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analysis_code_LaChioma2020.m
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analysis_code_LaChioma2020.m
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%% DI distribution (Fig. 2B)
% You just need to load variable Disp:
% load([pwd '\Vars\Disp_DGD_V1_LM_RL.mat']);
Areas = {'V1','LM','RL'};
nAreas = numel(Areas);
SFs = {'0.01'};
nSFs = numel(SFs);
thr_std = 4;
nbins = 7;
CC = load_DGD_colors;
for ax = 1 : nAreas
Area = Areas{ax};
for i = 1 : nSFs
SF = SFs{i};
s = find(strcmp({Disp.(Area).SF}, SF));
ts = find(Disp.(Area)(s).thr_std==thr_std);
nExps_tmp = length(Disp.(Area)(s).StatsDI_perExp{ts}.DI_eachExp);
DI_distrib_eachExp = nan(nExps_tmp, nbins);
for e = 1 : nExps_tmp
DI_thisExp = Disp.(Area)(s).StatsDI_perExp{ts}.DI_eachExp{e};
[DI_distrib_eachExp(e,:), edges] = histcounts(DI_thisExp, 0:1/nbins:1, 'Normalization','Probability');
end
DI_distrib_eachExp = DI_distrib_eachExp*100;
DI_distrib_meanAcrossExps = mean(DI_distrib_eachExp, 1);
DI_distrib_stdAcrossExps = std(DI_distrib_eachExp, 0,1);
DI_distrib_semAcrossExps = DI_distrib_stdAcrossExps/sqrt(nExps_tmp);
hdi = figure;
dx = (edges(2)-edges(1));
xticks = edges(2:end)-dx/2;
superbar( xticks, DI_distrib_meanAcrossExps,...
'E', DI_distrib_semAcrossExps,...
'BarWidth',0.9*dx, 'BarFaceColor', CC.(Areas{ax}){i}, 'BarEdgeColor','none',...
'ErrorbarRelativeWidth',0.0, 'ErrorbarLineWidth',3);
ylabel('Proportion of cells (%)' );
xlim([-0.04 1.04])
if strcmp(SF,'0.10')
ylim([0 52])
else
ylim([0 52])
end
set(gca, 'YTick',[0:10:100])
set(gca, 'XTick',[0 1],'XTickLabel',[0 1])
xlabel('Disparity selectivity Index')
title_str = ['Area ' Area ' - SF ' SF ' cpd'];
title( title_str, 'Interpreter','none' );
end
end
%% ODI (Fig. 3A)
% You just need to load variable OD
% load([pwd '\Vars\OD_DG_V1_LM_RL.mat']);
Areas = {'V1','LM','RL'};
nAreas = numel(Areas);
CC = load_DGD_colors(1);
edges = -1:2/7:1; dx = (edges(2)-edges(1)); xticks = edges(2:end)-dx/2;
xrange = [-1.1 1.1]; yrange = [-0.8 35]; dy = [10];
c = 2; % color shade
for ax = 1 : nAreas
Area = Areas{ax};
ODI = OD.(Area);
CountODI_EachExp_perc = 100*cat(1, ODI.CountODI_EachExp_perc);
CountODI_meanAcrossExps_perc = mean(CountODI_EachExp_perc, 1);
CountODI_stdAcrossExps_perc = std (CountODI_EachExp_perc, 0, 1);
nExps_tmp = size(CountODI_EachExp_perc, 1);
CountODI_semAcrossExps_perc = CountODI_stdAcrossExps_perc / sqrt(nExps_tmp);
% Figure: ODI distribution across exps
figure;
fig_pos = get(gcf, 'position'); fig_pos(3) = fig_pos(3)*1.3; %fig_pos(2) = fig_pos(2)-fig_pos(4)*0.5;
set(gcf, 'position',fig_pos); % [left, bottom, width, height]
hb = superbar( xticks, CountODI_meanAcrossExps_perc,...
'E', CountODI_semAcrossExps_perc,...
'BarWidth',0.9*dx, 'BarFaceColor', CC.(Areas{ax}){c}, 'BarEdgeColor','none',...
'ErrorbarRelativeWidth',0.0, 'ErrorbarLineWidth',3);
set(gca, 'XTick',[-1 0 1],'XLim',xrange,'XTickLabel',{'-1','0','1'}, 'TickLength',[0.02 0.025])
set(gca, 'YTick',[0:dy:200], 'YLim',yrange);
xlabel('Ocular Dominance Index (ODI)');
ylabel('Proportion of cells (%)')
hh = get( hb(1), 'BaseLine');
hh.LineStyle = 'none';
end
%% DI vs ODI (Fig. 3B)
% You just need to load variables OD and DIvar
% load([pwd '\Vars\OD_DG_V1_LM_RL.mat']);
% load([pwd '\Vars\DIvar_DGD_V1_LM_RL.mat']);
Areas = {'V1','LM','RL'};
SFs = {'0.01'};
nSFs = length(SFs);
nAreas = numel(Areas);
CC = load_DGD_colors(1); CC2 = load_DGD_colors(1);
acircle = 42;
rangex = [-1.05 1.05]; dx = 0.5; ticklabels = {'-1','','0','','1'};
rangey = [-0.05 1.05];
errorbar_width = 0.1; markesize = 10;
Edges = linspace(-1, 1, 7+1);
nBins = length(Edges) - 1;
xValBins = mean([Edges(1:end-1);Edges(2:end)]);
p_anova = nan(nAreas,1);
for ax = 1 : nAreas
Area = Areas{ax};
ODI = cat(1, OD.(Area).ODI);
MI = abs(ODI);
resp_ODI = find(~isnan(ODI));
ODIselall = []; DIselall = [];
for i = 1 : nSFs
SF = SFs{i};
s = find( strcmp(DIvar.SF, SF) );
DI = DIvar.(Area)(s).DI;
resp_DGDsf = DIvar.(Area)(s).resp_DGDthisSF;
resp = intersect(resp_DGDsf, resp_ODI);
ODIsel = ODI(resp);
MIsel = MI(resp);
BIsel = 1-MIsel;
DIsel = DI(resp);
ODIselall = [ODIselall; ODIsel];
DIselall = [DIselall ; DIsel ];
end
ODIsel_binmean = nan(nBins,1);
ODIsel_binsem = nan(nBins,1);
[~,~,bins] = histcounts(ODIselall, Edges);
for b = 1:nBins
binmean = mean( DIselall(bins==b) ) ;
binsem = std( DIselall(bins==b) ) / sqrt(sum(bins==b));
if isempty(binmean), binmean = NaN; binsem = NaN;end;
ODIsel_binmean(b) = binmean;
ODIsel_binsem(b) = binsem;
end
%%%% ANOVA test:
Group_all = [];
DIsel_allbins = [];
[~,~,bins] = histcounts(ODIselall, Edges);
for b = 1:nBins
DIsel_thisbin = DIselall(bins==b) ;
DIsel_allbins = [DIsel_allbins; DIsel_thisbin];
Group_thisbin = repmat(b, length(DIsel_thisbin),1);
Group_all = [Group_all; Group_thisbin];
end
TestType = 'KW';
p_anova(ax) = perform_anova1(DIsel_allbins, Group_all, TestType);
%%%%
[rhoMIDI,pMIDI] = corr( DIselall, (ODIselall), 'type', 'Pearson' );
figure; hold on;
fig_pos = get(gcf, 'position'); fig_pos(3) = fig_pos(3)*1.0; %fig_pos(2) = fig_pos(2)-fig_pos(4)*0.5;
set(gcf, 'position',fig_pos); % [left, bottom, width, height]
scatter(ODIselall, DIselall, acircle, CC.(Area){2}, 'filled')
supererr(xValBins, ODIsel_binmean, [], ODIsel_binsem,...
errorbar_width, 'LineWidth',2, 'Color',CC2.(Area){1});
plot( xValBins, ODIsel_binmean,...
'LineWidth',2,'Color',CC2.(Area){1},'Marker','o','MarkerSize',markesize,'MarkerFaceColor',CC.(Area){1})
xlabel('ODI');
ylabel('DI');
xlim(rangex); ylim(rangey);
set(gca, 'XTick',[-1:dx:1],'YTick',[0:dx:1],'XTickLabel',ticklabels);
str_title = {...
['Area ' Area ' - SF:' ' all pooled' ];...
['CorrCoeff = ' num2str(rhoMIDI,'%3.2f') ' (p=' num2str(pMIDI,'%4.3f') ')' ]};
title(str_title, 'Interpreter','none');
end
%% Facilitation index (Fig. 3C)
% You just need to load variable R:
% load([pwd '\Vars\R__DGD_V1_LM_RL.mat']);
Which_R = 'Ratio_F_DGD_F_DG_sumLR'; yrange = [0 3]; dy = 0.5;
% Which_R = 'Ratio_F_DGD_F_DG_maxLR'; yrange = [0 4]; dy = 0.5;
% Which_Mean = 'exps_pooled'; %not implemented
Which_Mean = 'exp_wise';
% Bar_Orientation = 'v' ;
Bar_Orientation = 'h' ;
Areas = {'V1','LM','RL'};
nAreas = numel(Areas);
SFs = {'0.01'};
nSFs = numel(SFs);
CC = load_DGD_colors(2);
for ax = 1 : nAreas
Area = Areas{ax};
R_meanAcrossExps = nan(nSFs,1);
R_SEMAcrossExps = nan(nSFs,1);
R_AllSFs=[]; Group=[];
figure; hold on;
for i = 1 : nSFs
SF = SFs{i};
s = find(strcmp({R.(Area).SF}, SF));
if strcmp(Which_R, 'Ratio_F_DGD_F_DG_sumLR')
if strcmp(Which_Mean, 'exp_wise')
R_meanAcrossExps(i) = R.(Area)(s).Ratio_F_DGD_F_DG_sumLR_meanAcrossExps;
R_SEMAcrossExps(i) = R.(Area)(s).Ratio_F_DGD_F_DG_sumLR_semAcrossExps;
R_singlevals = R.(Area)(s).Ratio_F_DGD_F_DG_sumLR_meanEachExp;
end
elseif strcmp(Which_R, 'Ratio_F_DGD_F_DG_maxLR')
if strcmp(Which_Mean, 'exp_wise')
R_meanAcrossExps(i) = R.(Area)(s).Ratio_F_DGD_F_DG_maxLR_meanAcrossExps;
R_SEMAcrossExps(i) = R.(Area)(s).Ratio_F_DGD_F_DG_maxLR_semAcrossExps;
R_singlevals = R.(Area)(s).Ratio_F_DGD_F_DG_maxLR_meanEachExp;
end
end
% for following Significance test
R_AllSFs = [R_AllSFs; R_singlevals];
Group_tmp = repmat({SF}, numel(R_singlevals),1);
Group = [Group; Group_tmp];
end
[~,P] = perform_anova1(R_AllSFs, Group);
P( P>0.05 ) = nan;
% P = nan;
% hb = bar( i, R_meanAcrossExps, R_SEMAcrossExps, 'FaceColor',CC.(Area){i});
superbar( [1:nSFs], R_meanAcrossExps,...
'E', R_SEMAcrossExps,...
'Orientation', Bar_Orientation,...
'BarWidth',[], 'BarFaceColor', CC.(Areas{ax}), 'BarEdgeColor','none',...
'ErrorbarRelativeWidth',0.0, 'ErrorbarLineWidth',4,...
'P', P,...
'PStarShowNS',false,'PLineWidth',1,'PStarBackgroundColor','none','PLineBackingColor','none',...
'PStarFontSize',10, 'PLineOffset',0.2 ...
);
if strcmp(Bar_Orientation,'v')
set(gca, 'YLim',yrange,'YTick',[0:dy:5]);
set(gca, 'XTick',[],'XTickLabel',[])
else
set(gca, 'XLim',yrange,'XTick',[0:dy:5]);
set(gca, 'YTick',[],'YTickLabel',[])
end
title('Facilitation index')
end
%% Suppression index (Fig. 3C)
% You just need to load variable R:
% load([pwd '\Vars\R__DGD_V1_LM_RL.mat']);
% Which_R = 'Ratio_F_DGD_min_F_DG_sumLR'; yrange = [0 0.6];
Which_R = 'Ratio_F_DGD_min_F_DG_maxLR'; yrange = [0 1]; dy = 0.25;
% Bar_Orientation = 'v' ;
Bar_Orientation = 'h' ;
Areas = {'V1','LM','RL'};
nAreas = numel(Areas);
SFs = {'0.01'};
nSFs = numel(SFs);
CC = load_DGD_colors(2);
for ax = 1 : nAreas
Area = Areas{ax};
R_meanAcrossExps = nan(nSFs,1);
R_SEMAcrossExps = nan(nSFs,1);
R_AllSFs=[]; Group=[];
figure; hold on;
for i = 1 : nSFs
SF = SFs{i};
s = find(strcmp({R.(Area).SF}, SF));
if strcmp(Which_R, 'Ratio_F_DGD_min_F_DG_sumLR')
R_meanAcrossExps(i) = R.(Area)(s).Ratio_F_DGD_min_F_DG_sumLR_meanAcrossExps;
R_SEMAcrossExps(i) = R.(Area)(s).Ratio_F_DGD_min_F_DG_sumLR_semAcrossExps;
R_singlevals = R.(Area)(s).Ratio_F_DGD_min_F_DG_sumLR_meanEachExp';
elseif strcmp(Which_R, 'Ratio_F_DGD_min_F_DG_maxLR')
R_meanAcrossExps(i) = R.(Area)(s).Ratio_F_DGD_min_F_DG_maxLR_meanAcrossExps;
R_SEMAcrossExps(i) = R.(Area)(s).Ratio_F_DGD_min_F_DG_maxLR_semAcrossExps;
R_singlevals = R.(Area)(s).Ratio_F_DGD_min_F_DG_maxLR_meanEachExp';
end
% for following Significance test
R_AllSFs = [R_AllSFs; R_singlevals];
Group_tmp = repmat({SF}, numel(R_singlevals),1);
Group = [Group; Group_tmp];
end
[~,P] = perform_anova1(R_AllSFs, Group);
P( P>0.05 ) = nan;
% P = nan;
% hb = bar( i, R_meanAcrossExps, R_SEMAcrossExps, 'FaceColor',CC.(Area){i});
hb = superbar( [1:nSFs], R_meanAcrossExps,...
'E', R_SEMAcrossExps,...
'Orientation', Bar_Orientation,...
'BarWidth',[], 'BarFaceColor', 'none', 'BarEdgeColor',CC.(Areas{ax}),'BarLineWidth',10,...
'ErrorbarRelativeWidth',0.0, 'ErrorbarLineWidth',4,...
'P', P,...
'PStarShowNS',false,'PLineWidth',1,'PStarBackgroundColor','none','PLineBackingColor','none',...
'PStarFontSize',10, 'PLineOffset',0.1 ...
);
hh = get( hb(1), 'BaseLine'); hh.LineStyle = 'none';
if strcmp(Bar_Orientation,'v')
set(gca, 'YLim',yrange,'YTick',[-2:0.25:5]);
set(gca, 'XTick',[],'XTickLabel',[])
else
set(gca, 'XLim',yrange,'XTick',[0:dy:5]);
set(gca, 'YTick',[],'YTickLabel',[])
end
title('Suppression index')
end
%% Facilitation and suppression: F_DGD vs. F_DG (Fig. 3C)
% You just need to load variable R:
% load([pwd '\Vars\R__DGD_V1_LM_RL.mat']);
Areas = {'V1','LM','RL'};
nAreas = numel(Areas);
SFs = {'0.01'};
nSFs = numel(SFs);
thr_std = 4;
CC = load_DGD_colors(2);
acircle = 72; color_diagonal = [1 1 1]*0.5;
for ax = 1 : nAreas
Area = Areas{ax};
for i = 1 : nSFs
SF = SFs{i};
s = find(strcmp({Rall.(Area).SF}, SF));
F_DGD_max_bothDG_DGD = Rall.(Area)(s).F_DGD_max_bothDG_DGD;
F_DGD_min_bothDG_DGD = Rall.(Area)(s).F_DGD_min_bothDG_DGD;
F_DG_maxLR_bothDG_DGD = Rall.(Area)(s).F_DG_maxLR_bothDG_DGD;
F_DG_sumLR_bothDG_DGD = Rall.(Area)(s).F_DG_sumLR_bothDG_DGD;
figure; hs=[];
cbar_range = [0 1];
axes_range_1 = [-2.5/30 2.5]; axes_range_2 = [-1/30 1];
hold on
scatter(F_DG_sumLR_bothDG_DGD, F_DGD_max_bothDG_DGD, acircle, CC.(Area){i}, 'filled');
h = plot([0 10],[0 10],'LineStyle','--','Color',color_diagonal);
uistack(h, 'down');
xlim(axes_range_1); ylim(axes_range_1);
set(gca, 'XTick',[0:10], 'YTick',[0:10])
xlabel('\DeltaF/F_{Monoc Left+Right}'); %xlabel('F DG sumLR (\DeltaF/F)')
ylabel('\DeltaF/F_{Binoc Peak}'); %ylabel('F DGD peak (\DeltaF/F)')
axis square
if SaveFigures
% title(''); xlabel(''); ylabel(''); set(gca,'XTickLabel',[]); set(gca,'YTickLabel',[]);
title(''); axis_pos = get(gca, 'Position');
filename = ['I:\Alessandro La Chioma\AnalyzedData\Alessandro\DGD\2017-01-06\Figures Poster\'...
'F_DGD_peak vs F_DG ' 'Area ' Area ' SF ' SF '.eps'];
set(gca, 'Color', 'none'); set(gcf, 'Color', 'none');
set(gca, 'Position',axis_pos);
export_fig(gcf, filename, '-eps', '-q101', '-transparent')
end
%
figure;
% hs(2) = subaxis(1,2,2);
hold on
scatter(F_DG_maxLR_bothDG_DGD, F_DGD_min_bothDG_DGD, acircle-10, CC.(Area){i},'LineWidth',1);
% scatter(F_DG_sumLR_bothDG_DGD, F_DGD_min_bothDG_DGD, acircle-10, CC.(Area){i},'LineWidth',1);
h = plot([0 10],[0 10],'LineStyle','--','Color',color_diagonal);
uistack(h, 'down');
xlim(axes_range_2); ylim(axes_range_2);
set(gca, 'XTick',[0:0.5:10], 'YTick',[0:0.5:10])
xlabel('\DeltaF/F_{Monoc Max}'); %xlabel('F DG maxLR (\DeltaF/F)')
ylabel('\DeltaF/F_{Binoc Null}'); %ylabel('F DGD min (\DeltaF/F)')
axis square
end
end
%% Neighbor analysis (Fig. 4B)
Areas = {'V1'};%, 'LM', 'RL'};
SFs = {'0.01'};
thr_type = 1; thr_std = 4;
thr_di = 0.3;
plot_figs = [];
Edges = [0,10,20,40:40:200,300,400,Inf];
PhasesToExclude = [ ];
nAreas = numel(Areas);
nSFs = numel(SFs);
NNexp=[]; NNall=[];
for i = 1 : nSFs
SF = SFs{i};
NNexp(i).SF = SF; NNall(i).SF = SF;
for ax = 1 : nAreas
Area = Areas{ax};
filename = [pwd '\Vars\' 'var_DGD_' Area '.mat'];
aDGDx = load( filename );
aDGDx = aDGDx.('aDGD');
[NNexp(i).(Area)] = Map_IOPhase_NearestNeighbor( aDGDx, [90,270], thr_type, thr_std, thr_di, SF, Edges, PhasesToExclude, plot_figs);
end
end
%% Occurrences of disparity preference (Fig. 4C)
% You just need to load variable TC_A
% load([pwd '\Vars\TC_A__DGD_V1_LM_RL.mat']);
Areas = {'V1','LM','RL'};
nAreas = numel(Areas);
SFs = {'0.01'};
nSFs = numel(SFs);
CC = load_DGD_colors(1);
IOPhases = [0:45:360]; xrange = [-30 390];
yrange = [0 50; 0 50; 0 120]; dy = [10, 10, 20];
for ax = 1 : nAreas
Area = Areas{ax};
for i = 1 : nSFs
SF = SFs{i};
s = find( strcmp(TC_A.SF, SF) );
CountPrefIOPhase_norm_meanAcrossExps = TC_A.(Area)(s).DD_Centered.CountPrefIOPhase_norm_meanAcrossExps;
CountPrefIOPhase_norm_semAcrossExps = TC_A.(Area)(s).DD_Centered.CountPrefIOPhase_norm_semAcrossExps;
% make distribution symmetric, adding x=360=0 at the end:
CountPrefIOPhase_norm_meanAcrossExps = 100*[CountPrefIOPhase_norm_meanAcrossExps, CountPrefIOPhase_norm_meanAcrossExps(1)];
CountPrefIOPhase_norm_semAcrossExps = 100*[CountPrefIOPhase_norm_semAcrossExps , CountPrefIOPhase_norm_semAcrossExps(1) ];
hdi = figure;
superbar( IOPhases, CountPrefIOPhase_norm_meanAcrossExps,...
'E', CountPrefIOPhase_norm_semAcrossExps,...
'BarWidth',0.85*45, 'BarFaceColor', CC.(Areas{ax}){s}, 'BarEdgeColor','none',...
'ErrorbarRelativeWidth',0.0, 'ErrorbarLineWidth',2);
set(gca, 'XTick',IOPhases,'XTickLabel',IOPhases,'XLim',xrange)
set(gca, 'YTick',[0:dy(s):100], 'YLim',yrange(s,:));
ylabel('Proportion of cells (%)' );
xlabel('Disparity preference (phase deg)');
title({ ['Area ' Area ' - SF ' SF ' cpd'] })
end
end
%% Noise correlations (distribution) (Fig. 5A)
% You just need to load variables NC
% load([pwd '\Vars\NC_DGD_V1_LM_RL.mat']);
NC = NC00;
Areas = {'V1','LM','RL'};
nAreas = numel(Areas);
CC = load_DGD_colors(1); c = 2;
xrange = [-0.5 0.5]; dx = 0.25;
for ax = 1:nAreas
Area = Areas{ax};
NCv_allExpsPooled = NC.(Area).NCv_allExpsPooled;
[NC_bincounts, edges] = histcounts(NCv_allExpsPooled, [-0.5:0.01:0.5]);
figure; % #1
fig_pos = get(gcf, 'position'); % fig_pos(3) = fig_pos(3)*1.5;
set(gcf, 'position',fig_pos); % [left, bottom, width, height]
ddx = (edges(2)-edges(1));
xticks = edges(2:end)-ddx/2;
superbar( xticks, NC_bincounts,...
'BarWidth',1*ddx, 'BarFaceColor', CC.(Areas{ax}){i}, 'BarEdgeColor','none');
title({['nPairs=' num2str(numel(NCv_allExpsPooled))];...
['MeanAll=' num2str(mean(NCv_allExpsPooled)) ' - ' 'Mean=' num2str(NC.(Area).NC_meanAcrossExps,'%4.3f') ]...
});
xlabel('Pairwise noise correlation')
ylabel('# pairs')
set(gca,'XLim',xrange,'XTick',[xrange(1):dx:xrange(2)],'XTickLabel',num2cell([xrange(1):dx:xrange(2)]))
end
%% Noise correlations vs. Distance vs. disparity preference difference (Fig. 5B)
% You just need to load variables NC
% load([pwd '\Vars\NC_DGD_V1_LM_RL.mat']);
NC = NC03;
Areas = {'V1','LM','RL'};
nAreas = numel(Areas);
nBinsDist = NC.(Areas{1})(1).nBinsDist; nBinsIOP = NC.(Areas{1})(1).nBinsIOP;
EdgesDist = NC.(Areas{1})(1).EdgesDist; xvalsEdges = EdgesDist(1:end-1) ;
dist_limit = 250; dix = find(EdgesDist>dist_limit, 1) -1;
Palettes = {'Blues','Greens','Oranges'};
for ax = 1 : nAreas
colors = cbrewer2(Palettes{ax}, 64);
colors = flipud(colors); % puts red on top, blue at the bottom
dc = 0.02;
colors = colors - repmat([dc dc dc],64,1); colors(colors<0)=0;
CCmap{ax} = colors;
end
for ax = 1:nAreas
Area = Areas{ax};
NC_vs_Dist_vs_IOP_allPairsPooled = NC.(Area).NC_vs_Dist_vs_IOP_allPairsPooled;
figure; % #6
hold on;
imagesc( NC_vs_Dist_vs_IOP_allPairsPooled((1:dix),:) );
cbar_h = colorbar;
cbar_h.TickDirection = 'in'; cbar_h.Box = 'off';
ylabel(cbar_h,'Noise correlation','rotation',270, 'VerticalAlignment','bottom', 'FontSize',14 )
% colormap('hot');
colormap(CCmap{ax});
set(gca,'YDir','normal')
set(gca,'XTick',[1:nBinsIOP], 'XTickLabel',[0:45:180])
axis tight
axis square;
set(gca, 'YTick',[0:dix]+0.5, 'YTickLabel',xvalsEdges(1:dix+1))
xlabel('Preferred disparity difference (deg)');
ylabel(['Intra-pair distance (' char(181) 'm)'])
set(gca,'LineWidth',2)
box on
end
%% Noise Correlations, perform shuffling (in preparation for Fig. 5C,D)
% load([pwd '\Vars\NC_DGD_V1_LM_RL.mat']);
NC = NC03;
nShuffles = 1000;
Areas = {'V1','LM','RL'};
nAreas = numel(Areas);
for ax = 1 : nAreas
Area = Areas{ax};
NCsh.(Area) = NoiseCorr_DGD_Shuffling(NC.(Area), nShuffles);
end
%% Noise Correlations vs. cortical distance (w/ shuffles) (Fig. 5C)
% Before this section, run the section:
% %% Noise Correlations, perform shuffling
Areas = {'V1','LM','RL'};
nAreas = numel(Areas);
or = 2;
%
EdgesDist = NC.(Areas{1})(1).EdgesDist; dx = diff(EdgesDist(1:2)); xvalsEdges = EdgesDist(1:end-1) +dx/2 ;
dist_limit = 200; dix = find(EdgesDist>dist_limit, 1);
linestyle = '-'; markersize = 12; markerstyle = 'o';
errorbar_width = 0;
yrange = [-0.01 0.05]; dy = 0.01;
for ax = 1:nAreas
Area = Areas{ax};
MarkerFaceColor = CC.(Area){i};
NCmean_Dist_perBin_acrossExps = NCsh.(Area).NCmean_Dist_perBin_acrossExps(:,or);
NCsem_Dist_perBin_acrossExps = NCsh.(Area).NCsem_Dist_perBin_acrossExps(:,or);
figure; hold on;
h = plot( xvalsEdges(1:dix), NCmean_Dist_perBin_acrossExps(1:dix),...
'LineWidth',3, 'LineStyle',linestyle, 'Color',CC.(Area){i},...
'Marker',markerstyle, 'MarkerFaceColor',MarkerFaceColor,'MarkerSize',markersize);
supererr( xvalsEdges(1:dix), NCmean_Dist_perBin_acrossExps(1:dix), [], NCsem_Dist_perBin_acrossExps(1:dix),...
errorbar_width, 'LineWidth',2, 'Color',CC.(Area){i});
plot( xvalsEdges(1:dix), NCsh.(Area).NCmean_Dist_perBin_eachExp_shuf.prctile975(1:dix,or),...
'LineWidth',1, 'LineStyle',':', 'Color',CC.(Area){i},...
'Marker','none', 'MarkerFaceColor',MarkerFaceColor,'MarkerSize',markersize/2);
plot( xvalsEdges(1:dix), NCsh.(Area).NCmean_Dist_perBin_eachExp_shuf.prctile025(1:dix,or),...
'LineWidth',1, 'LineStyle',':', 'Color',CC.(Area){i},...
'Marker','none', 'MarkerFaceColor',MarkerFaceColor,'MarkerSize',markersize/2);
uistack(h, 'top');
set(gca,'XTick',EdgesDist, 'XLim',[0 EdgesDist(dix)+dx]);
set(gca,'YLim',yrange);
% set(gca,'YTick',[-1:dy:1]), 'YLim',yrange);
xlabel(['Intra pair distance (' char(181) 'm)'])
ylabel({['Pairwise noise correlation']})
end
%% Noise Correlations vs. disparity preference difference (w/ shuffles) (Fig. 5D)
% Before this section, run the section:
% %% Noise Correlations, perform shuffling
NC = NC03;
Areas = {'V1','LM','RL'};
nAreas = numel(Areas);
CC = load_DGD_colors(1);
or = 2;
linestyle = {'-'}; markersize = 12; markerstyle = {'o'};
errorbar_width = 0;
yrange = [-0.0101 0.05]; dy = 0.01;
for ax = 1 : nAreas
Area = Areas{ax};
NCmean_IOPdiff_perBin_acrossExps = NC.(Area).NCmean_IOPdiff_perBin_acrossExps;
NCsem_IOPdiff_perBin_acrossExps = NC.(Area).NCsem_IOPdiff_perBin_acrossExps;
figure; hold on;
MarkerFaceColor = CC.(Area){i};
h= plot( [0:45:180], NCmean_IOPdiff_perBin_acrossExps(:,or),...
'LineWidth',3, 'LineStyle',linestyle, 'Color',CC.(Area){i},...
'Marker',markerstyle, 'MarkerFaceColor',MarkerFaceColor,'MarkerSize',markersize);
supererr( [0:45:180], NCmean_IOPdiff_perBin_acrossExps(:,or), [], NCsem_IOPdiff_perBin_acrossExps(:,or),...
errorbar_width, 'LineWidth',1.5, 'Color',CC.(Area){i});
plot( [0:45:180], NCsh.(Area).NCmean_IOPdiff_perBin_perOri_eachExp_shuf.prctile975(:,or),...
'LineWidth',1, 'LineStyle',':', 'Color',CC.(Area){i},...
'Marker','none', 'MarkerFaceColor',MarkerFaceColor,'MarkerSize',markersize/2);
plot( [0:45:180], NCsh.(Area).NCmean_IOPdiff_perBin_perOri_eachExp_shuf.prctile025(:,or),...
'LineWidth',1, 'LineStyle',':', 'Color',CC.(Area){i},...
'Marker','none', 'MarkerFaceColor',MarkerFaceColor,'MarkerSize',markersize/2);
uistack(h, 'top');
set(gca,'XTick',[0:45:180], 'XLim',[-20 200]);
set(gca,'YTick',[-1:dy:1], 'YLim',yrange);
xlabel('Preferred disparity difference')
ylabel({['Pairwise noise correlation']})
end
%% Population decoding (SVM)
% Run this section to perform SVM decoding from scratch (this will take
% many hours, with the default settings).
% Alternativerly, run directly the next section, which loads and plots
% SVM classifications already computed.
% Areas = {'V1', 'LM', 'RL'};
Areas = {'V1'};
nAreas = numel(Areas);
SF = '0.01';
thr_type = 1;
thr_std = 4;
nPredictors = [2, 5, 10, 15, 20, 40];
nCombosPred = 20;
% Directions = [2,4]'; % treat each direction (90 and 270) separately
Directions = [2,4]; % treat directions (90 and 270) together
ExpIDs = [];
Split = 6;
nShuffles = 100;
NoiseCorrelationBlind = 0;
nCVPpermutations = 1;
CombosStims = [1:8];
Method = 1;
newdir_name = [pwd '\Vars\' 'SVM_discrimination_new\' 'mc_' datestr(now,'yyyy-mm-dd_HH-MM-SS')];
mkdir(newdir_name);
for ax = 1 : nAreas
Area = Areas{ax};
filename = [pwd '\Vars\' 'var_DGD_' Area '.mat'];
aDGDx = load( filename );
aDGDx = aDGDx.('aDGD');
Function_SVM_DGD(aDGDx, SF, thr_type, thr_std, ExpIDs,...
nPredictors, nCombosPred, Directions, Split,...
nShuffles, NoiseCorrelationBlind, nCVPpermutations, CombosStims, Method, newdir_name)
end
%% Population decoding (SVM), plot (Fig. 6)
Areas = {'V1', 'LM', 'RL'};
folder = [pwd '\Vars\SVM_discrimination'];
nAreas = numel(Areas);
SF = '0.01';
for ax = 1 : nAreas
Area = Areas{ax};
S = plot_SVM_DGD_overExps( Area, SF, folder );
end
%% Plot RDS tuning curves (as in Fig. 7A)
% load([pwd '\Vars\Fit__AsymGauss__aRDS__V1_LM_RL.mat')
%%% Select area, exp nr., ROIs to plot
Areas = {'LM'}; ...{'V1','LM','RL'};
eee = 7; %%%%%%%%%%%%%%%%%%%%%%%%%%
ROIsToPlot = 1 : 10; % indixes of selROIs
%%%%%%%%%%%%%%%%%%%%%%%%%%
% Responsiveness criteria:
thr_std = 4;
thr_type = 1;
thr_rep_fraction_rds = 0.5;
thr_Rsquared = 0.5;%0.5;
RDStype = 'aRDS';
nAreas = numel(Areas);
CC = load_DGD_colors(1); cx = 1;
for ax = 1 : nAreas
Area = Areas{ax};
ai = find(strcmp(Area,{Fit.Area}));
nExps = length(Fit(ai).(RDStype));
for e = eee%1 : 3%%%%%nExps
% respRDS_1 = above_stdThreshold_DGD(aD_RDS(ai).(RDStype )(e) , thr_std, thr_type, [], thr_rep_fraction_rds);
% respRDS_2 = above_stdThreshold_DGD(aD_RDS(ai).(RDStype2)(e) , thr_std, thr_type, [], thr_rep_fraction_rds);
% respRDS = intersect(respRDS_1, respRDS_2);
% rsqRDS_1 = above_RsquaredThreshold(Fit(ai).(RDStag )(e).TC, thr_Rsquared, 'Rsquared_meanTC_1');
% rsqRDS_2 = above_RsquaredThreshold(Fit(ai).(RDStag )(e).TC, thr_Rsquared, 'Rsquared_meanTC_2');
% selROIs = mintersect(respRDS, rsqRDS_1, rsqRDS_2);
selROIs = [];
for i = 1 : length(Fit(ai).(RDStype)(e).TC)
if isfield(Fit(ai).(RDStype)(e).TC(i).GoF, 'Rsquared_meanTC') && Fit(ai).(RDStype)(e).TC(i).GoF.Rsquared_meanTC > thr_Rsquared
selROIs = [selROIs; i]; %#ok<AGROW>
end
end
disp([' Selected ROIs: ' num2str(length(selROIs)) ' / ' num2str(length(Fit(ai).(RDStype )(e).TC)) ]);
for i = ROIsToPlot % 1 : 10%20%length(selROIs)
nr = selROIs(i);
hf=figure(100000000+ax*10000000+e*10000+nr); hold on;
fig_pos = get(gcf, 'position'); fig_pos(3) = fig_pos(3)*1.3;
set(gcf, 'position',fig_pos); % [left, bottom, width, height]
x_val = Fit(ai).(RDStype)(e).StimSettings.bin_centers_deg;
hsa=subaxis(1,4,1,1,3,1); hold on;
supererr(x_val, Fit(ai).(RDStype)(e).TC(nr).TuningCurve_mean, [],Fit(ai).(RDStype)(e).TC(nr).TuningCurve_sem,...
'|','Color',CC.(Area){cx})
plot( x_val, Fit(ai).(RDStype)(e).TC(nr).TuningCurve_mean, 'Color',CC.(Area){cx},...
'Marker','o','LineWidth',2, 'MarkerFaceColor',CC.(Area){cx}, 'MarkerEdgeColor','none');
% plot( Fit(ai).(RDStype)(e).TC(nr).FittedData_x,...
% Fit(ai).(RDStype)(e).TC(nr).FittedData, 'r', 'LineWidth',2);
xlim([min(x_val) max(x_val)])
set(gca, 'XTick',[-20:10:20])
set(gca,'Color','none')
box off
subaxis(1,4,4,1,1,1)
Info = Fit(ai).(RDStype)(e).Info;
str_info= {...
[ Info.Mouse ];...
[ 'Area ' Info.Area ];...
[ Info.Spot ];...
[ 'Date ' Info.Date ,...
'exp' Info.Exp ];...' - ' 'IDexp ' num2str(Fit(ai).IDexp(e))];...
[ ' ' ];...
[ 'ROI # ' num2str(nr) ]; ...
[ ' ' ];
[ 'thr_std = ' num2str(thr_std) ];...
[ 'thr_type = ' num2str(thr_type) ];...
[ 'thr_rep_fraction_rds = ' num2str(thr_rep_fraction_rds)];...
[ 'thr_Rsquared = ' num2str(thr_Rsquared)];...
};
FitParam = Fit(ai).(RDStype)(e).TC(nr).FitParam; str_param=cell(length(FitParam),1);
for p = 1 : length(FitParam)
str_param{p} = [FitParam{p} ' = ' num2str(Fit(ai).(RDStype)(e).TC(nr).BestFitEsts(p)) ];
end
% str_param{p+1} = ['phase shift' ' = ' num2str((Fit(ai).(RDStag)(e).TC(nr).BestFitEsts(6)-Fit(ai).(RDStag)(e).TC(nr).BestFitEsts(8))) ];
str_param(end+1:end+4) = {...
[ ' ' ];...
[ 'SSE-meanTC_1 = ' num2str(Fit(ai).(RDStype)(e).TC(nr).GoF.SSE_meanTC) ];...
% [ 'SSE-meanTC_2 = ' num2str(Fit(ai).(RDStype)(e).TC(nr).GoF.SSE_meanTC_2) ];...
[ 'RMSE-meanTC_1 = ' num2str(Fit(ai).(RDStype)(e).TC(nr).GoF.RMSE_meanTC) ];...
% [ 'RMSE-meanTC_2 = ' num2str(Fit(ai).(RDStype)(e).TC(nr).GoF.RMSE_meanTC_2) ];...
[ 'R2-meanTC_1 = ' num2str(Fit(ai).(RDStype)(e).TC(nr).GoF.Rsquared_meanTC)];...
% [ 'R2-meanTC_2 = ' num2str(Fit(ai).(RDStype)(e).TC(nr).GoF.Rsquared_meanTC_2)];...
};
text( 0, 0.5, [str_info; str_param],...
'FontSize',8,'HorizontalAlignment','left','VerticalAlignment','middle','Interpreter','none');
axis off
end
end
end
%% Plot RDS tuning width and asymmetry (Fig. 7C,D)
%
% load([pwd '\Vars\Fit__AsymGauss__aRDS__V1_LM_RL.mat')
% clearvars -except db aD aD_RDS aD_RPS Fit Fit_RDS Fit_RPS
% Responsiveness criteria:
thr_std = 4;
thr_type = 1;
thr_Rsquared = 0.5;%0.5;
Areas = {'V1','LM','RL'};
% Areas = {'LM'};
RDStype1 = 'aRDS';
% RDStype2 = 'aRDS2';
RDStype = RDStype1; select_rois = 'intersect(respRDS_1, rsqRDS_1)';
% RDStype = RDStype2; select_rois = 'intersect(respRDS_2, rsqRDS_2)';
nAreas = numel(Areas);
nExps_max = 9;
Sigma_eachExp = cell(nExps_max, nAreas, 2);
SigmaDelta_eachExp = cell(nExps_max, nAreas);
SigmaSum_eachExp = cell(nExps_max, nAreas);
Sigma_allEachArea = cell(nAreas, 2);
SigmaDelta_allEachArea = cell(1, nAreas);
SigmaSum_allEachArea = cell(1, nAreas);
SigmaDelta_medEachExp = cell(1, nAreas);
SigmaSum_medEachExp = cell(1, nAreas);
selROIs_eachExp = cell(nExps_max, nAreas);
for ax = 1 : nAreas
Area = Areas{ax};
ai = find(strcmp(Area,{Fit.Area}));
nExps = length(Fit(ai).(RDStype));
for e = 1 : nExps
respRDS_1 = above_RsquaredThreshold(Fit(ai).(RDStype1 )(e).TC, 0, 'Rsquared_meanTC');
% respRDS_2 = above_RsquaredThreshold(Fit(ai).(RDStype2 )(e).TC, 0, 'Rsquared_meanTC');
rsqRDS_1 = above_RsquaredThreshold(Fit(ai).(RDStype1 )(e).TC, thr_Rsquared, 'Rsquared_meanTC');
% rsqRDS_2 = above_RsquaredThreshold(Fit(ai).(RDStype2 )(e).TC, thr_Rsquared, 'Rsquared_meanTC');
selROIs = eval(select_rois);
disp([' Selected ROIs: ' num2str(length(selROIs)) ' / ' num2str(length(Fit(ai).(RDStype )(e).TC)) ]);
Sigma = nan(length(selROIs),2);
SigmaDelta = nan(length(selROIs),1);
SigmaSum = nan(length(selROIs),1);
for i = 1 : length(selROIs)
nr = selROIs(i);
Sigma(i,1) = Fit(ai).(RDStype)(e).TC(nr).BestFitEsts(2);
Sigma(i,2) = Fit(ai).(RDStype)(e).TC(nr).BestFitEsts(5);
SigmaDelta(i) = Sigma(i,2) - Sigma(i,1);
SigmaSum(i) = Sigma(i,2) + Sigma(i,1);
end
if length(selROIs) > 0
Sigma_eachExp{e,ax,1} = Sigma(:,1);
Sigma_eachExp{e,ax,2} = Sigma(:,2);
SigmaDelta_eachExp{e,ax} = SigmaDelta;
SigmaSum_eachExp{e,ax} = SigmaSum;
selROIs_eachExp{e,ax} = selROIs;
end
end
Sigma_allEachArea{ax,1} = cell2mat(Sigma_eachExp(:,ax,1));
Sigma_allEachArea{ax,2} = cell2mat(Sigma_eachExp(:,ax,2));
SigmaDelta_allEachArea{ax} = cell2mat(SigmaDelta_eachExp(:,ax));
SigmaSum_allEachArea{ax} = cell2mat(SigmaSum_eachExp(:,ax));
SigmaDelta_medEachExp{ax} = cellfun(@(x) median(abs(x)), SigmaDelta_eachExp(:,ax),'UniformOutput',true);
SigmaSum_medEachExp{ax} = cellfun(@median, SigmaSum_eachExp(:,ax),'UniformOutput',true);
end
%%% Figures
CC = load_DGD_colors(1); cx = 1;
CC2 = load_DGD_colors(2); cx = 1;
% % Tuning asymmetry (Sigma2 - Sigma1) - cell-wise
% figure;
% for ax = 1 : nAreas
% Area = Areas{ax};
% h(ax) = subplot(nAreas,1,ax);
% hold on;
% histogram( (SigmaDelta_allEachArea{ax}),[-20:5:20],'Normalization','probability',...
% 'FaceColor',CC.(Area){cx});
% % set(gca,'XTick',[-180:30:180]);
% title(['mean = ' num2str(mean(SigmaDelta_allEachArea{ax})) ' - median = ' num2str(median(SigmaDelta_allEachArea{ax}))])
% end
% linkaxes(h);
% % Tuning asymmetry abs(Sigma2 - Sigma1) - cell-wise
% figure;
% for ax = 1 : nAreas
% Area = Areas{ax};
% h(ax) = subplot(nAreas,1,ax);
% hold on;
% histogram( abs(SigmaDelta_allEachArea{ax}),[-20:5:20],'Normalization','probability',...
% 'FaceColor',CC.(Area){cx});
% % set(gca,'XTick',[-180:30:180]);
% title(['mean = ' num2str(mean(abs(SigmaDelta_allEachArea{ax}))) ' - median = ' num2str(median(abs(SigmaDelta_allEachArea{ax})))])
% end
% linkaxes(h);
% Tuning asymmetry abs(Sigma2 - Sigma1) - exp-wise
figure;
sigma_delta_edges = [0:2.5:20, Inf];
n_bins = length(sigma_delta_edges) - 1;
for ax = 1 : nAreas
Area = Areas{ax};
h(ax) = subplot(nAreas,1,ax);
hold on
SigmaDelta_counts_eachExp = nan(length(SigmaSum_eachExp(:,ax)), n_bins);
for e = 1 : length(SigmaSum_eachExp(:,ax))
SigmaDelta_counts_eachExp(e,:) = histcounts(abs(SigmaDelta_eachExp{e,ax}),sigma_delta_edges,'Normalization','probability');
end
SigmaDelta_counts_meanEachExp = nanmean(SigmaDelta_counts_eachExp, 1);
SigmaDelta_counts_semEachExp = nanstd(SigmaDelta_counts_eachExp,0,1) ./ sqrt(sum(~isnan(SigmaDelta_counts_eachExp),1));
hb=superbar(1:n_bins, SigmaDelta_counts_meanEachExp, 'E',SigmaDelta_counts_semEachExp,...
'BarFaceColor',CC.(Area){cx}, 'ErrorbarColor',CC2.(Area){cx}, 'ErrorbarStyle','|');
set(hb, 'ShowBaseLine', 'off')
if ax==nAreas
set(gca, 'XTick',[0.5:n_bins-0.5,n_bins], 'XTickLabel',[num2cellstr(sigma_delta_edges(1:end-1)),{['>' num2str(sigma_delta_edges(end-1))]} ] )
xlabel('Tuning asymmetry |\sigma2 - \sigma1| (deg)')
else
set(gca, 'XTick',[])
end
end
linkaxes(h); ylim([-0.02 0.50]);
% % Tuning width cell-wise (Sigma1+Sigma2, as in La Chioma et al. 2019)
% figure;
% for ax = 1 : nAreas
% Area = Areas{ax};
% h(ax) = subplot(nAreas,1,ax);
% hold on;
% histogram( SigmaSum_allEachArea{ax},[0:3:42],'Normalization','probability',...
% 'FaceColor',CC.(Area){cx});
% % set(gca,'XTick',[-180:30:180]);