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- root='/ephys/2/Cecilia_TWF/Analysis'
- if ~exist(root,'dir')
- root=['/home/colliculus/' root]
- end
- cd([root '/Analysis_AC_ECoG_TWF_ITD_SparseData/decoding'])
- addpath(root)
- fsz = 10; % font size
- % working on Cecilia's desktop
- % cd Z:/ephys/2/Cecilia_TWF/Analysis/Analysis_AC_ECoG_TWF_ITD_SparseData/decoding
- % addpath('Z:/ephys/2/Cecilia_TWF/Analysis')
- %% compute average 1-30ms for naive rats
- temp=dir('TWF*twin_perm.mat');
- all_decoding_avg=[];
- all_decoding_perm=[];
- for i=1:length(temp)
- load(temp(i).name);
-
- all_decoding_avg(i,:)=mean(dist_all,2);
- for n=1:no_iter
- all_decoding_perm(i,n,:)=mean(dist_all.*reshape(randsample([-1 1],size(dist_all,1)*size(dist_all,2),'true'),size(dist_all)),2);
- end
- end
- confint_99_avg=squeeze(mean(prctile(all_decoding_perm,[.5 99.5],2),1));
- clear ps
- for i=1:4
- [ps(i) h stats]=signrank(all_decoding_avg(:,i),0,'method','approximate');
- zs(i) = stats.zval;
- end
- for i=1:4
- for j=1:4
- ps2(i,j)=signrank(all_decoding_avg(:,i),all_decoding_avg(:,j));
- end
- end
- all_decoding_avg_naive = all_decoding_avg;
- ps_naive = ps;
- %% compute average 1-30ms for naive rats
- temp=dir('trained*twin_perm.mat');
- all_decoding_avg=[];
- all_decoding_perm=[];
- for i=1:length(temp)
- load(temp(i).name);
-
- all_decoding_avg(i,:)=mean(dist_all,2);
- for n=1:no_iter
- all_decoding_perm(i,n,:)=mean(dist_all.*reshape(randsample([-1 1],size(dist_all,1)*size(dist_all,2),'true'),size(dist_all)),2);
- end
- end
- confint_99_avg=squeeze(mean(prctile(all_decoding_perm,[.5 99.5],2),1));
- clear ps
- for i=1:4
- [ps(i) h stats]=signrank(all_decoding_avg(:,i),0,'method','approximate');
- zs(i) = stats.zval;
- end
- for i=1:4
- for j=1:4
- ps2(i,j)=signrank(all_decoding_avg(:,i),all_decoding_avg(:,j));
- end
- end
- all_decoding_avg_trained = all_decoding_avg;
- ps_trained = ps;
- %% Now plot
- figure('units','centimeters','position',[1,1,15,13]); clf;
- %
- ax1=subplot(1,2,1)
- set(ax1,'fontsize',fsz)
- cols={'b' 'r' 'm' [.5 .5 .5]};
- hold on
- for i=1:4
- bar(i,mean(all_decoding_avg_naive(:,i),1),'facecolor',cols{i},'linestyle','none')
- line([i i],[mean(all_decoding_avg_naive(:,i),1)-std(all_decoding_avg_naive(:,i),1)/sqrt(14) mean(all_decoding_avg_naive(:,i),1)+std(all_decoding_avg_naive(:,i),1)/sqrt(14)],'color','k')
- end
- xlim([0 5])
- ylabel('decoding (a.u.)','fontsize',fsz)
- xlabel('click','fontsize',fsz)
- xticklabels({' ','1','2','3','4',' '})
- ylim([-2 14]*10e-4)
- scatter(find(ps_naive<.05),mean(all_decoding_avg_naive(:,find(ps_naive<.05)),1)*1.4,10,'k*')
- t1=title('naive rats','fontsize',fsz)
- p=get(t1,'pos'); p(2)=p(2)*1.07; set(t1,'pos',p)
- p=get(ax1,'pos'); p(2)=p(2)*1.1; p(4)=p(4)*.9; set(ax1,'pos',p)
- xl=xlim();xw=xl(2)-xl(1); lx=xl(1)-xw*0.10;
- yl=ylim();yw=yl(2)-yl(1); ly=yl(2)+yw*0.1;
- lab1=text(lx, ly, 'A', 'fontsize',fsz+3);
- ax1=subplot(1,2,2)
- set(ax1,'fontsize',fsz)
- cols={'b' 'r' 'm' [.5 .5 .5]};
- hold on
- for i=1:4
- bar(i,mean(all_decoding_avg_trained(:,i),1),'facecolor',cols{i},'linestyle','none')
- line([i i],[mean(all_decoding_avg_trained(:,i),1)-std(all_decoding_avg_trained(:,i),1)/sqrt(14) mean(all_decoding_avg_trained(:,i),1)+std(all_decoding_avg_trained(:,i),1)/sqrt(14)],'color','k')
- end
- xlim([0 5])
- ylabel('decoding (a.u.)','fontsize',fsz)
- xlabel('click','fontsize',fsz)
- xticklabels({' ','1','2','3','4',' '})
- ylim([-2 14]*10e-4)
- scatter(find(ps_trained<.05),mean(all_decoding_avg_trained(:,find(ps_trained<.05)),1)*1.4,10,'k*')
- t1=title('trained rats','fontsize',fsz)
- p=get(t1,'pos'); p(2)=p(2)*1.07; set(t1,'pos',p)
- p=get(ax1,'pos'); p(2)=p(2)*1.1; p(4)=p(4)*.9; set(ax1,'pos',p)
- xl=xlim();xw=xl(2)-xl(1); lx=xl(1)-xw*0.10;
- yl=ylim();yw=yl(2)-yl(1); ly=yl(2)+yw*0.1;
- lab1=text(lx, ly, 'A', 'fontsize',fsz+3);
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