OSCR

The dentate gyrus efficiently converges LEC and MEC inputs into multimodal, highly specific and reliable environmental representations.

Code ↔ Paper

7 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 7 matches · 2 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Quantification and statistical analysis › Speed modulation ↔ speedcells.m, the whole file · a weak match · score 0.84 · speed score, correlation coefficient, negatively speed, positively speed, Speed information, class · Report a mismatch
  2. [2] § Results › Running speed information conveyed by MEC inputs is sparsely represented in GCs ↔ speedcells.m, the whole file · a weak match · score 0.79 · Speed scores, speed information, correlation coefficient, negative speed, positively speed, Classifying · Report a mismatch
  3. [3] § Methods › Quantification and statistical analysis › Calcium imaging data processing and ROI extraction ↔ suite2p_workflow_2025.m, lines 27 to 84 · score 0.64 · transient detection, fluorescence trace, noise, speed, raw, window · Report a mismatch
  4. [4] § Methods › Quantification and statistical analysis › Calcium imaging data processing and ROI extraction ↔ significant_events3.m, lines 92 to 154 · score 0.62 · standard deviation, fluorescence trace, subtracting, window, baseline, transients · Report a mismatch
  5. [5] § Methods › Quantification and statistical analysis › Activity differences and spatial information ↔ spatial_info_bootstrap3.m, lines 58 to 99 · score 0.61 · firing rate, linear track, spent, bits, fraction, location · Report a mismatch
  6. [6] § Methods › Quantification and statistical analysis › Calcium imaging data processing and ROI extraction ↔ suite2p_workflow_2025.m, lines 242 to 280 · score 0.57 · manually curated, suite2p, calcium, cells · Report a mismatch
  7. [7] § Results › Multiday axonal recordings of LEC and MEC projections to the DG in familiar and novel virtual environments ↔ suite2p_workflow_2025.m, lines 242 to 280 · score 0.52 · manually curated, suite2p, licked, reward, calcium, cells · Report a mismatch

Paper

Loaded from Europe PMC by your browser; OSCR does not store it: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

MATLAB · 407 lines · 21 KB · CC-BY-4.0 · 3 matches

  1. %% Reminder ---------------------------------------------------------------
  2. % 1. update Fall.mat in GUI (version 0.6.14)
  3. % 2. load Fall.mat into workspace (update 25.04.22: now automatic)
  4. % 3. give pathname the folder containing .sbx, .mat and .ephys data
  5. % 4. give saving name and other parameters in User setting parameters Section
  6. % 5. user input dialogue for entering categories of each measurement
  7. % Note: this main script will call the functions:
  8. % - get_behaviourdata5.m
  9. % which will call remove_50Hz_v2.m for lick detection
  10. % - movingmask3.m
  11. % - normtobaseline3.m
  12. % - discretize2.m
  13. % - significant_events3.m (use transientmask.m and transientmask2.m)
  14. % - transientrates2.m
  15. % - spatial_info_bootstrap3.m (use shuffle2.m)
  16. % - placefields2.m (use place_field_dombeck.m and shuffle2.m)
  17. % - spatial_corr3.m (use findcells.m)
  18. % =========================================================================
  19. %% User setting parameters
  20. % addpath('calcium','behavior','neuron') % add path of sub-folders containing the functions used
  21. % load(fullfile('D:\Scanbox\Test1','Fall.mat')) % load Fall.mat from suite2p output
  23. %%% set datapath (the folder containing .sbx, .mat and .ephys data) %%%
  24. datapath = pwd; % current folder
  25. suite2path = pwd; % current folder
  26. % suite2path = 'Z:\2-PhotonData\AK\longtermRec\Ent_6778\DG\240222_to_240224_Ent_6778_DG_data'; % specific folder
  27. %%% load Fall.mat automatically %%%
  28. dir2process = strcat(suite2path, '\suite2p\combined'); % Folder of Fall.mat if multi-plane
  29. if exist(dir2process, 'dir') % check if the folder exists
  30. cd(dir2process); % go in there
  31. load('Fall.mat'); % load Fall.mat
  32. else
  33. dir2process = strcat(suite2path, '\suite2p\plane0'); % Folder of Fall.mat if single-plane
  34. cd(dir2process); % go in there
  35. load('Fall.mat'); % load Fall.mat
  36. end
  37. cd(datapath); % go back to the main folder
  38. h5_path = ''; % optional - the folder containing .h5 data (input suite2p)
  39. saving_name = 'LEC2DG_5744_230515-19_suite2p_workflow_tc_Boutons_nsigma_19_FneuFac_04_spacorr3'; % save dataset under <datapath>/<saving_name>.mat
  40. Minspeed = 0.05; % minimum speed defined as "moving" (unit: m/s; default = 0.05)
  41. Bins = 0:0.025:2; % for use with extractYTrackBoundaries & rescaleYBinning
  42. % Bins = 0.098:0.02275:1.918; % bin EDGES used for calculating dFoY and place fields %v2(>231127)
  43. % Bins = 0.05:0.025:2.05; % bin EDGES used for calculating dFoY and place fields %v1(<231127)
  44. include_lick = 1; % 1 true | 0 false
  45. include_eye = 0; % 1 true | 0 false
  46. compute_data = 1; % 1 to collect fluorescence traces and behaviour data
  47. compute_stat = 1; % 1 to perform statistical analysis
  48. compute_spatial = 1; % 1 to perform place fields analysis
  49. filter_option = 1; % 1 true | 0 false
  50. Fneu_scaleFac = 0.4; % set to 0 if only Raw data [F_raw - Fneu_scaleFac * F_neuropil]
  51. param_data = struct(... % For dFoT normalization
  52. 'percentile',8,... % substracting the percentile value of dFoT to remove the slow drift of baseline (e.g. 8)
  53. 'driftdur',20,... % drift duration (s), the window in which the percentile value is evaluated
  54. 'nsigma',1.9,... % excluding the above [nsigma] events in dFoT trace to recursively compute the baseline mean and sigma (default = 2.3)
  55. ... % For behavioural data (speed)
  56. 'fc',1,... % cut-off frequency (Hz) of low-pass filter (remove fast oscillation due to in- and out-of-plane motion artifact)
  57. 'nspeed',5,... % the number of datapoints (i.e. number of frames) used to average out the position vector (to reduce speed osillation noise)
  58. 'minspeed',Minspeed,...
  59. 'bins',Bins,...
  60. 'Fneu_scaleFac',Fneu_scaleFac,...
  61. 'ephysrate',1000);
  62. param_stat = struct(... % For event detection
  63. 'nsigma',[3,4,5],... % [nsigma] range in which the <5% false positive rate will be searched
  64. 'mindur',0.2,... % the min of transient width (s) in which the <5% false positive rate will be searched (default = 0.3)
  65. 'maxdur',1,... % the max of transient width (s) in which the <5% false positive rate will be searched
  66. 'cutoff',0.05,... % the threshold of <[cutoff] false positive rate
  67. 'GaussFiltFreq',1,... % applying Gaussian filter before transient detection (0 for no filter, 1 for Gaussian filter...)
  68. ... % For spatial information
  69. 'nshuffle',1000,... % the number of shuffle times
  70. 'ywindow',50,... % the number of datapoints in which the dFoT trace is broken in the shuffling process (default = 50)
  71. ... % For place fields ('nshuffle' and 'ywindow' are also used)
  72. 'method','Dombeck',... % 'Dombeck' or 'Significance'
  73. 'movingAverage',true,... % smooth the dFoY trace with the moving average filter of span 3
  74. 'Foutfield',0.25,... % fraction of dFoY used to estimate its baseline
  75. 'Finfield',0.25,... % fraction of max(dFoY)-baseline above which potential place fields are identified
  76. 'minwidth',3,... % min width (number of bins) to be considered as potential place fields
  77. 'Ftransient',0.2,... % fraction of transients that dFoT must be present in the place fields
  78. 'dFrelThresh',7,... % the mean in-field dFoY must be more than [dFrelThresh] times the mean out-of-field dFoY
  79. 'dFabsThresh',0.0,... % ??
  80. ... % For place field correlations
  81. 'sigmaGauss',1,... % applying Gaussian filter before correlation (0 for no filter, 1 for Gaussian filter...)
  82. 'active',1/60,... % active cell defined as >1 transient/min
  83. 'pSI',0.05); % singificance of spatial information>chance
  84. % =========================================================================
  85. %% Compute data (calcium signal and behavior data)
  86. if compute_data
  87. % ---------- Read imaging info --------------------------------------------
  88. % Find all .sbx data (files: structure array with fields: name, bytes, date...)
  89. files = dir(fullfile(datapath,'*.sbx'));
  91. % call extractYTrackBoundaries to resample the Bins (from 0 to 2)
  92. [startBinVolt,stopBinVolt,freezScreenVstep] = extractYTrackBoundaries(files, 0, Bins);
  94. % Extract global parameter from the first file, assumed identical for all
  95. fname = strrep(files(1).name,'.sbx','.mat');
  96. load(fullfile(datapath,fname),'info'); % load 'info'
  97. if ~info.volscan && isempty(info.otparam)
  98. nplanes = 1; % single plane
  99. else
  100. nplanes = info.otparam(3); % number of imaging planes
  101. end
  102. switch info.channels
  103. case 1 % both PMT0 & PMT1
  104. nchannels = 2;
  105. case 2 % only PMT 0
  106. nchannels = 1;
  107. case 3 % only PMT 1
  108. nchannels = 1;
  109. end
  110. % ---------- Assign categories --------------------------------------------
  111. categories = zeros(1,length(files)); % store category of each file
  113. definput = cell(1,length(files)); % default input: initial category
  114. for j = 1:length(files)
  115. fname = strrep(files(j).name,'.sbx','.mat');
  116. load(fullfile(datapath,fname)); % load 'info' and 'category'
  117. if ~exist('category')
  118. definput{j} = '0';
  119. else
  120. definput{j} = num2str(category);
  121. end
  122. end
  123. opts.Resize = 'on';
  125. a = fix(length(files)/15); % divide the total number into multiple of 10
  126. b = rem(length(files),15); % the remainer
  127. for s = 1:a % multiple of 15
  128. answer = inputdlg({files(15*(s-1)+1:15*s).name},'Category',[1 40],definput(15*(s-1)+1:15*s),opts); % create dialog box to gather user input
  129. categories(15*(s-1)+1:15*s) = cellfun(@str2num, answer); % array of category of all measurement
  130. end
  131. if b ~= 0 % the remainer
  132. answer = inputdlg({files(15*a+1:15*a+b).name},'Category',[1 40],definput(15*a+1:15*a+b),opts);
  133. categories(15*a+1:15*a+b) = cellfun(@str2num, answer);
  134. end
  135. categories = uint16(categories); % store categories as 16-bit unsigned integers
  137. % ---------- Calculate nframes per file -----------------------------------
  138. nframes = zeros(1,length(files)); % frame number per imaging file
  139. if nplanes > 1
  140. nframes_z = zeros(nplanes,length(files)); % frame number per plane (different z planes will be counted in respective rows)
  141. end
  142. framerates = zeros(1,length(files)); % number of imaging frames per plane (sec^-1)
  143. catrun_count = zeros(1,length(files),'uint16'); % count how many runs have been stored in this category
  145. % loop over files
  146. for j = 1:length(files)
  147. fname = strrep(files(j).name,'.sbx','.mat');
  148. load(fullfile(datapath,fname),'info'); % load 'info'
  150. framerates(j) = info.resfreq*(2-info.scanmode)/info.config.lines/nplanes; % scanmode=0 bidirection; scanmode=1 unidirection
  152. category = categories(j);
  153. catrun_count(j) = uint16(1 + sum(categories(1:j-1)==categories(j)));
  155. % calculate nframes per file with the following priviledge order:
  156. % 1 - output of new version suite2p (version>0.7? that takes sbx directly)
  157. % 2 - h5_path used prior suite2p if still exist
  158. % 3 - calculate from sbx size and check for data earlier or later than 2020/03/18
  159. if isfield(ops,'nframes_per_folder') % not 'frames_per_file' ??
  160. nframes(j) = ops.nframes_per_folder(j)/nchannels; % ops.nframes_per_folder divided by info.nchan (ops.nchannels) ?? check suite2p code
  161. nframes_z(:,j) = repmat(nframes(j),[nplanes,1]); % incomplete stack (remainder of division by nplanes) is removed
  162. else % old suite2p
  163. if ~isempty(h5_path)
  164. runh5info = h5info(fullfile(h5_path,[fname,'.h5'],'/data'));
  165. max_idx = runh5info.Dataspace.Size(3)/nchannels;
  166. else
  167. max_idx = files(j).bytes/(info.sz(1)*info.sz(2))/nchannels/2 - 1; % !! in scanbox convention, index begins with 0, see sbxread.m !!
  168. if files(j).date > datetime(2020,03,27)
  169. max_idx = max_idx + 1; % after 2020/03/27 correction of the missing frame...
  170. end
  171. end
  172. if nplanes == 1
  173. nframes(j) = max_idx;
  174. else
  175. nframes(j) = floor(max_idx/nplanes); % round down integer in case microscope miss end frames for nplanes>1
  176. nframes_z(:,j) = repmat(nframes(j),[nplanes,1]); % store actual frame numbers per imaging plane
  177. for i = 1:rem(max_idx, nplanes)
  178. nframes_z(i,j) = nframes_z(i,j) + 1; % remainding frames are distributed to plane z0, z1,... that old h5 -> suite2p takes in the remainder...
  179. end
  180. end
  181. end
  182. % save category and updated info
  183. % save(strrep(files(j).name,'.sbx','.mat'), 'category', '-append');
  184. save(fullfile(datapath,fname),'category','info');
  185. end
  186. % ---------- Concatenated frame numbers -----------------------------------
  187. concat_nframes = [0,cumsum(nframes)];
  188. if nplanes > 1
  189. concat_nframes_z = cumsum(nframes_z,2);
  190. concat_nframes_z = [zeros(nplanes,1),concat_nframes_z];
  191. end
  192. fprintf('Categories and frame numbers done\n')
  193. % =========================================================================
  195. % ------ Collect metadata (imaging parameters and behaviour data) ---------
  196. % Initialize dataset structure
  197. dataset = struct('metadata',struct('categories',{cell(1,length(unique(categories)))}),...
  198. 'cells',[],...
  199. 'param_data',param_data);
  201. fprintf('Process dataset metadata...\n')
  202. %% loop over files
  203. for j = 1:length(files)
  204. fname = strrep(files(j).name,'.sbx','');
  206. % sort the category
  207. thiscat = categories(j);
  208. catrun = catrun_count(j);
  209. tt = 1/framerates(j);
  211. dataset.metadata.categories{thiscat}.filename{catrun} = fname;
  212. dataset.metadata.categories{thiscat}.acquisition_rate{catrun} = framerates(j);
  213. dataset.metadata.categories{thiscat}.nframes{catrun} = nframes(j); % frame number
  214. dataset.metadata.categories{thiscat}.frameT{catrun} = 1:nframes(j); % frame indices
  215. dataset.metadata.categories{thiscat}.ft{catrun} = (1:nframes(j))*tt; % time (sec)
  216. dataset.metadata.categories{thiscat}.elapsed_ft{catrun} = (concat_nframes(j)+1):concat_nframes(j+1); % concatenated frame indices
  218. % ---------- Extract behavioural data ---------------------------------
  219. METADATA = get_behaviourdata5(fullfile(datapath,fname),nplanes,nframes(j),...
  220. 'includeLick', 1, 'includeReward', 1, 'includeSound', 1,'lick_TTLsignal',1,... % lick, reward, sound
  221. 'includeAirCH3', 1,'includeAirCH4', 1,... % air puff, channels 3 and 4
  222. 'includeEye',0,'EyeAreaMeth','brightPixlSum','EyeAreaSmooth',0,'overwriteEyeArea',0); % pupil size
  224. % location/movement related
  225. dataset.metadata.categories{thiscat}.x{catrun} = METADATA(2,:);
  226. dataset.metadata.categories{thiscat}.yRaw{catrun} = METADATA(3,:);
  227. % rescaled Y tracks
  228. dataset.metadata.categories{thiscat}.y{catrun} = rescaleYBinning(METADATA(3,:),Bins,stopBinVolt,startBinVolt,freezScreenVstep) ;
  230. % yRaw vs y - plot every 25th run (for testing / check)
  231. if mod(j,25)==0
  232. figure; hold on;
  233. title(num2str(files(1).name(1:11)),j,'interpreter','none');
  234. plot(dataset.metadata.categories{thiscat}.yRaw{catrun},'r'); % raw Y values, in red
  235. plot(dataset.metadata.categories{thiscat}.y{catrun},'g'); % new/corrected Y values, in green
  236. drawnow
  237. end
  239. dataset.metadata.categories{thiscat}.moving{catrun} = ...
  240. movingmask3(METADATA(3,:), framerates(j), param_data.nspeed, Minspeed); % calculating the speed from the gradient of the position vector y averaged over [nspeed] frames, mask for speed below Minspeed
  242. % licks and pupil area
  243. dataset.metadata.categories{thiscat}.licks{catrun} = METADATA(4,:); % NaN if not included
  244. dataset.metadata.categories{thiscat}.pupil_area{catrun} = METADATA(5,:); % NaN if not included
  246. % Reward, Sound and airpuff
  247. dataset.metadata.categories{thiscat}.reward{catrun} = METADATA(6,:); % NaN if not included
  248. dataset.metadata.categories{thiscat}.sound{catrun} = METADATA(7,:); % NaN if not included
  249. dataset.metadata.categories{thiscat}.airCH4{catrun} = METADATA(8,:); % NaN if not included
  250. dataset.metadata.categories{thiscat}.airCH3{catrun} = METADATA(9,:); % NaN if not included
  252. end
  253. fprintf('Completed dataset metadata\n')
  254. % =========================================================================
  256. % -------- Extract calcium signals from suite2p output Fall.mat -----------
  257. cell_ID = find(iscell(:,1)==1); % manually curated cells/boutons
  258. cell_stat = stat(cell_ID); % keep only iscell stat (to be broadcast in parfor loop)
  259. cell_array = cell(1,length(cell_ID)); % initialize a cell array (of structures)
  261. % Define low-pass filter for fluorescence traces corrupted by fast motion
  262. fs = framerates(1);
  263. fc = param_data.fc;
  264. [b,a] = butter(6, fc/(fs/2)); % 6th-order low-pass Butterworth filter with normalized cutoff frequency fc/(fs/2)
  266. fprintf('Process dataset cells...\n')
  267. % loop over cells
  268. parfor k = 1:length(cell_ID)
  270. cell_array{k}.ID = cell_ID(k); % store cell ID
  271. cell_array{k}.stat = cell_stat{k}; % store statistics of each cell
  273. if isfield(cell_stat{k},'iplane')
  274. cell_array{k}.image_plane = cell_stat{k}.iplane; % multi-plane imaging !! iplane index begins with 0 !!
  275. else
  276. cell_array{k}.image_plane = 1; % single-plane imaging
  277. end
  279. % ---------- loop over imaging files ----------------------------------
  280. for j = 1:length(files)
  282. % identify the category and how many runs have been proceeded in this category
  283. thiscat = categories(j);
  284. catrun = catrun_count(j);
  286. % Reconstruct the fluorescence traces
  287. % separate case of cell plane for multi-plane imaging
  288. if nplanes == 1
  289. F_raw = F(cell_ID(k), (concat_nframes(j)+1):concat_nframes(j+1));
  290. F_neu = Fneu(cell_ID(k), (concat_nframes(j)+1):concat_nframes(j+1));
  291. else
  292. F_raw = F(cell_ID(k), concat_nframes_z(cell_stat{k}.iplane+1,j)+1:...
  293. concat_nframes_z(cell_stat{k}.iplane+1,j+1) ); % !! cell plane index start with 0
  294. F_neu = Fneu(cell_ID(k), concat_nframes_z(cell_stat{k}.iplane+1,j)+1:...
  295. concat_nframes_z(cell_stat{k}.iplane+1,j+1) ); % !!cell plane index start with 0
  296. for i = 1:nplanes
  297. if i == (cell_stat{k}.iplane+1) && nframes_z(i,j)>nframes(j)
  298. F_raw = F_raw(1:end-1); % remove last element in case multi-plane of incomplete stack
  299. F_neu = F_neu(1:end-1);
  300. break
  301. end
  302. end
  303. end
  305. % Apply neuropile substraction
  306. F2bsldetect = F_raw - (F_neu.* Fneu_scaleFac);
  307. % [dFoT,F0,SIGMA] = normtobaseline2(F_raw, 2, 5); % calculate the baseline based on eliminating the 2*sigma events above the baseline with a repetition of 5 times
  308. [dFoT,F0,SIGMA] = normtobaseline3(F2bsldetect, param_data.percentile,....
  309. param_data.driftdur*framerates(1),...
  310. param_data.nsigma);
  311. % apply filter
  312. if filter_option
  313. dFoT = filtfilt(b,a,double(dFoT));
  314. end
  315. % calculate the baseline based on the [percentile] value in a window of [driftdur] sec and by excluding the [nsigma] events
  316. cell_array{k}.categories{thiscat}.dFoT{catrun} = dFoT;
  317. cell_array{k}.categories{thiscat}.F0{catrun} = F0;
  318. cell_array{k}.categories{thiscat}.baselineSD{catrun} = SIGMA;
  319. cell_array{k}.categories{thiscat}.spikes{catrun} = ...
  320. spks(cell_ID(k),(concat_nframes(j)+1):concat_nframes(j+1));
  321. cell_array{k}.categories{thiscat}.dFoY{catrun} = ...
  322. discretize2(dFoT.*dataset.metadata.categories{thiscat}.moving{catrun},...
  323. dataset.metadata.categories{thiscat}.y{catrun}, Bins);
  324. end
  325. end
  326. dataset.cells = cell_array;
  328. fprintf('Completed dataset cells\n')
  330. param_stat.FneuFac = Fneu_scaleFac;
  332. % modify saving name according to activated options
  333. % if filter_option
  334. % saving_name = [saving_name , '_filter'];
  335. % end
  336. % if Fneu_scaleFac ~=0
  337. % saving_name = [saving_name , '_nrpilSub'];
  338. % end
  340. % save dataset
  341. sizeDatasetCheck = whos('dataset');
  342. if sizeDatasetCheck.bytes>(2*10^9) % check if file size > 2Gb
  343. save(fullfile(suite2path,saving_name),'dataset','-v7.3','-nocompression'); % for big files (>2Gb)
  344. cprintf([0.1, 0.6, 0.2], 'dataset saved with following parameters: -v7.3 -nocompression');
  345. disp(' ');
  346. else
  347. save(fullfile(suite2path,saving_name),'dataset','-v7'); % for small files (<2Gb)
  348. cprintf([0.1, 0.6, 0.2], 'dataset saved with following parameters: -v7');
  349. disp(' ');
  350. end
  352. % clearvars -except dataset saving_name Minspeed Bins
  353. end
  355. % =========================================================================
  356. %% Compute statistics
  357. if compute_stat
  358. fprintf('Computing statistics...\n');
  360. saving_name2 = [saving_name,'_swf']; % swf for standard workflow
  362. data = dataset;
  363. data.param_stat = param_stat;
  365. % data = standard_workflow2(dataset);
  367. fprintf('Detecting events...\n'); % detect significant transients and recalculate dFoY using significant events
  368. data = significant_events3(data,Bins,...
  369. 'nsigma',param_stat.nsigma,'mindur',param_stat.mindur,'maxdur',param_stat.maxdur,...
  370. 'cutoff',param_stat.cutoff,'GaussFiltFreq',param_stat.GaussFiltFreq);
  372. fprintf('Calculating transient rates...\n');
  373. data = transientrates2(data,true); % calculate the number of transients per sec (during running)
  375. if compute_spatial == 1
  377. fprintf('Calculating spatial information...\n');
  378. data = spatial_info_bootstrap3(data,Bins,param_stat.nshuffle,param_stat.ywindow);
  380. fprintf('Detecting placefields...\n');
  381. data = placefields2(data,Bins,param_stat.method,...
  382. 'movingAverage',param_stat.movingAverage,...
  383. 'Foutfield',param_stat.Foutfield,'Finfield',param_stat.Finfield,'minwidth',param_stat.minwidth,...
  384. 'Ftransient',param_stat.Ftransient,'dFrelThresh',param_stat.dFrelThresh,'dFabsThresh',0.0,...
  385. 'nshuffle',param_stat.nshuffle,'ywindow',param_stat.ywindow);
  387. fprintf('Correlating spatial information across sessions...\n');
  388. data = spatial_corr3(data,param_stat.sigmaGauss,param_stat.active,param_stat.pSI);
  390. else
  391. end
  393. % save data
  394. sizeDatasetCheck = whos('data');
  395. if sizeDatasetCheck.bytes>(2*10^9) % check if file size > 2Gb
  396. save(fullfile(suite2path,saving_name2),'data','-v7.3','-nocompression'); % for big files (>2Gb)
  397. cprintf([0.1, 0.6, 0.2], 'data saved with following parameters: -v7.3 -nocompression');
  398. disp(' ');
  399. else
  400. save(fullfile(suite2path,saving_name2),'data','-v7'); % for small files (<2Gb)
  401. cprintf([0.1, 0.6, 0.2], 'data saved with following parameters: -v7');
  402. disp(' ');
  403. end
  405. end
  407. clearvars -except data

suite2p_workflow_2025.m, under CC-BY-4.0 · at the source

Overview

Authors: Thibault Cholvin1, Marlene Bartos1
ORCID iDs: Thibault Cholvin, Marlene Bartos
  1. Institute for Physiology I, University of Freiburg, Medical Faculty,Freiburg, Germany
Institutions: University of Freiburg (Germany)
Journal: Nature neuroscience, volume 29, issue 5, pages 1166-1180
Dates: received 23 June 2025; accepted 11 February 2026; published online 27 March 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41593-026-02240-0 · PMID 41896306 · PMCID PMC13156041 · OpenAlex W7141591696
Open access: hybrid, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: mouse (organism), systems (subfield)
Methods: Statistics, Machine learning, Connectivity, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Spatial memory, Cellular neuroscience, Hippocampus
MeSH: Dentate Gyrus*, Entorhinal Cortex*, Environment*, Animals, Male, Mice, Mice, Inbred C57BL, Neural Pathways, Neurons (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: European Research Council (787450); Deutsche Forschungsgemeinschaft (561281570, 537575602)
Citations: cited by 1 paper (Europe PMC); 81 references in the paper

Abstract

The entorhinal cortex–hippocampal network plays a key role in the processing, storage and retrieval of contextual information. However, how convergent multimodal information provided by the lateral and medial entorhinal cortex (LEC and MEC) is represented by granule cells (GCs), the principal cells of the dentate gyrus (DG), remains poorly understood. In this study, we employed two-photon calcium imaging of LEC and MEC projections in the DG together with GC population activity in mice navigating familiar and novel virtual environments over five consecutive days. We found that LEC inputs primarily convey olfactory information, whereas MEC inputs provide rich context-specific information to the DG. Although environmental representations rapidly emerged in LEC and MEC projections upon exposure to novel environments and remained stable over time, representations in the DG improved gradually and required repeated exposure to stabilize. Our findings suggest that the convergence of rapidly emerging, high-dimensional LEC and MEC inputs into a sparse and slowly evolving GC population code provides an energy-efficient mechanism for generating multimodal, modality-specific and context-specific representations in the DG.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

Zenodo 15722268

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: MATLAB (24)
Size: 24 files, 24 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (5 files), Signal Processing Toolbox (3 files), Statistics and Machine Learning Toolbox (3 files)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
24 files
At the source:

Code availability

Custom code used for analyses has been deposited at Zenodo and is publicly available as of the date of publication (10.5281/zenodo.15722268). Any additional information required to reanalyze the data reported in this paper is available from the lead contacts upon reasonable request.

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;
  • 24 scripts, each with its path and the digest of its content;
  • 7 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • 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

No dataset and no data link were found in the paper.

Data availability

Due to the very large size of the original data reported in this paper, the original data will be shared by the lead contacts upon reasonable request.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 3 keywords, 9 MeSH terms, 2 funders, 80 references.

Cite

This paper

Cholvin, T., & Bartos, M. (2026). The dentate gyrus efficiently converges LEC and MEC inputs into multimodal, highly specific and reliable environmental representations. Nature neuroscience, 29(5), 1166-1180. https://doi.org/10.1038/s41593-026-02240-0

BibTeX

@article{cholvin2026dentate,
author = {Cholvin, Thibault and Bartos, Marlene},
title = {{The dentate gyrus efficiently converges LEC and MEC inputs into multimodal, highly specific and reliable environmental representations}},
journal = {Nature neuroscience},
year = {2026},
month = mar,
volume = {29},
number = {5},
pages = {1166--1180},
publisher = {Nature Portfolio},
issn = {1097-6256},
doi = {10.1038/s41593-026-02240-0},
url = {https://doi.org/10.1038/s41593-026-02240-0},
pmid = {41896306},
pmcid = {PMC13156041}
}

RIS

TY - JOUR
AU - Cholvin, Thibault
AU - Bartos, Marlene
TI - The dentate gyrus efficiently converges LEC and MEC inputs into multimodal, highly specific and reliable environmental representations
T2 - Nature neuroscience
J2 - Nat Neurosci
PY - 2026
DA - 2026/03/27
VL - 29
IS - 5
SP - 1166
EP - 1180
SN - 1097-6256
PB - Nature Portfolio
DO - 10.1038/s41593-026-02240-0
UR - https://doi.org/10.1038/s41593-026-02240-0
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41593-026-02240-0",
"type": "article-journal",
"title": "The dentate gyrus efficiently converges LEC and MEC inputs into multimodal, highly specific and reliable environmental representations",
"container-title": "Nature neuroscience",
"author": [
{
"family": "Cholvin",
"given": "Thibault"
},
{
"family": "Bartos",
"given": "Marlene"
}
],
"container-title-short": "Nat Neurosci",
"volume": "29",
"issue": "5",
"page": "1166-1180",
"DOI": "10.1038/s41593-026-02240-0",
"PMID": "41896306",
"PMCID": "PMC13156041",
"ISSN": "1097-6256",
"publisher": "Nature Portfolio",
"URL": "https://doi.org/10.1038/s41593-026-02240-0",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
27
]
]
}
}

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

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s41467-022-34039-5 [code]
Hemisphere-specific spatial representation by hippocampal granule cells.
Journal: Nature communications
In common: Signal Processing Toolbox, Image Processing Toolbox, Statistics and Machine Learning Toolbox, systems, mouse, 22 references, 2 authors
[2] doi:10.1038/s41467-024-54932-5 [code]
Multiplexing of temporal and spatial information in the lateral entorhinal cortex.
Journal: Nature communications
In common: Statistics and Machine Learning Toolbox, 17 references
[3] doi:10.1073/pnas.2416214122 [code]
Impaired spatial coding of the hippocampus in a dentate gyrus hypoplasia mouse model.
Journal: Proceedings of the National Academy of Sciences of the United States of America
In common: mouse, 18 references
[4] doi:10.1038/s41467-024-44882-3 [code]
Subfield-specific interneuron circuits govern the hippocampal response to novelty in male mice.
Journal: Nature communications
In common: Signal Processing Toolbox, Image Processing Toolbox, Statistics and Machine Learning Toolbox, systems, mouse, 12 references, author Marlene Bartos
[5] doi:10.1016/j.isci.2024.110361 [code]
Transformation of spatial representations along hippocampal circuits.
Journal: iScience
In common: systems, 18 references
[6] doi:10.1038/s41467-026-72453-1 [code]
The entorhinal spatial map integrates visual identity information of landmarks.
Journal: Nature communications
In common: Statistics and Machine Learning Toolbox, mouse, 15 references
[7] doi:10.7554/elife.83008 [code]
Fan cells in lateral entorhinal cortex directly influence medial entorhinal cortex through synaptic connections in layer 1.
Journal: eLife
In common: mouse, 14 references
[8] doi:10.7554/elife.75391 [code]
Long-term transverse imaging of the hippocampus with glass microperiscopes.
Journal: eLife
In common: Signal Processing Toolbox, Image Processing Toolbox, Statistics and Machine Learning Toolbox, mouse, 13 references
[9] doi:10.1038/s41467-025-60841-y [code]
Predictive goal coding by dentate gyrus somatostatin-expressing interneurons in male mice.
Journal: Nature communications
In common: systems, mouse, 10 references, author Marlene Bartos
[10] doi:10.1038/s42003-026-09592-0 [code]
Sparsification and decorrelation of granule cell activity in the dentate gyrus by noradrenaline.
Journal: Communications biology
In common: systems, mouse, 13 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Add the badge to the code's README

OSCR: the badge of this paper

If you are an author or a maintainer of this paper's code, you can add this badge. It says what OSCR knows of the paper (its code, its validated tracing map) and links the code to this page. Paste one of these snippets under the README's title. Keep the two marks around it, so that the badge can be replaced in place and is never added twice. OSCR changes nothing in your repository.

Markdown
<!-- oscr-badge:start -->
[![OSCR](https://openscicode.org/badge/doi/10.1038/s41593-026-02240-0.svg)](https://openscicode.org/badge/doi/10.1038/s41593-026-02240-0)
<!-- oscr-badge:end -->
reStructuredText
.. oscr-badge:start

.. image:: https://openscicode.org/badge/doi/10.1038/s41593-026-02240-0.svg
   :target: https://openscicode.org/badge/doi/10.1038/s41593-026-02240-0
   :alt: OSCR

.. oscr-badge:end
HTML
<!-- oscr-badge:start -->
<a href="https://openscicode.org/badge/doi/10.1038/s41593-026-02240-0"><img src="https://openscicode.org/badge/doi/10.1038/s41593-026-02240-0.svg" alt="OSCR"></a>
<!-- oscr-badge:end -->

If you choose to, you can have the badge kept up to date for you, with oscr badge --write from the command line or with the GitHub Action in your repository's workflows. Every other form and option (a card, AsciiDoc, LaTeX, shields.io) is on the badge page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: code errors and mismatches between the code and the paper about this paper and its code, from readers and its authors, beside the paper and its tracing map.

Report a mismatch between the code and the paper · Report a code error (signed in)

Reproductions: reports from readers who ran the authors' code and did not get the paper's result, what they ran, with which environment, commit and data.

Report a reproduction failure (signed in)

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.