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@@ -0,0 +1,479 @@
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+function figure_neurotrace(fld)
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+
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+fprintf('\n======================================================\n');
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+fprintf('-- Creating Figure neurotrace --\n');
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+fprintf('======================================================\n');
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+
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+%% settings
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+green = [23, 105, 13]./255;
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+red = [201, 0, 34]./255;
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+cols = [green; 0.3 0.3 0.3; red; 1.00,0.84,0.00];
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+
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+snrthres = 2.5;
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+smoothfact = 15; mindays = 3;
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+falpha = 1; % set to 1 for converting to illustrator
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+
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+do_cleantraces = true; % if true we throw out artefact traces
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+% citerion: avg emplitude in first 100ms after stimulus onset should be
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+% at least 3 times the standard deviation in the preceding 100 ms
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+do_latency = true;
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+
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+%% load RTs
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+if exist(fullfile(fld.basedir, 'results','figure_behavior','RTs.mat'),'file')
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+ load(fullfile(fld.basedir, 'results','figure_behavior','RTs.mat'),'RTs');
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+else
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+ error('RTs missing, run figure_behavior.m first');
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+end
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+
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+%% plots
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+% load traces
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+monkeys = {'M1','M2'};
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+wm = [];
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+
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+%% plot
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+f1 = figure;
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+set(f1,'Position',[500 500 1600 800])
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+% individual animals
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+for mi = 1:2
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+ monkey = monkeys{mi};
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+ savedir = fullfile(fld.basedir,'results','figure_neurotrace');
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+ load(fullfile(savedir, [monkey '_averages_snr' num2str(snrthres) '_mindays' num2str(mindays) '.mat']),...
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+ 'traces','tracesLUT','grandtraces','grandtracesLUT','env_t');
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+
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+ traces_ALL{mi} = traces; %#ok<*AGROW,*NASGU>
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+ tracesLUT_ALL{mi} = tracesLUT;
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+ grandtraces_ALL{mi} = grandtraces;
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+ grandtracesLUT_ALL{mi} = grandtracesLUT;
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+ env_t_ALL{mi} = env_t;
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+
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+ figure(f1); subplot(2,3,mi+1); hold on;
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+ mns = []; sems = []; trcs = cell(0);
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+ for cond = 1:3
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+
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+ if do_cleantraces
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+ incl = grandtracesLUT(:,2)==cond;
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+ get_traces = grandtraces(incl,:);
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+ sel_LUT = grandtracesLUT(incl,:);
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+ incl_ALL{mi,cond} = incl;
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+
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+ prewin = env_t > -0.1 & env_t < 0;
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+ postwin = env_t > 0 & env_t < 0.1;
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+ sd_pre = std(get_traces(:,prewin),0,2);
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+ m_post = mean(get_traces(:,postwin),2);
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+ snr_sel = m_post > 3*sd_pre;
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+
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+ incl = incl(snr_sel');
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+ get_traces = get_traces(snr_sel',:);
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+ newLUT = sel_LUT(snr_sel',:);
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+
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+ % now average sites over sessions
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+ get_traces_ = get_traces; % backup
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+ get_traces = [];
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+ for rs = unique(newLUT(:,1))'
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+ if sum(newLUT(:,1)==rs)>1
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+ get_traces = [get_traces;...
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+ mean(get_traces_(newLUT(:,1)==rs,:))];
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+ else
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+ get_traces = [get_traces; get_traces_(newLUT(:,1)==rs,:)];
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+ end
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+ end
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+ recsites{mi,cond}=unique(newLUT(:,1));
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+ else
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+ incl = tracesLUT(:,2)==cond;
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+ get_traces = traces(incl,:);
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+ sel_LUT = tracesLUT(incl,:);
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+ incl_ALL{mi,cond} = incl;
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+
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+ newLUT = sel_LUT;
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+ recsites{mi,cond}=unique(newLUT(:,1));
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+ end
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+
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+ % calculate the mean across channels
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+ s_traces = [];
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+ for t = 1:size(get_traces,1)
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+ s_traces(t,:) = smooth(get_traces(t,:),smoothfact);
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+ end
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+ s_traces_ALL{mi,cond} = s_traces;
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+
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+ mn = mean(s_traces);
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+ mns(cond,:) = mn; % for calculating the difference between conditions
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+ trcs{cond} = s_traces; % for calculating the modulation onset
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+ sem = std(s_traces)./sqrt(sum(incl));
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+ sems(cond,:) = sem;
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+
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+ t2 = [env_t*1e3, fliplr(env_t*1e3)];
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+ inBetween = [mn+sem, fliplr(mn-sem)];
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+ fill(t2, inBetween, 'g','LineStyle','none','FaceColor',cols(cond,:),...
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+ 'FaceAlpha',falpha); hold all
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+ plot(env_t*1e3,mn,'Color',cols(cond,:));
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+
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+ RT=RTs{mi}(cond);
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+ plot([RT RT],[0 1],'--','Color',cols(cond,:),'LineWidth',2)
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+
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+ xlim([0 0.25*1e3]); ylim([0 1]);
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+ end
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+ title(monkeys{mi}); xlabel('time(ms)'); ylabel('MUA');
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+
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+
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+ % only include sites with all conditions
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+ if size(recsites{mi,1},1) <= size(recsites{mi,2},1) && ...
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+ size(recsites{mi,1},1) <= size(recsites{mi,3},1)
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+ rsidx = [1 2 3];
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+ elseif size(recsites{mi,2},1) < size(recsites{mi,1},1) && ...
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+ size(recsites{mi,2},1) < size(recsites{mi,3},1)
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+ rsidx = [2 1 3];
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+ elseif size(recsites{mi,3},1) < size(recsites{mi,1},1) && ...
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+ size(recsites{mi,3},1) < size(recsites{mi,2},1)
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+ rsidx = [3 1 2];
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+ end
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+ rsinc{mi,rsidx(1)} = ismember(recsites{mi,rsidx(1)},recsites{mi,rsidx(1)});
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+ rsinc{mi,rsidx(2)} = ismember(recsites{mi,rsidx(2)},recsites{mi,rsidx(1)});
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+ rsinc{mi,rsidx(3)} = ismember(recsites{mi,rsidx(3)},recsites{mi,rsidx(1)});
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+
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+ inRF = {'target','','distractor'};
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+ for cond = [1 3 4]
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+ figure(f1)
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+ subplot(2,3,mi+4);
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+ if cond < 4
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+ df = trcs{cond}(rsinc{mi,cond},:) - ...
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+ trcs{2}(rsinc{mi,2},:); % difference for each channel separately
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+ mn = mean(df);
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+ csem = std(trcs{cond}(rsinc{mi,cond},:) - ...
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+ trcs{2}(rsinc{mi,2},:))./...
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+ sqrt(size(trcs{cond}(rsinc{mi,cond},:),1));
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+ else
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+ df = trcs{1}(rsinc{mi,1},:) - ...
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+ trcs{3}(rsinc{mi,3},:);
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+ mn = mean(df);
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+ csem = std(trcs{1}(rsinc{mi,1},:) - ...
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+ trcs{3}(rsinc{mi,3},:))./...
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+ sqrt(size(trcs{1}(rsinc{mi,1},:),1));
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+ end
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+
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+ inBetween = [mn+csem, fliplr(mn-csem)];
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+ fill(t2, inBetween, 'g','LineStyle','none','FaceColor',cols(cond,:),...
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+ 'FaceAlpha',falpha); hold all
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+ plot(env_t*1e3,mn,'Color',cols(cond,:));
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+ xlim([0 0.25*1e3]); ylim([-0.15 0.25]);
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+
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+ % let's calculate the latency of modulation per channel
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+ if do_latency
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+ do_latfig=0;
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+ if mi==1
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+ until = find(env_t<0.25,1,'last');
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+ else
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+ until = find(env_t<0.21,1,'last');
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+ end
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+ lat = []; coeff = []; rs = []; converge = [];
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+ for i = 1:size(df,1) % loop channels
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+ cur_resp = df(i,1:until)';
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+ if cond==3
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+ % the latency function cannot fit downward modulation
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+ cur_resp = cur_resp*-1;
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+ end
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+ [lat(i),~] = latencyfit_jp(cur_resp,env_t(1:until)'*1000,do_latfig);
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+ end
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+
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+ % latency on average
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+ cur_resp = mn(1:until)';
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+ if cond==3
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+ % the latency function cannot fit downward modulation
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+ cur_resp = cur_resp*-1;
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+ end
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+ lt = latencyfit_jp(smooth(cur_resp,20),env_t(1:until)'*1000,do_latfig);
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+
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+ if cond < 4
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+ rng('default') % for reproducibility
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+ nsamp = 100;
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+ btstrp_LT = bootstrp(nsamp,@latencyfit_jp,smooth(cur_resp,20),env_t(1:until)'*1000,0);
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+ fprintf([monkey ' ' inRF{cond} ' - Bootstrapped latency [' num2str(nsamp) ' samples] =====\n'])
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+ fprintf(['mean: ' num2str(mean(btstrp_LT,'omitnan')) ', std:' num2str(std(btstrp_LT,'omitnan')) '\n'])
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+ BLT{mi,cond}=btstrp_LT;
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+ end
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+
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+ if ~isnan(lt) % if lt = nan, latencyfit_jp gives no figure, so we shouldn't make a title either
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+ if cond < 4
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+ title([monkey ', fit on average response, ' inRF{cond} ' in RF']);
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+ else
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+ title([monkey ', fit on average response modulation']);
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+ end
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+ end
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+
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+ if cond < 4
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+ disp([monkey ', latency of modulation onset when ' inRF{cond} ' is in RF: ' ...
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+ num2str(round(mean(lat,'omitnan'))) 'ms (' num2str(round(lt)) ...
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+ 'ms as estimated on the average response)']);
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+ else
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+ disp([monkey ', latency of targ-distr modulation: ' ...
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+ num2str(round(mean(lat,'omitnan'))) 'ms (' num2str(round(lt)) ...
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+ 'ms as estimated on the average response)']);
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+ end
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+ %disp(['individual channel estimates: ' num2str(lat)]);
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+
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+ ModLatency{mi,cond} = lt;
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+ figure(f1);
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+ plot([lt lt],[-0.2 0.25],'--','Color',cols(cond,:),'LineWidth',2)
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+ if isnan(lt)
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+ plot([round(mean(lat,'omitnan')) round(mean(lat,'omitnan'))],[-0.2 0.25],...
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+ ':','Color',cols(cond,:),'LineWidth',2)
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+ end
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+ end
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+ end
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+ figure(f1)
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+ plot([0 .25*1e3],[0 0],'k');
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+ title(monkeys{mi}); xlabel('time(ms)'); ylabel('Modulation(MUA)');
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+
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+ % save the extra cleaned traces
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+ save(fullfile(savedir,'Traces_SNR_trialbased.mat'),...
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+ 's_traces_ALL','recsites','rsinc');
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+
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+ %% statistics
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+ window_means = [];
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+ win_idx = find(env_t>0.15 & env_t<0.20);
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+
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+ for cond = 1:3
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+ if do_cleantraces
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+ incl = grandtracesLUT(:,2)==cond;
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+ get_traces = grandtraces(incl,win_idx); %#ok<*FNDSB>
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+ else
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+ incl = tracesLUT(:,2)==cond;
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+ get_traces = traces(incl,win_idx);
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+ end
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+ incl = tracesLUT(:,2)==cond;
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+
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+ % calculate the average in a window
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+ s_traces = [];
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+ for t = 1:size(get_traces,1)
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+ s_traces(t,:) = smooth(get_traces(t,:),smoothfact);
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+ end
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+ window_means(:,cond) = mean(s_traces,2);
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+ end
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+ anova1(window_means);
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+
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+ wm = [wm; window_means];
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+ wm2{mi} = window_means;
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+end
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+
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+figure(f1); subplot(2,3,1); hold on;
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+% pooled animals
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+for cond = 1:3
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+ traces = [s_traces_ALL{1,cond};s_traces_ALL{2,cond}];
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+
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+ % calculate the mean across channels
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+ % get_traces = traces(incl,:);
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+ get_traces = traces;
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+ s_traces = [];
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+ for t = 1:size(get_traces,1)
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+ s_traces(t,:) = smooth(get_traces(t,:),smoothfact);
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+ end
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+
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+ mn = mean(s_traces);
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+ mns(cond,:) = mn; % for calculating the difference between conditions
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+ trcs{cond} = s_traces; % for calculating the modulation onset
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+ sem = std(s_traces)./sqrt(sum(size(s_traces,1)));
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+ sems(cond,:) = sem;
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+
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+ t2 = [env_t*1e3, fliplr(env_t*1e3)];
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+ inBetween = [mn+sem, fliplr(mn-sem)];
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+ fill(t2, inBetween, 'g','LineStyle','none','FaceColor',cols(cond,:),...
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+ 'FaceAlpha',falpha); hold all
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+
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+ RT=RTs{3}(cond);
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+ plot([RT RT],[0 1],'--','Color',cols(cond,:),'LineWidth',2)
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+ plot(env_t*1e3,mn,'Color',cols(cond,:));
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+ xlim([0 0.25*1e3]); ylim([0 1]);
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+end
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+title('BOTH animals pooled'); xlabel('time(ms)'); ylabel('MUA');
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+
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+inRF = {'target','','distractor'};
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+
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+for cond=1:3
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+ rsinc{3,cond} = [rsinc{1,cond};rsinc{2,cond}];
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+end
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+
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+for cond = [1 3 4]
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+ figure(f1)
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+ subplot(2,3,4);
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+
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+ if cond < 4
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+ df = trcs{cond}(rsinc{3,cond},:) - ...
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+ trcs{2}(rsinc{3,2},:); % difference for each channel separately
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+ mn = mean(df);
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+ csem = std(trcs{cond}(rsinc{3,cond},:) - ...
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+ trcs{2}(rsinc{3,2},:))./...
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+ sqrt(size(trcs{cond}(rsinc{3,cond},:),1));
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+ else
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+ df = trcs{1}(rsinc{3,1},:) - ...
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+ trcs{3}(rsinc{3,3},:);
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+ mn = mean(df);
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+ csem = std(trcs{1}(rsinc{3,1},:) - ...
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+ trcs{3}(rsinc{3,3},:))./...
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+ sqrt(size(trcs{1}(rsinc{3,1},:),1));
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+ end
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+
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+ inBetween = [mn+csem, fliplr(mn-csem)];
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+ fill(t2, inBetween, 'g','LineStyle','none','FaceColor',cols(cond,:),...
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+ 'FaceAlpha',falpha); hold all
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+ plot(env_t*1e3,mn,'Color',cols(cond,:));
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+ xlim([0 0.25*1e3]); ylim([-0.15 0.25]);
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+
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+ % let's calculate the latency of modulation per channel
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+ if do_latency
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+ do_latfig=0;
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+ if mi==1
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+ until = find(env_t<0.25,1,'last');
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+ else
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+ until = find(env_t<0.21,1,'last');
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+ end
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+ lat = []; coeff = []; rs = []; converge = [];
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+ for i = 1:size(df,1) % loop channels
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+ cur_resp = df(i,1:until)';
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+ if cond==3
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+ % the latency function cannot fit downward modulation
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+ cur_resp = cur_resp*-1;
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+ end
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+ [lat(i),~] = latencyfit_jp(cur_resp,env_t(1:until)'*1000,do_latfig);
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+ end
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+
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+ %collect latencty
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+ clat{cond} = lat;
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+
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+ % latency on average
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+ do_latfig=1;
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+ cur_resp = mn(1:until)';
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+ if cond==3
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+ % the latency function cannot fit downward modulation
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+ cur_resp = cur_resp*-1;
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+ end
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+ lt = latencyfit_jp(smooth(cur_resp,20),env_t(1:until)'*1000,do_latfig);
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+
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+ if cond < 4
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+ rng('default') % for reproducibility
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+ nsamp = 100;
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+ btstrp_LT = bootstrp(nsamp,@latencyfit_jp,smooth(cur_resp,20),env_t(1:until)'*1000,0);
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+ fprintf([inRF{cond} ' - Bootstrapped latency [' num2str(nsamp) ' samples] =====\n'])
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+ fprintf(['mean: ' num2str(mean(btstrp_LT,'omitnan')) ', std:' num2str(std(btstrp_LT,'omitnan')) '\n'])
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+ BLT{3,cond}=btstrp_LT;
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+ end
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+
|
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+ if ~isnan(lt) % if lt = nan, latencyfit_jp gives no figure, so we shouldn't make a title either
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+ if cond < 4
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+ title(['BOTH monkeys, fit on average response, ' inRF{cond} ' in RF']);
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+ else
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+ title('BOTH monkeys, fit on average response modulation');
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+ end
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+ end
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+ if cond < 4
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+ disp(['BOTH monkeys, latency of modulation onset when ' inRF{cond} ' is in RF: ' ...
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+ num2str(round(mean(lat,'omitnan'))) 'ms (' num2str(round(lt)) ...
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+ 'ms as estimated on the average response)']);
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+ else
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+ disp(['BOTH monkeys, latency of targ-distr modulation: ' ...
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+ num2str(round(mean(lat,'omitnan'))) 'ms (' num2str(round(lt)) ...
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+ 'ms as estimated on the average response)']);
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+ end
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+ %disp(['individual channel estimates: ' num2str(lat)]);
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+
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+ figure(f1)
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+ plot([lt lt],[-0.2 0.25],'--','Color',cols(cond,:),'LineWidth',2)
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+ if isnan(lt)
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+ plot([round(mean(lat,'omitnan')) round(mean(lat,'omitnan'))],[-0.2 0.25],...
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+ ':','Color',cols(cond,:),'LineWidth',2)
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+ end
|
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+
|
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+ end
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+end
|
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+figure(f1)
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+plot([0 .25*1e3],[0 0],'k');
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+title('ALL animals pooled'); xlabel('time(ms)'); ylabel('Modulation (MUA)');
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+
|
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+%% do a bootstrapped latency analysis to get some stats
|
|
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+rng('default') % for reproducibility
|
|
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+nsamp = 1000;
|
|
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+btstrp_LAT = bootstrp(nsamp,@nanmean,[clat{1}' clat{3}']);
|
|
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+[h,p,ci,stats]=ttest(btstrp_LAT(:,1)-btstrp_LAT(:,2));
|
|
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+fprintf('== BTSTRP comparison of latencies ===\n');
|
|
|
+fprintf(['Mean difference in latency (T-SD): ' ...
|
|
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+ num2str(mean(btstrp_LAT(:,1)-btstrp_LAT(:,2))) ', SEM: ' ...
|
|
|
+ num2str(std(btstrp_LAT(:,1)-btstrp_LAT(:,2))./sqrt(size(btstrp_LAT,1))) ...
|
|
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+ 'ms\n']);
|
|
|
+fprintf([num2str(sum((btstrp_LAT(:,1)-btstrp_LAT(:,2))<0)) '/' num2str(nsamp) ...
|
|
|
+ ' T before SD, p = ' num2str(sum((btstrp_LAT(:,1)-btstrp_LAT(:,2))>0)/nsamp) ...
|
|
|
+ ' that H0[T not before SD] is rejected \n']);
|
|
|
+fprintf(['ttest t(' num2str(stats.df) ') = ' num2str(stats.tstat) ...
|
|
|
+ ', p = ' num2str(p) '\n']);
|
|
|
+
|
|
|
+%% use the bootstrapped latency estimates for stats
|
|
|
+fprintf('== Comparison of bootstrapped T-SD latencies ===\n');
|
|
|
+for mi=1:3
|
|
|
+ [h,p,ci,stats]=ttest(BLT{mi,1}-BLT{mi,3});
|
|
|
+ nn = sum(~isnan());
|
|
|
+ if mi<3
|
|
|
+ fprintf(['-- Monkey ' num2str(mi) ' --\n']);
|
|
|
+ else
|
|
|
+ fprintf('-- BOTH Monkeys --\n');
|
|
|
+ end
|
|
|
+ fprintf(['Mean difference in latency (T-SD): ' ...
|
|
|
+ num2str(mean(BLT{mi,1}-BLT{mi,3},'omitnan')) ', SEM: ' ...
|
|
|
+ num2str(std(BLT{mi,1}-BLT{mi,3},'omitnan')./sqrt(sum(~isnan(BLT{mi,1})))) ...
|
|
|
+ 'ms\n']);
|
|
|
+ fprintf([num2str(sum((BLT{mi,1}-BLT{mi,3})<0)) '/' num2str(100) ...
|
|
|
+ ' T before SD, p = ' num2str(sum((BLT{mi,1}-BLT{mi,3})>0)/100) ...
|
|
|
+ ' that H0[T not before SD] is rejected \n']);
|
|
|
+ fprintf(['ttest t(' num2str(stats.df) ') = ' num2str(stats.tstat) ...
|
|
|
+ ', p = ' num2str(p) '\n']);
|
|
|
+end
|
|
|
+
|
|
|
+%% statistics
|
|
|
+% do a simple paired t-test for each monkey
|
|
|
+fprintf('=============================\n');
|
|
|
+fprintf(' Paired T-test \n');
|
|
|
+fprintf('=============================\n');
|
|
|
+clear h p stats
|
|
|
+for mi = 1:2
|
|
|
+ mns = wm2{mi};
|
|
|
+ p = []; vp=[]; ci=1;
|
|
|
+ for cond = [1 3]
|
|
|
+ [vh,vp(end+1)]=vartest2(mns(:,cond),mns(:,2));
|
|
|
+ [h,p(end+1),~,stats{mi,ci}] = ttest(mns(:,cond), mns(:,2));
|
|
|
+ ci=ci+1;
|
|
|
+ end
|
|
|
+ [vh,vp(end+1)]=vartest2(mns(:,1),mns(:,3));
|
|
|
+ [h,p(end+1),~,stats{mi,3}] = ttest(mns(:,1), mns(:,3));
|
|
|
+
|
|
|
+ disp([monkeys{mi} ', target response increased: p=' num2str(p(1)) ...
|
|
|
+ ' t = ' num2str(stats{mi,1}.tstat) ', df = ' num2str(stats{mi,1}.df) ...
|
|
|
+ ' (unequal var p = ' num2str(vp(1)) ')']);
|
|
|
+ disp([monkeys{mi} ', distractor response decreased: p=' num2str(p(2)) ...
|
|
|
+ ' t = ' num2str(stats{mi,2}.tstat) ', df = ' num2str(stats{mi,2}.df) ...
|
|
|
+ ' (unequal var p = ' num2str(vp(2)) ')']);
|
|
|
+ disp([monkeys{mi} ', targ vs distractor: p=' num2str(p(3)) ...
|
|
|
+ ' t = ' num2str(stats{mi,3}.tstat) ', df = ' num2str(stats{mi,3}.df) ...
|
|
|
+ ' (unequal var p = ' num2str(vp(3)) ')']);
|
|
|
+end
|
|
|
+
|
|
|
+%Both together
|
|
|
+mns = [wm2{1}; wm2{2}];
|
|
|
+p = [];vp=[]; ci=1;
|
|
|
+for cond = [1 3]
|
|
|
+ [vh,vp(end+1)]=vartest2(mns(:,cond),mns(:,2));
|
|
|
+ [h,p(end+1),~,stats{3,ci}] = ttest(mns(:,cond), mns(:,2));
|
|
|
+ ci=ci+1;
|
|
|
+end
|
|
|
+[vh,vp(end+1)]=vartest2(mns(:,1),mns(:,3));
|
|
|
+[h,p(end+1),~,stats{3,3}] = ttest(mns(:,1), mns(:,3));
|
|
|
+
|
|
|
+disp(['BOTH, target response increased: p=' num2str(p(1)) ...
|
|
|
+ ' t = ' num2str(stats{3,1}.tstat) ', df = ' num2str(stats{3,1}.df) ...
|
|
|
+ ' (unequal var p = ' num2str(vp(1)) ')']);
|
|
|
+disp(['BOTH, distractor response decreased: p=' num2str(p(2)) ...
|
|
|
+ ' t = ' num2str(stats{3,2}.tstat) ', df = ' num2str(stats{3,2}.df) ...
|
|
|
+ ' (unequal var p = ' num2str(vp(2)) ')']);
|
|
|
+disp(['BOTH, targ vs distractor: p=' num2str(p(3)) ...
|
|
|
+ ' t = ' num2str(stats{3,3}.tstat) ', df = ' num2str(stats{3,3}.df) ...
|
|
|
+ ' (unequal var p = ' num2str(vp(3)) ')']);
|
|
|
+
|
|
|
+
|
|
|
+%% save figure
|
|
|
+figure(f1)
|
|
|
+saveas(gcf,fullfile(savedir,'figure_neurotrace.svg'));
|
Chris Klink