,1)),1);
+   end
+    namelist(1,1:length(sprintf('chan_%d',i))) = sprintf('chan_%d',i);
+ norm_data_peaks(i).namelist = all_norm_pks(:,~all(isnan(all_norm_pks)));
+channelfilename = [newdatadir filename];
+%save(strcat(channelfilename, '.mat'), 'all_pks');
+ end
+  allfilename = [newdatadir 'all_norm_data_peaks'];
+ save(strcat(allfilename, '.mat'), 'norm_data_peaks');
+
+    %{
+ %all_locs =nan(4,length(channel_data(1,1,:)));
+ for n =1:length(data(1,:))
+   lpc       = 4.5; %low pass cutoff
+   lWn       = lpc/nyq;
+   [bwb,bwa] = butter(4,lWn,'low');
+   lpsu      = filtfilt(bwb,bwa, data(:,n));
+
+   norm_lpsu = (lpsu - min(lpsu))/(max(lpsu)- min(lpsu));
+   %all_lpsu(:,n) = lpsu;
+  % plot(lpsu)
+ if ~all(isnan(norm_lpsu)) 
+  for len = 30:550
+            if norm_lpsu(len) < norm_lpsu(len+1)
+   locs = findpeaks(norm_lpsu(len:1200));
+        break
+            end
+  end
+
+         if length(locs.loc) >= 4
+             %adjust location to the first data point of lpsu (+len),
+    lpsulocs = locs.loc(1:4) + len;
+   all_norm_pks(:,n) = norm_lpsu(lpsulocs(1:4));
+
+         end 
+ end
+% plot(locs.loc +len, lpsu(locs.loc +len))
+ end
+
+ namelist(1,1:length(sprintf('chan_%d',i))) = sprintf('chan_%d',i);
+ norm_data_peaks(i).namelist = all_norm_pks;
+channelfilename = [newdatadir filename];
+%save(strcat(channelfilename, '.mat'), 'all_pks');
+ end
+    %}
+ allfilename = [newdatadir 'all_norm_data_peaks'];
+ save(strcat(allfilename, '.mat'), 'norm_data_peaks');

get_peaks.m

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+%after saving the data with new_data_set.m, we isolate the peaks for the
+%new analysis of the refined data
+
+newdatadir = 'C:\Users\maier\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\';
+channelfilename = [newdatadir 'refined_dataset']; 
+data_file = load(channelfilename);
+
+%exclude 160517, (first unit, left empty, it is a K neuron)
+%Reject 180806 p1 uclust17, M cell, as doesn't seem well triggered (46)
+%Reject 181207 (B) uclust22, M cell, as doesn't seem well triggered (55)
+ layer = {'K','M','P','K','K','K','M','P','P','','M','M','','','M','','','P','','M','','M','M','','P','M','','P', ...
+'P','','','K','P','M','M','M','P','','P','K','P','P','','P','P','M','','P','M','P','M','P','','P','M','M','P','','M','M','P','M', ...
+'','','M','M','M','P','M','M','M','M','P','P'};
+layer([1,46,55]) = [];
+ f = {'DE0_NDE50','DE50_NDE0','DE50_NDE50'};
+
+ %% find peaks in order to analyze them on R and fit a LMER
+
+channum = 1: length(data_file.new_data);
+xabs = -199:1300;
+nyq = 500;
+mean_filtered_dSUA = struct();
+
+mean_blank_power = nan(length(channum),1);
+mean_fourhzpowerhighcontrast = nan(length(channum),1);
+
+all_locsdSUA_filtered = nan(1,length(channum));
+norm_data_peaks = struct();
+
+ for i = channum
+ data = squeeze(data_file.new_data(i).channel_data.hypo{1,2}.cont_su(401:1900,:,:));
+
+ filename = [data_file.new_data(i).channel_data.filename, f{2}];
+ filename = erase(filename, '.mat');
+
+ all_norm_pks = nan(4,length(data(1,:)));
+
+
+   blankcontrast = data_file.new_data(i).channel_data.contrast ==  0 & data_file.new_data(i).channel_data.fixedc ==  0;
+   highcontrast = data_file.new_data(i).channel_data.contrast >=  0.5 & data_file.new_data(i).channel_data.fixedc ==  0; 
+
+    trialidx = 1:length(data_file.new_data(i).channel_data.sdftr_chan(1,:));
+    raw_bs = nan(length(xabs), length(trialidx));
+    filtered_dSUA = nan(length(xabs), length(trialidx));
+    all_norm_lpdSUA= nan(length(xabs),length(trialidx));
+
+
+    powerstim = nan(length(trialidx),1025);
+    freqstim = nan(length(trialidx),1025);
+    fourhzpowerstim =nan(length(trialidx),1);
+    bsl = nan(1, length(trialidx));
+    mean_wnd1 = nan(1,length(trialidx));
+     for tridx = trialidx
+
+    all_data = data_file.new_data(i).channel_data.sdftr_chan(401:1900,tridx);
+
+    bsl(tridx) = mean(all_data(1:200));
+    raw_bs(:,tridx) = all_data(1:end)- bsl(tridx);
+
+    lpc       = 4.5; %low pass cutoff
+    lWn       = lpc/nyq;
+    [bwb,bwa] = butter(4,lWn,'low');
+    lpdSUA      = filtfilt(bwb,bwa, raw_bs(:,tridx));
+
+
+    filtered_dSUA(:,tridx) = lpdSUA;
+    all_norm_lpdSUA(:,tridx) = (lpdSUA - min(lpdSUA))/(max(lpdSUA)- min(lpdSUA));
+    mean_wnd1(tridx) = mean(lpdSUA(231:480));
+
+    %%% power
+
+
+    [powerstim(tridx,:), freqstim(tridx,:)] = calcFFT(all_data(200:1350));
+
+    %find the index of the frequency vector closest to 4hz and point to the
+    %power value of this index for every trial, and store the value in
+    %fourhzpower
+    [val,index] = min(abs(4-freqstim(tridx,:)));
+    fourhzpowerstim(tridx,1) = powerstim(tridx,index);
+
+     end
+   %power related variables
+   power0 = fourhzpowerstim(blankcontrast);
+   power5 = fourhzpowerstim(highcontrast);
+   mean_blank_power(i,1) = mean(power0);
+   mean_fourhzpowerhighcontrast(i,1) = mean(power5);
+
+   %spiking activity related variables
+   mean_wnd1_5 =mean_wnd1(highcontrast);
+   filtered_dSUA_high = filtered_dSUA(:, highcontrast);
+   all_norm_lpdSUA_high = all_norm_lpdSUA(:, highcontrast);
+   %first peak location related variables 
+    sua_bsl =  mean(filtered_dSUA_high(1:200,:),1);
+
+
+  %%%%%%%%%%% %reject trials below the 95%CI in the blank condition %%%%%%
+   for tr = 1:length(power5)
+       if power5(tr) > mean(power0)+1.96*std(power0) && mean_wnd1_5(tr) > mean(sua_bsl) + 1.96*std(sua_bsl)
+
+           filtered_dSUA_high(:,tr) = filtered_dSUA_high(:,tr);
+
+       else
+
+           filtered_dSUA_high(:,tr) = nan(length(filtered_dSUA_high(:,tr)),1);
+       end
+   end
+   filtered_dSUA_high = filtered_dSUA_high(:,~all(isnan(filtered_dSUA_high))); % for nan - cols
+
+
+   all_locsdSUA_trials = nan(6,length(filtered_dSUA_high(1,:)));
+   if length(filtered_dSUA_high(1,:)) >=10
+
+    %determine the first peak location for each trial of a given single
+    %unit
+
+
+    for trial = 1:length(filtered_dSUA_high(1,:))
+
+         for ln = 30:550
+             if filtered_dSUA_high(200+ln,trial) < filtered_dSUA_high(200+ln+1,trial)
+
+
+             locsdSUA_trial = findpeaks(filtered_dSUA_high(200+ln:1350,trial));
+             %store 4 peaks locations 
+             all_locsdSUA_trials(1:length(locsdSUA_trial.loc),trial) = locsdSUA_trial.loc+200+ln;
+             break 
+             end 
+         end
+
+        if length(locsdSUA_trial.loc) >= 4
+             %adjust location to the first data point of lpsu (+len),
+    lpsulocs = locsdSUA_trial.loc(1:4) + 200+ ln;
+    all_norm_pks(:,trial) = all_norm_lpdSUA_high(lpsulocs(1:4), trial);
+
+         end 
+    end
+    elseif length(filtered_dSUA_high(1,:)) <=10  || length(locsdSUA_trial.loc) <= 4
+      all_locsdSUA_trials(:,:) = nan(length(all_locsdSUA_trials(:,1)),length(all_locsdSUA_trials(1,:)));
+      all_norm_pks(:,:) = nan(length(all_norm_pks(:,1)),length(all_norm_pks(1,:)));
+   end
+ namelist(1,1:length(sprintf('chan_%d',i))) = sprintf('chan_%d',i);
+ norm_data_peaks(i).namelist = all_norm_pks(:,~all(isnan(all_norm_pks)));
+ all_norm_pks = all_norm_pks(:,~all(isnan(all_norm_pks)));
+channelfilename = [newdatadir 'su_peaks\' filename];
+save(strcat(channelfilename, '.mat'), 'all_norm_pks');
+ end
+  allfilename = [newdatadir 'all_norm_data_peaks'];
+ save(strcat(allfilename, '.mat'), 'norm_data_peaks');
+
+    %{
+ %all_locs =nan(4,length(channel_data(1,1,:)));
+ for n =1:length(data(1,:))
+   lpc       = 4.5; %low pass cutoff
+   lWn       = lpc/nyq;
+   [bwb,bwa] = butter(4,lWn,'low');
+   lpsu      = filtfilt(bwb,bwa, data(:,n));
+
+   norm_lpsu = (lpsu - min(lpsu))/(max(lpsu)- min(lpsu));
+   %all_lpsu(:,n) = lpsu;
+  % plot(lpsu)
+ if ~all(isnan(norm_lpsu)) 
+  for len = 30:550
+            if norm_lpsu(len) < norm_lpsu(len+1)
+   locs = findpeaks(norm_lpsu(len:1200));
+        break
+            end
+  end
+
+         if length(locs.loc) >= 4
+             %adjust location to the first data point of lpsu (+len),
+    lpsulocs = locs.loc(1:4) + len;
+   all_norm_pks(:,n) = norm_lpsu(lpsulocs(1:4));
+
+         end 
+ end
+% plot(locs.loc +len, lpsu(locs.loc +len))
+ end
+
+ namelist(1,1:length(sprintf('chan_%d',i))) = sprintf('chan_%d',i);
+ norm_data_peaks(i).namelist = all_norm_pks;
+channelfilename = [newdatadir filename];
+%save(strcat(channelfilename, '.mat'), 'all_pks');
+ end
+    %}
+ allfilename = [newdatadir 'all_norm_data_peaks'];
+ save(strcat(allfilename, '.mat'), 'norm_data_peaks');

new_data_set.asv

@@ -0,0 +1,15 @@
+gooddatadir = 'C:\Users\maier\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\';
+newdatadir = 'C:\Users\maier\Documents\LGN_data\single_units\inverted_power_channels\good_single_units_data_4bumps_more\new_peak_alignment_anal\';
+
+
+channelfilename = [gooddatadir 'good_single_units_data_4bmpmore']; 
+data_file = load(channelfilename);
+
+%Reject 160517 as no well triggered (1)
+%Reject 180806 p1 uclust17, M cell, as doesn't seem well triggered (46)
+%Reject 181207 (B) uclust22, M cell, as doesn't seem well triggered (55)
+new_data = data_file.good_data([2:45,47:54,56:end]);
+
+allfilename = [newdatadir 'refined_dataset'];
+save(strcat(allfilename, '.mat'), 'new_data', '-v7.3');
+