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The entorhinal spatial map integrates visual identity information of landmarks.

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Paper

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The authors' code

MATLAB · 885 lines · 21 KB · Apache-2.0

  1. copyfile('D:\GarretData\IdenticalCueData\environmentTemplate\temp_ENV2.mat','temp_ENV2.mat');
  2. copyfile('D:\GarretData\IdenticalCueData\environmentTemplate\ENV2_CueBins.mat','ENV2_CueBins.mat');
  3. load('temp_ENV2.mat');
  4. load('ENV2_CueBins.mat');
  5. temp=temp_ENV2; %cue template
  6. c=ENV2_CueBins;
  8. % use 95 percentile
  9. load('D:\GarretData\IdenticalCueData\tracks234\cueCellsEachDay\cell95Idx.mat');
  10. load('D:\GarretData\IdenticalCueData\allFolders.mat');
  11. env=2;
  12. day=1;
  14. %calculate amplitude difference using both peak or mean
  15. % for sequence correlation:
  18. %creat folders and cell idx
  19. useIdx=cell95Idx(:,env-1);
  20. useFolders={};
  21. p=pwd;
  22. for n=1:length(allFolders)
  23. cd(allFolders{n});
  24. load('allDayEnv.mat')
  25. cd(allDayEnv{env}{day});
  26. d=dir('TSeries-*');
  28. for m=1:length(d)
  29. useFolders{end+1}=[d(m).folder '\' d(m).name '\suite2p'];
  30. end
  31. end
  32. cd(p)
  33. save('useIdx.mat','useIdx')
  34. save('useFolders.mat','useFolders');
  35. %%
  36. binWidth=5;
  37. %move cue identify to the first column
  38. cues=[];
  39. cues(:,1)=c(:,3);%first column: cue identify 1 and 2: same number is the same cue
  40. cues(:,[2 3])=c(:,[1 2]); %second and third columns: cue start and end
  41. cues(:,4)=(cues(:,3)+cues(:,2))/2-0.5; %cue center in bin: for the cues with add number of bins, this is the end of the second bin
  42. cues(:,5)=cues(:,4)*5; %cue center in cm
  44. save('temp.mat','temp');
  45. save('cues.mat','cues');
  47. figure,plot(temp)
  48. for n=1:size(cues,1);
  49. if cues(n,1)==1;
  50. hold on
  51. plot([cues(n,2):1:cues(n,3)],1,'g.');
  52. else
  53. hold on
  54. plot([cues(n,2):1:cues(n,3)],1,'m.');
  55. end
  56. end
  58. saveas(gcf,'templatePlot.fig')
  59. close
  61. %%
  62. %all distances
  63. allDistancesI=[];%all distances betwen identical cues: cue numbers are the row numbers in "cues"
  64. allDistancesNI=[];%all distances betwen non-identical cues: cue numbers are the row numbers in "cues"
  66. %first column is one cue, second column is the second cue, the third column is their distances in bins, and the last one
  67. %is their distances in cm.
  69. %identical cue combinations
  70. idxI=[];
  71. %the both types of cue
  72. cueType=[1 2];
  73. for ct=1:length(cueType)
  74. i=find(cues(:,1)==cueType(ct));
  75. for n=1:length(i)-1;
  76. for m=n+1:length(i);
  77. thisPair=[i(n) i(m)];
  78. idxI=[idxI;thisPair];
  79. end
  80. end
  81. end
  83. allDistancesI=idxI;%the first and second columns are the two cues
  84. %the 3rd column is the distance between cue centers by bin
  85. for n=1:size(allDistancesI,1);
  86. allDistancesI(n,3)=abs(cues(allDistancesI(n,1),4)-cues(allDistancesI(n,2),4));
  87. allDistancesI(n,4)=allDistancesI(n,3)*binWidth;
  88. end
  89. [~,i]=sort(allDistancesI(:,4));
  90. allDistancesI=allDistancesI(i,:);
  92. %non identical cue combinations
  93. idxNI=[];
  94. %the first type of cue
  95. i1=find(cues(:,1)==1);
  96. i2=find(cues(:,1)==2);
  97. for n=1:length(i1);
  98. for m=1:length(i2);
  99. thisPair=[i1(n) i2(m)];
  100. idxNI=[idxNI;thisPair];
  101. end
  102. end
  104. allDistancesNI=idxNI;%the first and second columns are the two cues
  105. %the 3rd column is the distance between cue centers by bin
  106. for n=1:size(allDistancesNI,1);
  107. allDistancesNI(n,3)=abs(cues(allDistancesNI(n,1),4)-cues(allDistancesNI(n,2),4));
  108. allDistancesNI(n,4)=allDistancesNI(n,3)*binWidth;
  109. end
  110. [~,i]=sort(allDistancesNI(:,4));
  111. allDistancesNI=allDistancesNI(i,:);
  113. save('allDistancesNI.mat','allDistancesNI');
  114. save('allDistancesI.mat','allDistancesI');
  116. % group the same distances in cells
  117. allDistancesIGroup={};
  118. allDistancesIGroup{1}=allDistancesI([1 2],:);
  119. allDistancesIGroup{2}=allDistancesI(3,:);
  120. allDistancesIGroup{3}=allDistancesI(4,:);
  121. allDistancesIGroup{4}=allDistancesI(5,:);
  122. allDistancesIGroup{5}=allDistancesI(6,:);
  123. allDistancesIGroup{6}=allDistancesI(7,:);
  125. allDistancesNIGroup={};
  126. allDistancesNIGroup{1}=allDistancesNI(1,:);
  127. allDistancesNIGroup{2}=allDistancesNI([2 3],:);
  128. allDistancesNIGroup{3}=allDistancesNI(4,:);
  129. allDistancesNIGroup{4}=allDistancesNI(5,:);
  130. allDistancesNIGroup{5}=allDistancesNI(6,:);
  131. allDistancesNIGroup{6}=allDistancesNI(7,:);
  132. allDistancesNIGroup{7}=allDistancesNI(8,:);
  134. save('allDistancesNIGroup.mat','allDistancesNIGroup');
  135. save('allDistancesIGroup.mat','allDistancesIGroup');
  137. %% amplitude differences
  139. load('useIdx.mat');
  140. load('useFolders.mat')
  141. load('allDistancesIGroup.mat')
  142. load('allDistancesNIGroup.mat')
  143. p=pwd;
  144. cueCellDataThresh=95;
  145. [ampDiffIMean,ampDiffNIMean,ampDiffIPeak,ampDiffNIPeak] = ampDiff(useIdx,useFolders,allDistancesIGroup,allDistancesNIGroup,cueCellDataThresh);
  147. save('ampDiffIMean.mat','ampDiffIMean');
  148. save('ampDiffIPeak.mat','ampDiffIPeak');
  149. save('ampDiffNIMean.mat','ampDiffNIMean');
  150. save('ampDiffNIPeak.mat','ampDiffNIPeak');
  154. %% plot them
  155. ampDiffIMeanAll={};% if there are two cells, merge them
  156. ampDiffIMeanMean=[];%mean
  157. ampDiffIMeanSEM=[];
  158. for n=1:length(ampDiffIMean);
  159. a=ampDiffIMean{n};
  160. for m=1:length(a);
  161. a{m}=a{m}';
  162. end
  163. aa=nanmean(cell2mat(a),2)';
  165. ampDiffIMeanAll{n}=aa;
  166. ampDiffIMeanMean(n)=nanmean(aa);
  167. ampDiffIMeanSEM(n)=nansem(aa,2);
  169. end
  171. ampDiffIPeakAll={};% if there are two cells, merge them
  172. ampDiffIPeakMean=[];%mean
  173. ampDiffIPeakSEM=[];
  174. for n=1:length(ampDiffIPeak);
  175. a=ampDiffIPeak{n};
  176. for m=1:length(a);
  177. a{m}=a{m}';
  178. end
  179. aa=nanmean(cell2mat(a),2)';
  181. ampDiffIPeakAll{n}=aa;
  182. ampDiffIPeakMean(n)=nanmean(aa);
  183. ampDiffIPeakSEM(n)=nansem(aa,2);
  185. end
  187. distanceOnlyI=unique(allDistancesI(:,end));
  189. ampDiffNIMeanAll={};% if there are two cells, merge them
  190. ampDiffNIMeanMean=[];%mean
  191. ampDiffNIMeanSEM=[];
  192. for n=1:length(ampDiffNIMean);
  193. a=ampDiffNIMean{n};
  194. for m=1:length(a);
  195. a{m}=a{m}';
  196. end
  197. aa=nanmean(cell2mat(a),2)';
  199. ampDiffNIMeanAll{n}=aa;
  200. ampDiffNIMeanMean(n)=nanmean(aa);
  201. ampDiffNIMeanSEM(n)=nansem(aa,2);
  203. end
  205. ampDiffNIPeakAll={};% if there are two cells, merge them
  206. ampDiffNIPeakMean=[];%mean
  207. ampDiffNIPeakSEM=[];
  208. for n=1:length(ampDiffNIPeak);
  209. a=ampDiffNIPeak{n};
  210. for m=1:length(a);
  211. a{m}=a{m}';
  212. end
  213. aa=nanmean(cell2mat(a),2)';
  215. ampDiffNIPeakAll{n}=aa;
  216. ampDiffNIPeakMean(n)=nanmean(aa);
  217. ampDiffNIPeakSEM(n)=nansem(aa,2);
  219. end
  220. distanceOnlyNI=unique(allDistancesNI(:,end));
  222. figure
  223. subplot(221)
  225. errorbar(distanceOnlyI,ampDiffIMeanMean,ampDiffIMeanSEM,'g')
  226. hold on
  227. errorbar(distanceOnlyNI,ampDiffNIMeanMean,ampDiffNIMeanSEM,'m')
  228. title('ampDifferences mean');
  230. subplot(222)
  232. errorbar(distanceOnlyI,ampDiffIPeakMean,ampDiffIPeakSEM,'g')
  233. hold on
  234. errorbar(distanceOnlyNI,ampDiffNIPeakMean,ampDiffNIPeakSEM,'m')
  235. title('ampDifferences peak');
  237. %no distances, just include all
  238. I=cell2mat(ampDiffIMeanAll);
  239. NI=cell2mat(ampDiffNIMeanAll);
  240. M=[];
  241. M(1)=nanmean(I);
  242. M(2)=nanmean(NI);
  243. S=[];
  244. S(1)=nansem(I,2);
  245. S(2)=nansem(NI,2);
  246. subplot(223)
  247. bar([1 2],M)
  248. hold on
  249. errorbar([1 2],M,S,'.')
  250. names={'Iden'; 'Noniden' };
  251. set(gca,'xticklabel',names)
  252. [r,p1]=ttest2(I,NI);
  253. title(['mean p=',num2str(p1)])
  255. %no distances, just include all
  256. I=cell2mat(ampDiffIPeakAll);
  257. NI=cell2mat(ampDiffNIPeakAll);
  258. M=[];
  259. M(1)=nanmean(I);
  260. M(2)=nanmean(NI);
  261. S=[];
  262. S(1)=nansem(I,2);
  263. S(2)=nansem(NI,2);
  264. subplot(224)
  265. bar([1 2],M)
  266. hold on
  267. errorbar([1 2],M,S,'.')
  268. names={'Iden'; 'Noniden' };
  269. set(gca,'xticklabel',names)
  270. [r,p1]=ttest2(I,NI);
  271. title(['Peak p=',num2str(p1)])
  273. saveas(gcf,'ampDifferences.fig')
  275. %only use matched distances
  276. idxI=[1 3 5 6];
  277. idxNI=[1 3 5 6];
  278. figure
  279. subplot(221)
  281. errorbar(distanceOnlyI(idxI),ampDiffIMeanMean(idxI),ampDiffIMeanSEM(idxI),'g')
  282. hold on
  283. errorbar(distanceOnlyNI(idxNI),ampDiffNIMeanMean(idxNI),ampDiffNIMeanSEM(idxNI),'m')
  285. %STATs
  286. ITemp=ampDiffIMeanAll(idxI);
  287. NITemp=ampDiffNIMeanAll(idxNI);
  288. I=[];
  289. NI=[];
  291. for n=1:length(idxI);
  292. I(:,n)=ITemp{n}';
  293. NI(:,n)=NITemp{n}';
  294. end
  296. [pAnova,pMC,pLabel] = anovaRM2W_full_BH(I,NI,1);
  297. title(['ampDifferences mean pAnova',num2str(pAnova(1))]);
  299. subplot(222)
  301. errorbar(distanceOnlyI(idxI),ampDiffIPeakMean(idxI),ampDiffIPeakSEM(idxI),'g')
  302. hold on
  303. errorbar(distanceOnlyNI(idxNI),ampDiffNIPeakMean(idxNI),ampDiffNIPeakSEM(idxNI),'m')
  305. %STATs
  306. ITemp=ampDiffIPeakAll(idxI);
  307. NITemp=ampDiffNIPeakAll(idxNI);
  308. I=[];
  309. NI=[];
  311. for n=1:length(idxI);
  312. I(:,n)=ITemp{n}';
  313. NI(:,n)=NITemp{n}';
  314. end
  316. [pAnova,pMC,pLabel] = anovaRM2W_full_BH(I,NI,1);
  317. title(['ampDifferences peak pAnova',num2str(pAnova(1))]);
  319. %no distances, just include all
  320. I=cell2mat(ampDiffIMeanAll(idxI));
  321. NI=cell2mat(ampDiffNIMeanAll(idxNI));
  322. M=[];
  323. M(1)=nanmean(I);
  324. M(2)=nanmean(NI);
  325. S=[];
  326. S(1)=nansem(I,2);
  327. S(2)=nansem(NI,2);
  328. subplot(223)
  329. bar([1 2],M)
  330. hold on
  331. errorbar([1 2],M,S,'.')
  332. names={'Iden'; 'Noniden' };
  333. set(gca,'xticklabel',names)
  334. [r,p1]=ttest2(I,NI);
  335. title(['mean p=',num2str(p1)])
  337. %no distances, just include all
  338. I=cell2mat(ampDiffIPeakAll(idxI));
  339. NI=cell2mat(ampDiffNIPeakAll(idxNI));
  340. M=[];
  341. M(1)=nanmean(I);
  342. M(2)=nanmean(NI);
  343. S=[];
  344. S(1)=nansem(I,2);
  345. S(2)=nansem(NI,2);
  346. subplot(224)
  347. bar([1 2],M)
  348. hold on
  349. errorbar([1 2],M,S,'.')
  350. names={'Iden'; 'Noniden' };
  351. set(gca,'xticklabel',names)
  352. [r,p1]=ttest2(I,NI);
  353. title(['Peak p=',num2str(p1)])
  355. saveas(gcf,'ampDifferencesMatchedDistance.fig')
  356. %% get all lags
  357. cueCellDataThresh=95;
  358. load('useIdx.mat');
  359. load('useFolders.mat');
  360. p=pwd;
  361. lags={};
  362. for n=1:length(useIdx);
  363. lags{n}=[];
  364. disp(n)
  365. cd(useFolders{n});
  366. cells=useIdx{n};
  367. if cueCellDataThresh==95;
  368. filename1='cueAnalysis_sig\newScoreShuffleTemplate\cueCellsAllThresh.mat';
  369. filename2='cueCellsAllThresh';
  370. else
  371. filename1=sprintf('%s%d.mat','cueAnalysis_sig\newScoreShuffleTemplate\cueCellsAllThresh',cueCellDataThresh);
  372. filename2=['cueCellsAllThresh',num2str(cueCellDataThresh)];
  373. end
  375. load(filename1);
  376. data=eval(filename2);
  377. [~,useCellIdx,~]=intersect(data.realIdx,cells);
  378. lags{n}=data.lags(useCellIdx);
  379. end
  381. cd(p);
  382. save('lags.mat','lags');
  384. %% get all dfofs
  385. cueCellDataThresh=95;
  386. load('useIdx.mat');
  387. load('useFolders.mat');
  388. p=pwd;
  389. dfofs={};
  390. for n=1:length(useIdx);
  391. dfofs{n}=[];
  392. disp(n)
  393. cd(useFolders{n});
  394. cells=useIdx{n};
  395. if cueCellDataThresh==95;
  396. filename1='cueAnalysis_sig\newScoreShuffleTemplate\cueCellsAllThresh.mat';
  397. filename2='cueCellsAllThresh';
  398. else
  399. filename1=sprintf('%s%d.mat','cueAnalysis_sig\newScoreShuffleTemplate\cueCellsAllThresh',cueCellDataThresh);
  400. filename2=['cueCellsAllThresh',num2str(cueCellDataThresh)];
  401. end
  403. load(filename1);
  404. data=eval(filename2);
  405. [~,useCellIdx,~]=intersect(data.realIdx,cells);
  406. dfofs{n}=data.dfofAvg(:,useCellIdx);
  407. end
  409. cd(p);
  410. save('dfofs.mat','dfofs');
  419. %% calculate correlation
  420. load('useIdx.mat');
  421. load('useFolders.mat')
  422. load('allDistancesIGroup.mat')
  423. load('allDistancesNIGroup.mat')
  424. p=pwd;
  426. cueCellDataThresh=95;
  427. % number of cells per sequence
  428. NCell=15;
  429. % number of shuffles
  430. NShuffle=100;
  431. NCellFolders=[];
  432. for n=1:length(useIdx);
  433. NCellFolders(n)=length(useIdx{n});
  434. end
  436. iUse=find(NCellFolders>NCell);
  437. useIdxN=useIdx(iUse);
  438. useFoldersN=useFolders(iUse);
  440. [randomCells,corrIMean,corrNIMean,corrIPeak,corrNIPeak] = corrAtCues_NCellFOV(useIdxN,useFoldersN,allDistancesIGroup,allDistancesNIGroup,cueCellDataThresh,NCell);
  442. save('corrIMean.mat','corrIMean');
  443. save('corrIPeak.mat','corrIPeak');
  444. save('corrNIMean.mat','corrNIMean');
  445. save('corrNIPeak.mat','corrNIPeak');
  446. save('randomCells.mat','randomCells');
  448. %% plot corr
  449. corrIMeanAll={};% if there are two cells, merge them
  450. corrIMeanMean=[];%mean
  451. corrIMeanSEM=[];
  452. for n=1:length(corrIMean);
  453. a=corrIMean{n};
  454. for m=1:length(a);
  455. a{m}=a{m}';
  456. end
  457. aa=nanmean(cell2mat(a),2)';
  459. corrIMeanAll{n}=aa;
  460. corrIMeanMean(n)=nanmean(aa);
  461. corrIMeanSEM(n)=nansem(aa,2);
  463. end
  465. corrIPeakAll={};% if there are two cells, merge them
  466. corrIPeakMean=[];%mean
  467. corrIPeakSEM=[];
  468. for n=1:length(corrIPeak);
  470. a=corrIPeak{n};
  471. for m=1:length(a);
  472. a{m}=a{m}';
  473. end
  474. aa=nanmean(cell2mat(a),2)';
  476. corrIPeakAll{n}=aa;
  477. corrIPeakMean(n)=nanmean(aa);
  478. corrIPeakSEM(n)=nansem(aa,2);
  480. end
  482. distanceOnlyI=unique(allDistancesI(:,end));
  484. corrNIMeanAll={};% if there are two cells, merge them
  485. corrNIMeanMean=[];%mean
  486. corrNIMeanSEM=[];
  487. for n=1:length(corrNIMean);
  489. a=corrNIMean{n};
  490. for m=1:length(a);
  491. a{m}=a{m}';
  492. end
  493. aa=nanmean(cell2mat(a),2)';
  495. corrNIMeanAll{n}=aa;
  496. corrNIMeanMean(n)=nanmean(aa);
  497. corrNIMeanSEM(n)=nansem(aa,2);
  499. end
  501. corrNIPeakAll={};% if there are two cells, merge them
  502. corrNIPeakMean=[];%mean
  503. corrNIPeakSEM=[];
  504. for n=1:length(corrNIPeak);
  505. a=corrNIPeak{n};
  506. for m=1:length(a);
  507. a{m}=a{m}';
  508. end
  509. aa=nanmean(cell2mat(a),2)';
  511. corrNIPeakAll{n}=aa;
  512. corrNIPeakMean(n)=nanmean(aa);
  513. corrNIPeakSEM(n)=nansem(aa,2);
  515. end
  516. distanceOnlyNI=unique(allDistancesNI(:,end));
  518. figure
  519. subplot(221)
  521. errorbar(distanceOnlyI,corrIMeanMean,corrIMeanSEM,'g')
  522. hold on
  523. errorbar(distanceOnlyNI,corrNIMeanMean,corrNIMeanSEM,'m')
  524. title('corr mean');
  526. subplot(222)
  528. errorbar(distanceOnlyI,corrIPeakMean,corrIPeakSEM,'g')
  529. hold on
  530. errorbar(distanceOnlyNI,corrNIPeakMean,corrNIPeakSEM,'m')
  531. title('corr peak');
  533. %no distances, just include all
  534. I=cell2mat(corrIMeanAll);
  535. NI=cell2mat(corrNIMeanAll);
  536. M=[];
  537. M(1)=nanmean(I);
  538. M(2)=nanmean(NI);
  539. S=[];
  540. S(1)=nansem(I,2);
  541. S(2)=nansem(NI,2);
  542. subplot(223)
  543. bar([1 2],M)
  544. hold on
  545. errorbar([1 2],M,S,'.')
  546. names={'Iden'; 'Noniden' };
  547. set(gca,'xticklabel',names)
  548. [r,p1]=ttest2(I,NI);
  549. title(['mean p=',num2str(p1)])
  551. %no distances, just include all
  552. I=cell2mat(corrIPeakAll);
  553. NI=cell2mat(corrNIPeakAll);
  554. M=[];
  555. M(1)=nanmean(I);
  556. M(2)=nanmean(NI);
  557. S=[];
  558. S(1)=nansem(I,2);
  559. S(2)=nansem(NI,2);
  560. subplot(224)
  561. bar([1 2],M)
  562. hold on
  563. errorbar([1 2],M,S,'.')
  564. names={'Iden'; 'Noniden' };
  565. set(gca,'xticklabel',names)
  566. [r,p1]=ttest2(I,NI);
  567. title(['Peak p=',num2str(p1)])
  569. filename=sprintf('%s_%d_%s_%d.fig', 'corrNCell', NCell,'NShuffle',NShuffle);
  570. saveas(gcf,filename)
  572. figure
  573. subplot(221)
  575. errorbar(distanceOnlyI(idxI),corrIMeanMean(idxI),corrIMeanSEM(idxI),'g')
  576. hold on
  577. errorbar(distanceOnlyNI(idxNI),corrNIMeanMean(idxNI),corrNIMeanSEM(idxNI),'m')
  579. %STATs
  580. ITemp=corrIMeanAll(idxI);
  581. NITemp=corrNIMeanAll(idxNI);
  582. I=[];
  583. NI=[];
  585. for n=1:length(idxI);
  586. I(:,n)=ITemp{n}';
  587. NI(:,n)=NITemp{n}';
  588. end
  590. [pAnova,pMC,pLabel] = anovaRM2W_full_BH(I,NI,1);
  591. title(['corr mean pAnova',num2str(pAnova(1))]);
  594. subplot(222)
  596. errorbar(distanceOnlyI(idxI),corrIPeakMean(idxI),corrIPeakSEM(idxI),'g')
  597. hold on
  598. errorbar(distanceOnlyNI(idxNI),corrNIPeakMean(idxNI),corrNIPeakSEM(idxNI),'m')
  600. %STATs
  601. ITemp=corrIPeakAll(idxI);
  602. NITemp=corrNIPeakAll(idxNI);
  603. I=[];
  604. NI=[];
  606. for n=1:length(idxI);
  607. I(:,n)=ITemp{n}';
  608. NI(:,n)=NITemp{n}';
  609. end
  611. [pAnova,pMC,pLabel] = anovaRM2W_full_BH(I,NI,1);
  612. title(['corr peak pAnova',num2str(pAnova(1))]);
  614. %no distances, just include all
  615. I=cell2mat(corrIMeanAll(idxI));
  616. NI=cell2mat(corrNIMeanAll(idxNI));
  617. M=[];
  618. M(1)=nanmean(I);
  619. M(2)=nanmean(NI);
  620. S=[];
  621. S(1)=nansem(I,2);
  622. S(2)=nansem(NI,2);
  623. subplot(223)
  624. bar([1 2],M)
  625. hold on
  626. errorbar([1 2],M,S,'.')
  627. names={'Iden'; 'Noniden' };
  628. set(gca,'xticklabel',names)
  629. [r,p1]=ttest2(I,NI);
  630. title(['mean p=',num2str(p1)])
  632. %no distances, just include all
  633. I=cell2mat(corrIPeakAll(idxI));
  634. NI=cell2mat(corrNIPeakAll(idxNI));
  635. M=[];
  636. M(1)=nanmean(I);
  637. M(2)=nanmean(NI);
  638. S=[];
  639. S(1)=nansem(I,2);
  640. S(2)=nansem(NI,2);
  641. subplot(224)
  642. bar([1 2],M)
  643. hold on
  644. errorbar([1 2],M,S,'.')
  645. names={'Iden'; 'Noniden' };
  646. set(gca,'xticklabel',names)
  647. [r,p1]=ttest2(I,NI);
  648. title(['Peak p=',num2str(p1)])
  650. filename=sprintf('%s_%d_%s_%d_%s.fig', 'corrNCell', NCell,'NShuffle',NShuffle,'matchedDistance');
  651. saveas(gcf,filename)
  653. %% calculate correlation
  654. load('useIdx.mat');
  655. load('useFolders.mat')
  656. load('allDistancesIGroup.mat')
  657. load('allDistancesNIGroup.mat')
  658. p=pwd;
  660. cueCellDataThresh=95;
  661. % % number of cells per sequence
  662. % NCell=15;
  663. % % number of shuffles
  664. % NShuffle=100;
  666. [corrIMeanNoShuffle,corrNIMeanNoShuffle,corrIPeakNoShuffle,corrNIPeakNoShuffle] = corrAtCuesNoShuffle(useIdx,useFolders,allDistancesIGroup,allDistancesNIGroup,cueCellDataThresh);
  668. save('corrIMeanNoShuffle.mat','corrIMeanNoShuffle');
  669. save('corrIPeakNoShuffle.mat','corrIPeakNoShuffle');
  670. save('corrNIMeanNoShuffle.mat','corrNIMeanNoShuffle');
  671. save('corrNIPeakNoShuffle.mat','corrNIPeakNoShuffle');
  674. %% plot corr
  675. corrIMeanNoShuffleAll={};% if there are two cells, merge them
  676. corrIMeanNoShuffleMean=[];%mean
  677. corrIMeanNoShuffleSEM=[];
  678. for n=1:length(corrIMeanNoShuffle);
  680. a=corrIMeanNoShuffle{n};
  681. for m=1:length(a);
  682. a{m}=a{m}';
  683. end
  684. aa=nanmean(cell2mat(a),2)';
  686. corrIMeanNoShuffleAll{n}=aa;
  687. corrIMeanNoShuffleMean(n)=nanmean(aa);
  688. corrIMeanNoShuffleSEM(n)=nansem(aa,2);
  690. end
  692. corrIPeakNoShuffleAll={};% if there are two cells, merge them
  693. corrIPeakNoShuffleMean=[];%mean
  694. corrIPeakNoShuffleSEM=[];
  695. for n=1:length(corrIPeakNoShuffle);
  697. a=corrIPeakNoShuffle{n};
  698. for m=1:length(a);
  699. a{m}=a{m}';
  700. end
  701. aa=nanmean(cell2mat(a),2)';
  703. corrIPeakNoShuffleAll{n}=aa;
  704. corrIPeakNoShuffleMean(n)=nanmean(aa);
  705. corrIPeakNoShuffleSEM(n)=nansem(aa,2);
  707. end
  709. distanceOnlyI=unique(allDistancesI(:,end));
  711. corrNIMeanNoShuffleAll={};% if there are two cells, merge them
  712. corrNIMeanNoShuffleMean=[];%mean
  713. corrNIMeanNoShuffleSEM=[];
  714. for n=1:length(corrNIMeanNoShuffle);
  716. a=corrNIMeanNoShuffle{n};
  717. for m=1:length(a);
  718. a{m}=a{m}';
  719. end
  720. aa=nanmean(cell2mat(a),2)';
  722. corrNIMeanNoShuffleAll{n}=aa;
  723. corrNIMeanNoShuffleMean(n)=nanmean(aa);
  724. corrNIMeanNoShuffleSEM(n)=nansem(aa,2);
  726. end
  728. corrNIPeakNoShuffleAll={};% if there are two cells, merge them
  729. corrNIPeakNoShuffleMean=[];%mean
  730. corrNIPeakNoShuffleSEM=[];
  731. for n=1:length(corrNIPeakNoShuffle);
  733. a=corrNIPeakNoShuffle{n};
  734. for m=1:length(a);
  735. a{m}=a{m}';
  736. end
  737. aa=nanmean(cell2mat(a),2)';
  740. corrNIPeakNoShuffleAll{n}=aa;
  741. corrNIPeakNoShuffleMean(n)=nanmean(aa);
  742. corrNIPeakNoShuffleSEM(n)=nansem(aa,2);
  744. end
  745. distanceOnlyNI=unique(allDistancesNI(:,end));
  747. figure
  748. subplot(221)
  750. errorbar(distanceOnlyI,corrIMeanNoShuffleMean,corrIMeanNoShuffleSEM,'g')
  751. hold on
  752. errorbar(distanceOnlyNI,corrNIMeanNoShuffleMean,corrNIMeanNoShuffleSEM,'m')
  753. title('corr mean');
  755. subplot(222)
  757. errorbar(distanceOnlyI,corrIPeakNoShuffleMean,corrIPeakNoShuffleSEM,'g')
  758. hold on
  759. errorbar(distanceOnlyNI,corrNIPeakNoShuffleMean,corrNIPeakNoShuffleSEM,'m')
  760. title('corr peak');
  762. %no distances, just include all
  763. I=cell2mat(corrIMeanNoShuffleAll);
  764. NI=cell2mat(corrNIMeanNoShuffleAll);
  765. M=[];
  766. M(1)=nanmean(I);
  767. M(2)=nanmean(NI);
  768. S=[];
  769. S(1)=nansem(I,2);
  770. S(2)=nansem(NI,2);
  771. subplot(223)
  772. bar([1 2],M)
  773. hold on
  774. errorbar([1 2],M,S,'.')
  775. names={'Iden'; 'Noniden' };
  776. set(gca,'xticklabel',names)
  777. [r,p1]=ttest2(I,NI);
  778. title(['mean p=',num2str(p1)])
  780. %no distances, just include all
  781. I=cell2mat(corrIPeakNoShuffleAll);
  782. NI=cell2mat(corrNIPeakNoShuffleAll);
  783. M=[];
  784. M(1)=nanmean(I);
  785. M(2)=nanmean(NI);
  786. S=[];
  787. S(1)=nansem(I,2);
  788. S(2)=nansem(NI,2);
  789. subplot(224)
  790. bar([1 2],M)
  791. hold on
  792. errorbar([1 2],M,S,'.')
  793. names={'Iden'; 'Noniden' };
  794. set(gca,'xticklabel',names)
  795. [r,p1]=ttest2(I,NI);
  796. title(['Peak p=',num2str(p1)])
  798. saveas(gcf,'corrNCellNoShuffle.fig')
  800. figure
  801. subplot(221)
  803. errorbar(distanceOnlyI(idxI),corrIMeanNoShuffleMean(idxI),corrIMeanNoShuffleSEM(idxI),'g')
  804. hold on
  805. errorbar(distanceOnlyNI(idxNI),corrNIMeanNoShuffleMean(idxNI),corrNIMeanNoShuffleSEM(idxNI),'m')
  807. %STATs
  808. ITemp=corrIMeanNoShuffleAll(idxI);
  809. NITemp=corrNIMeanNoShuffleAll(idxNI);
  810. I=[];
  811. NI=[];
  813. for n=1:length(idxI);
  814. I(:,n)=ITemp{n}';
  815. NI(:,n)=NITemp{n}';
  816. end
  818. [pAnova,pMC,pLabel] = anovaRM2W_full_BH(I,NI,1);
  819. title(['corr mean pAnova',num2str(pAnova(1))]);
  822. subplot(222)
  824. errorbar(distanceOnlyI(idxI),corrIPeakNoShuffleMean(idxI),corrIPeakNoShuffleSEM(idxI),'g')
  825. hold on
  826. errorbar(distanceOnlyNI(idxNI),corrNIPeakNoShuffleMean(idxNI),corrNIPeakNoShuffleSEM(idxNI),'m')
  828. %STATs
  829. ITemp=corrIPeakNoShuffleAll(idxI);
  830. NITemp=corrNIPeakNoShuffleAll(idxNI);
  831. I=[];
  832. NI=[];
  834. for n=1:length(idxI);
  835. I(:,n)=ITemp{n}';
  836. NI(:,n)=NITemp{n}';
  837. end
  839. [pAnova,pMC,pLabel] = anovaRM2W_full_BH(I,NI,1);
  840. title(['corr mean pAnova',num2str(pAnova(1))]);
  842. %no distances, just include all
  843. I=cell2mat(corrIMeanNoShuffleAll(idxI));
  844. NI=cell2mat(corrNIMeanNoShuffleAll(idxNI));
  845. M=[];
  846. M(1)=nanmean(I);
  847. M(2)=nanmean(NI);
  848. S=[];
  849. S(1)=nansem(I,2);
  850. S(2)=nansem(NI,2);
  851. subplot(223)
  852. bar([1 2],M)
  853. hold on
  854. errorbar([1 2],M,S,'.')
  855. names={'Iden'; 'Noniden' };
  856. set(gca,'xticklabel',names)
  857. [r,p1]=ttest2(I,NI);
  858. title(['mean p=',num2str(p1)])
  860. %no distances, just include all
  861. I=cell2mat(corrIPeakNoShuffleAll(idxI));
  862. NI=cell2mat(corrNIPeakNoShuffleAll(idxNI));
  863. M=[];
  864. M(1)=nanmean(I);
  865. M(2)=nanmean(NI);
  866. S=[];
  867. S(1)=nansem(I,2);
  868. S(2)=nansem(NI,2);
  869. subplot(224)
  870. bar([1 2],M)
  871. hold on
  872. errorbar([1 2],M,S,'.')
  873. names={'Iden'; 'Noniden' };
  874. set(gca,'xticklabel',names)
  875. [r,p1]=ttest2(I,NI);
  876. title(['Peak p=',num2str(p1)])
  879. saveas(gcf,'corrNCellNoShuffle_matchedDistance.fig')

ampAndCorr.m at commit 76eb11a, under Apache-2.0 · at the source

Overview

Authors: Garret Wang1,2, Farid Shahid1, Taylor J. Malone1, Jean Tyan1,3, Kyle Cekada1,4, Lujia Chen1, Yi Gu1
ORCID iDs: Taylor J. Malone, Jean Tyan, Yi Gu
  1. Spatial Navigation and Memory Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health,Bethesda, MD USA
  2. Present Address: Medical Scientist Training Program, Medical College of Wisconsin,Milwaukee, WI USA
  3. Present Address: Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet,Stockholm, Sweden
  4. Present Address: Department of Psychological and Brain Sciences, University of California, Santa Barbara,Santa Barbara, CA USA
Institutions: National Institute of Neurological Disorders and Stroke (United States); Medical College of Wisconsin (United States); Karolinska Institutet (Sweden); University of California, Santa Barbara (United States)
Journal: Nature communications, volume 17, issue 1, article 6164
Dates: received 9 December 2024; accepted 16 April 2026; published online 7 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-72453-1 · PMID 42098095 · PMCID PMC13365227 · OpenAlex W4408445668
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.03.13.643169
Status: code verified
Categories: mouse (organism)
Methods: Spectral & time-frequency, Statistics, Machine learning, Connectivity, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Perception, Cognitive control
MeSH: Entorhinal Cortex*, Space Perception*, Animals, Cues, Grid Cells, Male, Mice, Mice, Inbred C57BL, Neurons (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NINDS (ZIA NS009415)
Citations: cited by 1 paper (Europe PMC); 59 references in the paper

Abstract

Landmarks guide navigation by providing information through their location and identity. The medial entorhinal cortex (MEC) is well known for representing landmark location, but whether it also encodes landmark identity remains unclear. Here we show, using two-photon calcium imaging of MEC neurons in mice navigating multiple virtual environments, that a population of neurons known as cue cells encodes landmark identity. Cue cells respond selectively to individual landmarks and produce more distinct activity patterns for visually disparate landmarks than for identical ones. Identity encoding is modulated by the spatial shift of cue cell activity relative to landmark location and is context dependent, changing across environments, but remaining stable within the same environment despite repeated experience. In contrast, cue cells’ representation of landmark location changes with experience. Grid cells, another major MEC cell type, more strongly represent landmark location, but only weakly encode identity. These findings suggest that the MEC integrates both the location and identity of landmarks to support navigation.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above.

GuLab-NIH/Wang-and-Shahid-2026

License: Apache-2.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 76eb11afe5dd89df7dc8bb9506de4578bc35161a, 19 March 2026
Languages: MATLAB (22)
Size: 24 files, 22 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (9 files), Violinplot-Matlab (6 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
24 files
At the source: github.com/GuLab-NIH/Wang-and-Shahid-2026

Code availability

Custom code used for the analysis of data is available on GitHub [https://github.com/GuLab-NIH/Wang-and-Shahid-2026].

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 22 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

Data generated in this study have been deposited in Zenodo [10.5281/zenodo.19115394]. Processed data underlying all figures are available in the Source Data file. Further information and requests for resources and reagents should be directed to the lead contact, Y.G. Source data are provided with this paper.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 2 keywords, 9 MeSH terms, 1 funder, 59 references.

Cite

This paper

Wang, G., Shahid, F., Malone, T. J., Tyan, J., Cekada, K., Chen, L., & Gu, Y. (2026). The entorhinal spatial map integrates visual identity information of landmarks. Nature communications, 17(1), 6164. https://doi.org/10.1038/s41467-026-72453-1

BibTeX

@article{wang2026entorhinal,
author = {Wang, Garret and Shahid, Farid and Malone, Taylor J. and Tyan, Jean and Cekada, Kyle and Chen, Lujia and Gu, Yi},
title = {{The entorhinal spatial map integrates visual identity information of landmarks}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {6164},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-72453-1},
url = {https://doi.org/10.1038/s41467-026-72453-1},
pmid = {42098095},
pmcid = {PMC13365227}
}

RIS

TY - JOUR
AU - Wang, Garret
AU - Shahid, Farid
AU - Malone, Taylor J.
AU - Tyan, Jean
AU - Cekada, Kyle
AU - Chen, Lujia
AU - Gu, Yi
TI - The entorhinal spatial map integrates visual identity information of landmarks
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/07
VL - 17
IS - 1
SP - 6164
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-72453-1
UR - https://doi.org/10.1038/s41467-026-72453-1
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-72453-1",
"type": "article-journal",
"title": "The entorhinal spatial map integrates visual identity information of landmarks",
"container-title": "Nature communications",
"author": [
{
"family": "Wang",
"given": "Garret"
},
{
"family": "Shahid",
"given": "Farid"
},
{
"family": "Malone",
"given": "Taylor J."
},
{
"family": "Tyan",
"given": "Jean"
},
{
"family": "Cekada",
"given": "Kyle"
},
{
"family": "Chen",
"given": "Lujia"
},
{
"family": "Gu",
"given": "Yi"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "6164",
"DOI": "10.1038/s41467-026-72453-1",
"PMID": "42098095",
"PMCID": "PMC13365227",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-72453-1",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
7
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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