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Figure_3_c_d_e_data.m
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Figure_3_c_d_e_data.m
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%% Thomas_Yang_et al, 2023 @Nuo Li lab
%% Fig. 3c-e single trials
clear all
close all
addpath('../func/');
load Figure_3_c_d_e_data
%% Group neurons into Lick Right preferring and Lick Left preferring, examine their single trial dynamics together
corr_r_yes = [];
corr_r_no = [];
R_neurons_singleTrials_yes_allSesssions = [];
L_neurons_singleTrials_yes_allSesssions = [];
R_neurons_singleTrials_no_allSesssions = [];
L_neurons_singleTrials_no_allSesssions = [];
session_ID_yes = [];
session_ID_no = [];
R_neurons_yes_allSesssions = [];
L_neurons_yes_allSesssions = [];
R_neurons_no_allSesssions = [];
L_neurons_no_allSesssions = [];
i_session_selected = [];
figure
n_R_list = [];
n_L_list = [];
for i_session = 1:size(activity_matrix_allSession,1)
i_timebin = find(time_bins>-3.2 & time_bins<=-.4); %only use the LDA before cue
activity_matrix = activity_matrix_allSession{i_session};
spk_count_yes_screen = spk_count_yes_screen_allSession{i_session,1}; % neuron x 5, [whole_trial sample delay resposne]
spk_count_no_screen = spk_count_no_screen_allSession{i_session,1};
sig_selective = sig_selective_allSession{i_session,1};
i_yes_screen_trial = i_yes_screen_allSession{i_session,1};
i_no_screen_trial = i_no_screen_allSession{i_session,1};
i_yes_correct_trial = i_yes_correct_allSession{i_session,1}; % indices for non-stim trials, independent test data only
i_no_correct_trial = i_no_correct_allSession{i_session,1};
i_yes_error_trial = i_yes_error_allSession{i_session,1};
i_no_error_trial = i_no_error_allSession{i_session,1};
%average_activity = nanmean(activity_matrix,3); % average spike rate across all trials types, this will be substracted at each time point (detrending the data) to examine competition dynamics more closely
%average_activity = nanmean(activity_matrix(:,:,[i_yes_screen_trial; i_no_screen_trial; i_yes_error_trial; i_no_error_trial]),3); % average spike rate across all trials types, this will be substracted at each time point (detrending the data) to examine competition dynamics more closely
average_activity = nanmean(activity_matrix(:,:,[i_yes_screen_trial i_no_screen_trial]),3); % average spike rate across all trials types, this will be substracted at each time point (detrending the data) to examine competition dynamics more closely
sig_selective = sig_selective<.01; % selective neurons only
i_selR_unit = find(spk_count_yes_screen(:,3)>spk_count_no_screen(:,3) & sum(sig_selective(:,1:3),2)>0); % yes perferring neurons, this is not yet flipped to contra/ipsi
i_selL_unit = find(spk_count_yes_screen(:,3)<spk_count_no_screen(:,3) & sum(sig_selective(:,1:3),2)>0); % no perferring neurons
if size(i_selR_unit,1)>=5 & size(i_selL_unit,1)>=5
R_neurons_yes_iSesssions = [];
L_neurons_yes_iSesssions = [];
R_neurons_no_iSesssions = [];
L_neurons_no_iSesssions = [];
n_plot = 0;
for i_trial = i_yes_correct_trial'
n_plot = n_plot+1;
% mean activity of yes and no perferring neurons
R_neurons = mean(activity_matrix(i_selR_unit,i_timebin,i_trial)-average_activity(i_selR_unit,i_timebin));
L_neurons = mean(activity_matrix(i_selL_unit,i_timebin,i_trial)-average_activity(i_selL_unit,i_timebin));
corr_r_yes(end+1,1) = corr(R_neurons',L_neurons');
R_neurons_yes_iSesssions(end+1,:) = R_neurons;
L_neurons_yes_iSesssions(end+1,:) = L_neurons;
end
for i_trial = i_no_correct_trial'
n_plot = n_plot+1;
% mean activity of yes and no perferring neurons
R_neurons = mean(activity_matrix(i_selR_unit,i_timebin,i_trial)-average_activity(i_selR_unit,i_timebin));
L_neurons = mean(activity_matrix(i_selL_unit,i_timebin,i_trial)-average_activity(i_selL_unit,i_timebin));
corr_r_no(end+1,1) = corr(R_neurons',L_neurons');
R_neurons_no_iSesssions(end+1,:) = R_neurons;
L_neurons_no_iSesssions(end+1,:) = L_neurons;
end
R_neurons_singleTrials_yes_allSesssions = cat(1,R_neurons_singleTrials_yes_allSesssions, R_neurons_yes_iSesssions);
L_neurons_singleTrials_yes_allSesssions = cat(1,L_neurons_singleTrials_yes_allSesssions, L_neurons_yes_iSesssions);
R_neurons_singleTrials_no_allSesssions = cat(1,R_neurons_singleTrials_no_allSesssions, R_neurons_no_iSesssions);
L_neurons_singleTrials_no_allSesssions = cat(1,L_neurons_singleTrials_no_allSesssions, L_neurons_no_iSesssions);
session_ID_yes = cat(1,session_ID_yes,ones(size(R_neurons_yes_iSesssions,1),1)*i_session);
session_ID_no = cat(1,session_ID_no,ones(size(L_neurons_no_iSesssions,1),1)*i_session);
population_sel1 = abs(mean(R_neurons_yes_iSesssions(:,end))-mean(R_neurons_no_iSesssions(:,end)));
population_sel2 = abs(mean(L_neurons_yes_iSesssions(:,end))-mean(L_neurons_no_iSesssions(:,end)));
if population_sel1>2 & population_sel2>2
i_session_selected = [i_session_selected; i_session];
n_R_list(end+1,:) = size(i_selR_unit,1);
n_L_list(end+1,:) = size(i_selL_unit,1);
hold on
plot(mean(R_neurons_yes_iSesssions),mean(L_neurons_yes_iSesssions),'b');
plot(mean(R_neurons_yes_iSesssions(:,end)),mean(L_neurons_yes_iSesssions(:,end)),'ow','markerfacecolor','b');
plot(mean(R_neurons_no_iSesssions),mean(L_neurons_no_iSesssions),'r');
plot(mean(R_neurons_no_iSesssions(:,end)),mean(L_neurons_no_iSesssions(:,end)),'ow','markerfacecolor','r');
end
end
end
title('All sessions')
xlabel('Lick right pref. neurons')
ylabel('Lick left pref. neurons')
% i_session = 8;
% i_session = 9;
i_session = 11;%21;
% lick right trials
figure;
R_neurons = R_neurons_singleTrials_yes_allSesssions(session_ID_yes==i_session,:);
L_neurons = L_neurons_singleTrials_yes_allSesssions(session_ID_yes==i_session,:);
corr_r_yes_tmp = corr_r_yes(session_ID_yes==i_session,:);
[dummy i_sort] = sort(corr_r_yes_tmp);
n_plot = 0;
for i_trial = i_sort(1:5)'
n_plot = n_plot+1;
subplot(4,5,n_plot); hold on
plot(time_bins(i_timebin),R_neurons(i_trial,:),'b');
plot(time_bins(i_timebin),L_neurons(i_trial,:),'r');
subplot(4,5,n_plot+5); hold on
plot(R_neurons(i_trial,:), L_neurons(i_trial,:),'b');
plot(R_neurons(i_trial,end), L_neurons(i_trial,end),'ow','markerfacecolor','b');
end
% lick left trials
R_neurons = R_neurons_singleTrials_no_allSesssions(session_ID_no==i_session,:);
L_neurons = L_neurons_singleTrials_no_allSesssions(session_ID_no==i_session,:);
corr_r_no_tmp = corr_r_no(session_ID_no==i_session,:);
[dummy i_sort] = sort(corr_r_no_tmp);
n_plot = 0;
for i_trial = i_sort(1:5)'
n_plot = n_plot+1;
subplot(4,5,n_plot+10); hold on
plot(time_bins(i_timebin),R_neurons(i_trial,:),'b');
plot(time_bins(i_timebin),L_neurons(i_trial,:),'r');
subplot(4,5,n_plot+15); hold on
plot(R_neurons(i_trial,:), L_neurons(i_trial,:),'r');
plot(R_neurons(i_trial,end), L_neurons(i_trial,end),'ow','markerfacecolor','r');
end
subplot(4,5,1);
title('Example Lick Right trials')
xlabel('Time (s)')
ylabel('delta FR (spk/s)')
subplot(4,5,5)
legend('Lick right pref neurons','Lick left pref neurons')
subplot(4,5,6);
xlabel('Right pref neurons')
ylabel('Left pref neurons')
subplot(4,5,11);
title('Example Lick Left trials')
figure(10); hold on
[y x] = hist(corr_r_yes(ismember(session_ID_yes,i_session_selected)));
plot(x,y/sum(y),'b');
[y x] = hist(corr_r_no(ismember(session_ID_no,i_session_selected)));
plot(x,y/sum(y),'r');
xlabel('Correlation between Left and Right perf neurons')
ylabel('Number of trials')
disp(['======================']);
disp(['yes: ',num2str(mean(corr_r_yes(ismember(session_ID_yes,i_session_selected)))),'+',num2str(std(corr_r_yes(ismember(session_ID_yes,i_session_selected)))/sqrt(size(corr_r_yes(ismember(session_ID_yes,i_session_selected)),1)))]);
disp(['no: ',num2str(mean(corr_r_no(ismember(session_ID_no,i_session_selected)))),'+',num2str(std(corr_r_no(ismember(session_ID_no,i_session_selected)))/sqrt(size(corr_r_no(ismember(session_ID_no,i_session_selected)),1)))]);
%% Shuffle control, random groups of neurons
corr_r_yes = [];
corr_r_no = [];
R_neurons_singleTrials_yes_allSesssions = [];
L_neurons_singleTrials_yes_allSesssions = [];
R_neurons_singleTrials_no_allSesssions = [];
L_neurons_singleTrials_no_allSesssions = [];
session_ID_yes = [];
session_ID_no = [];
R_neurons_yes_allSesssions = [];
L_neurons_yes_allSesssions = [];
R_neurons_no_allSesssions = [];
L_neurons_no_allSesssions = [];
for i_session = i_session_selected'%1:size(activity_matrix_allSession,1)
i_timebin = find(time_bins>-3.2 & time_bins<=-.4); %only use the LDA before cue
activity_matrix = activity_matrix_allSession{i_session};
spk_count_yes_screen = spk_count_yes_screen_allSession{i_session,1}; % neuron x 5, [whole_trial sample delay resposne]
spk_count_no_screen = spk_count_no_screen_allSession{i_session,1};
sig_selective = sig_selective_allSession{i_session,1};
i_yes_screen_trial = i_yes_screen_allSession{i_session,1};
i_no_screen_trial = i_no_screen_allSession{i_session,1};
i_yes_correct_trial = i_yes_correct_allSession{i_session,1}; % indices for non-stim trials, independent test data only
i_no_correct_trial = i_no_correct_allSession{i_session,1};
i_yes_error_trial = i_yes_error_allSession{i_session,1};
i_no_error_trial = i_no_error_allSession{i_session,1};
average_activity = nanmean(activity_matrix(:,:,[i_yes_screen_trial i_no_screen_trial]),3); % average spike rate across all trials types, this will be substracted at each time point (detrending the data) to examine competition dynamics more closely
sig_selective = sig_selective<.01; % selective neurons only
i_sell_unit = find(sum(sig_selective(:,1:3),2)>0);
i_selR_unit = i_sell_unit(1:round(length(i_sell_unit)/2)); % yes perferring neurons, this is not yet flipped to contra/ipsi
i_selL_unit = i_sell_unit(round(length(i_sell_unit)/2)+1:end); % no perferring neurons
if size(i_selR_unit,1)>=5 & size(i_selL_unit,1)>=5
R_neurons_yes_iSesssions = [];
L_neurons_yes_iSesssions = [];
R_neurons_no_iSesssions = [];
L_neurons_no_iSesssions = [];
for i_trial = i_yes_correct_trial'
% mean activity of yes and no perferring neurons
R_neurons = mean(activity_matrix(i_selR_unit,i_timebin,i_trial)-average_activity(i_selR_unit,i_timebin));
L_neurons = mean(activity_matrix(i_selL_unit,i_timebin,i_trial)-average_activity(i_selL_unit,i_timebin));
corr_r_yes(end+1,1) = corr(R_neurons',L_neurons');
R_neurons_yes_iSesssions(end+1,:) = R_neurons;
L_neurons_yes_iSesssions(end+1,:) = L_neurons;
end
for i_trial = i_no_correct_trial'
% mean activity of yes and no perferring neurons
R_neurons = mean(activity_matrix(i_selR_unit,i_timebin,i_trial)-average_activity(i_selR_unit,i_timebin));
L_neurons = mean(activity_matrix(i_selL_unit,i_timebin,i_trial)-average_activity(i_selL_unit,i_timebin));
corr_r_no(end+1,1) = corr(R_neurons',L_neurons');
R_neurons_no_iSesssions(end+1,:) = R_neurons;
L_neurons_no_iSesssions(end+1,:) = L_neurons;
end
R_neurons_singleTrials_yes_allSesssions = cat(1,R_neurons_singleTrials_yes_allSesssions, R_neurons_yes_iSesssions);
L_neurons_singleTrials_yes_allSesssions = cat(1,L_neurons_singleTrials_yes_allSesssions, L_neurons_yes_iSesssions);
R_neurons_singleTrials_no_allSesssions = cat(1,R_neurons_singleTrials_no_allSesssions, R_neurons_no_iSesssions);
L_neurons_singleTrials_no_allSesssions = cat(1,L_neurons_singleTrials_no_allSesssions, L_neurons_no_iSesssions);
session_ID_yes = cat(1,session_ID_yes,ones(size(R_neurons_yes_iSesssions,1),1)*i_session);
session_ID_no = cat(1,session_ID_no,ones(size(L_neurons_no_iSesssions,1),1)*i_session);
end
end
figure(10); hold on
[y x] = hist([corr_r_yes; corr_r_no]);
plot(x,y/sum(y),'k');
xlabel('Correlation between Left and Right perf neurons')
ylabel('Number of trials')
legend('Lick right trials, R vs. L pop.','Lick right trials, R vs. L pop.','Null, shuffl neuron grouping')
disp(['shuffle: ',num2str(mean([corr_r_yes; corr_r_no])),'+',num2str(std([corr_r_yes; corr_r_no])/sqrt(size([corr_r_yes; corr_r_no],1)))]);