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Figure7.m
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baseDir = 'D:\TPM\JK\Pub_S1AngleCode\'; % The folder containing folders of data ('\Behavior', '\Calcium', '\Whisker') and dependent codes ('\MATLAB codes')
%% basic settings
calciumDir = [baseDir, 'Calcium\'];
colors = [248 171 66; 40 170 225] / 255;
%% Figure 7A
tuneFn = 'angle_tuning_summary_preAnswer_perTouch_NC_PTC';
tune = load([calciumDir, tuneFn]);
learnerInd = [1:4,7,9];
tune.naive = tune.naive(learnerInd);
numMice = length(learnerInd);
jk027length = length(tune.naive(2).touchID);
jk027inds = find(tune.naive(2).touchID > 5000);
fn = fieldnames(tune.naive);
for i = 1 : length(fn)
if size(tune.naive(2).(fn{i}), 1) == jk027length
tune.naive(2).(fn{i}) = tune.naive(2).(fn{i})(jk027inds,:);
elseif size(tune.naive(2).(fn{i}), 2) == jk027length
tune.naive(2).(fn{i}) = tune.naive(2).(fn{i})(:,jk027inds);
end
end
maxResponseTuned = cell(numMice,2);
meanOtherResponseTuned = cell(numMice,2);
angleSelectivityTuned = cell(numMice,2);
angleSelectivityNormTuned = cell(numMice,2);
for mi = 1 : numMice
maxResponseTuned{mi,1} = cellfun(@(x) max(cellfun(@mean, x)), tune.naive(mi).val);
maxResponseTuned{mi,2} = cellfun(@(x) max(cellfun(@mean, x)), tune.expert(mi).val);
meanOtherResponseTuned{mi,1} = cellfun(@(x) (sum(cellfun(@mean, x)) - max(cellfun(@mean, x)))/6, tune.naive(mi).val);
meanOtherResponseTuned{mi,2} = cellfun(@(x) (sum(cellfun(@mean, x)) - max(cellfun(@mean, x)))/6, tune.expert(mi).val);
angleSelectivityTuned{mi,1} = maxResponseTuned{mi,1} - meanOtherResponseTuned{mi,1};
angleSelectivityTuned{mi,2} = maxResponseTuned{mi,2} - meanOtherResponseTuned{mi,2};
angleSelectivityNormTuned{mi,1} = (maxResponseTuned{mi,1} - meanOtherResponseTuned{mi,1}) ./ maxResponseTuned{mi,1};
angleSelectivityNormTuned{mi,2} = (maxResponseTuned{mi,2} - meanOtherResponseTuned{mi,2}) ./ maxResponseTuned{mi,2};
end
%%
figure, hold on
tempMat = cellfun(@mean, angleSelectivityTuned);
[~,p] = ttest(tempMat(:,1), tempMat(:,2));
errorbar([1,2], mean(tempMat), sem(tempMat), 'k', 'lines', 'no')
for i = 1 : 2
bar(i, mean(tempMat(:,i)), 'facecolor', colors(i,:))
end
for mi = 1 : numMice
plot([1,2], tempMat(mi,:), 'ko-')
end
errorbar([1,2], mean(tempMat), sem(tempMat), 'k', 'linewidth', 2, 'lines', 'no')
xlim([0.5 2.5]), xticks([1, 2])
xticklabels({'Naive', 'Expert'}), xtickangle(45)
ylim([0 0.6]), yticks(0:0.2:0.6)
ylabel('Mean angle selectivity')
title(sprintf('p = %f',p))
%%
%% 7B
%% Not-selective neurons among touch neurons
tuneFn = 'angle_tuning_summary_preAnswer_perTouch_NC_PTC';
tune = load([calciumDir, tuneFn]);
learnerInd = [1:4,7,9];
tune.naive = tune.naive(learnerInd);
numMice = length(learnerInd);
jk027length = length(tune.naive(2).touchID);
jk027inds = find(tune.naive(2).touchID > 5000);
fn = fieldnames(tune.naive);
for i = 1 : length(fn)
if size(tune.naive(2).(fn{i}), 1) == jk027length
tune.naive(2).(fn{i}) = tune.naive(2).(fn{i})(jk027inds,:);
elseif size(tune.naive(2).(fn{i}), 2) == jk027length
tune.naive(2).(fn{i}) = tune.naive(2).(fn{i})(:,jk027inds);
end
end
propNsTuned = zeros(numMice,2); % 1: naive 2: expert
for mi = 1 : numMice
if mi == 2
tempInd = find(tune.naive(mi).touchID > 5000);
propNsTuned(mi,1) = 1 - sum(tune.naive(mi).tuned(tempInd)) / length(tempInd);
else
propNsTuned(mi,1) = 1 - sum(tune.naive(mi).tuned) / length(tune.naive(mi).tuned);
end
propNsTuned(mi,2) = 1 - sum(tune.expert(mi).tuned) / length(tune.expert(mi).tuned);
end
figure, hold on
for i = 1 : size(propNsTuned,1)
plot(propNsTuned(i,:), 'o-', 'color', [0.6 0.6 0.6])
end
[~,p] = ttest(propNsTuned(:,1) - propNsTuned(:,2));
errorbar(mean(propNsTuned), sem(propNsTuned), 'ro', 'lines', 'no')
xticks([1:2])
xticklabels({'Naive', 'Expert'})
xtickangle(45)
xlim([0.5 2.5])
ylim([0 0.4]), yticks(0:0.1:0.4)
ylabel('Proportion (/touch cells)')
title({'Not-selective'; sprintf('p = %f', p)})
%%
%% 7C
%%
% Quantification of angle selectivity increase
% compared across preferred angles
angles = 45:15:135;
atShapes = cell(numMice,length(angles),2);
for mi = 1 : numMice
% naive
indTunedAngle = tune.naive(mi).tunedAngle;
for ai = 1 : length(angles)
indAngle = find(indTunedAngle == angles(ai));
atShapes{mi,ai,1} = cell2mat(cellfun(@(x) cell2mat(cellfun(@mean, x', 'un', 0)),tune.naive(mi).val(indAngle), 'un', 0));
end
% epxert
indTunedAngle = tune.expert(mi).tunedAngle;
for ai = 1 : length(angles)
indAngle = find(indTunedAngle == angles(ai));
atShapes{mi,ai,2} = cell2mat(cellfun(@(x) cell2mat(cellfun(@mean, x', 'un', 0)),tune.expert(mi).val(indAngle), 'un', 0));
end
end
asNaive = cell(1,length(angles));
asExpert = cell(1,length(angles));
for ai = 1 : length(angles)
asNaive{ai} = cell2mat(cellfun(@(x) max(x,[],2) - (sum(x,2) - max(x,[],2))/6, atShapes(:,ai,1), 'un', 0));
asExpert{ai} = cell2mat(cellfun(@(x) max(x,[],2) - (sum(x,2) - max(x,[],2))/6, atShapes(:,ai,2), 'un', 0));
end
%
barOffset = 0.2*15;
barWidth = 0.4*15;
p = zeros(1,length(angles));
figure, hold on
for ai = 1 : length(angles)
bar(angles(ai)-barOffset, mean(asNaive{ai}), barWidth, 'facecolor', colorsTransient(1,:))
bar(angles(ai)+barOffset, mean(asExpert{ai}), barWidth, 'facecolor', colorsTransient(2,:))
if ai == 1
legend({'Naive', 'Expert'}, 'box', 'off', 'autoupdate', false)
end
errorbar(angles(ai)-barOffset, mean(asNaive{ai}), sem(asNaive{ai}), 'k')
errorbar(angles(ai)+barOffset, mean(asExpert{ai}), sem(asExpert{ai}), 'k')
[~, p(ai)] = ttest2(asNaive{ai}, asExpert{ai});
end
ylabel('Angle selectivity')
xlim([35 145])
xticks(angles)
xlabel('Preferred angle (\circ)')
%%
%% 7D - Tuning curves (45 and 135 degrees)
%%
figure, subplot(211), hold on
angleInd = 1;
naiveShape = cell2mat(atShapes(:,angleInd,1));
expertShape = cell2mat(atShapes(:,angleInd,2));
plot(angles, mean(naiveShape), 'color', colorsTransient(1,:))
plot(angles, mean(expertShape), 'color', colorsTransient(2,:))
legend({'Naive', 'Expert'}, 'box', 'off', 'autoupdate', false)
boundedline(angles, mean(naiveShape), sem(naiveShape), 'cmap', colorsTransient(1,:))
boundedline(angles, mean(expertShape), sem(expertShape), 'cmap', colorsTransient(2,:))
xlim([40, 140]), xticks(angles), xlabel('Object angle (\circ)')
ylim([0 0.8]), yticks(0:0.2:0.8), ylabel('Response')
title('Preferring 45\circ')
subplot(212), hold on
angleInd = 7;
naiveShape = cell2mat(atShapes(:,angleInd,1));
expertShape = cell2mat(atShapes(:,angleInd,2));
plot(angles, mean(naiveShape), 'color', colorsTransient(1,:))
plot(angles, mean(expertShape), 'color', colorsTransient(2,:))
legend({'Naive', 'Expert'}, 'box', 'off', 'autoupdate', false)
boundedline(angles, mean(naiveShape), sem(naiveShape), 'cmap', colorsTransient(1,:))
boundedline(angles, mean(expertShape), sem(expertShape), 'cmap', colorsTransient(2,:))
xlim([40, 140]), xticks(angles), xlabel('Object angle ()')
ylim([0 0.8]), yticks(0:0.2:0.8), ylabel('Response')
title('Preferring 135\circ')
%%
%% S7G (intermediate angles)
%%
figure
for i = 1:5
subplot(1,5,i), hold on
angleInd = i+1;
naiveShape = cell2mat(atShapes(:,angleInd,1));
expertShape = cell2mat(atShapes(:,angleInd,2));
plot(angles, mean(naiveShape), 'color', colorsTransient(1,:))
plot(angles, mean(expertShape), 'color', colorsTransient(2,:))
if i == 1
legend({'Naive', 'Expert'}, 'box', 'off', 'autoupdate', false)
end
boundedline(angles, mean(naiveShape), sem(naiveShape), 'cmap', colorsTransient(1,:))
boundedline(angles, mean(expertShape), sem(expertShape), 'cmap', colorsTransient(2,:))
xlim([40, 140]), xticks(angles), xlabel('Object angle (\circ)')
ylim([0 0.8]), yticks(0:0.2:0.8), ylabel('Response')
title(['Preferring ', num2str(angles(angleInd)), '\circ'])
end