OSCR

Subicular plateaus signal reward locations during goal-directed behavior.

Code ↔ Paper

6 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 6 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § RESULTS › Unsupervised clustering organizes plateaus by pre-plateau Vm ↔ plateau_clustering_analysis_v3_anchors.m, lines 226–297 · score 0.77 · global template, t-SNE, plateau clusters, pre Vm, Fisher, silhouette
  2. [2] § METHOD DETAILS › K-means clustering and t-SNE visualization ↔ plateau_clustering_analysis_v3_anchors.m, lines 169–207 · score 0.71 · squared errors, silhouette scores, optimal, 1–6, sum, space
  3. [3] § METHOD DETAILS › Univariate analysis ↔ UnivariateAnalysis.zip/AnalyseTableUnivariateAnalysis.m, the whole file · a weak match · score 0.69 · Mann Whitney, univariate, pre event, plateau events, predicted, occurrence
  4. [4] § RESULTS › Subicular plateau occurrence is shaped by intrinsic and behavioral factors ↔ UnivariateAnalysis.zip/AnalyseTableUnivariateAnalysis.m, the whole file · a weak match · score 0.67 · Mann Whitney, plateau occurrence, pre event, univariate, predictor, variables
  5. [5] § METHOD DETAILS › Univariate analysis ↔ UnivariateAnalysis.zip/CreateTableUnivariateAnalysis.m, lines 1–21 · score 0.65 · plateau ratio, pre event, 20 ms, buffer, univariate, predicted
  6. [6] § METHOD DETAILS › Plateau analysis post reward location shift ↔ plateau-spatial-analysis.zip/analyzePlateauDistributions.m, lines 1–41 · score 0.51 · matched control, reward location, shift, delivered, opposite, bins

Paper

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The authors' code

MATLAB · 448 lines · 24 KB · CC-BY-4.0 · 2 matches

  1. function plateau_clustering_analysis_v3_anchors(main_folder)
  3. rng(1);
  5. %% ====================================================================
  6. %% SECTION 1: SETUP & CONFIGURATION
  7. %% ====================================================================
  9. if nargin < 1
  10. main_folder = uigetdir('', 'Select Data Folder');
  11. if main_folder == 0, return; end
  12. end
  14. timestamp = datestr(now, 'yyyymmdd_HHMMSS');
  15. save_dir = fullfile(main_folder, ['Clustering_Results_' timestamp]);
  16. if ~exist(save_dir, 'dir'), mkdir(save_dir); end
  17. FigList = struct('h', {}, 'name', {});
  19. % 1.1 Parameters
  20. k_clusters = 2;
  21. n_spatial_bins = 10;
  23. % 1.2 Anchors
  24. UseAnchors.Reward = true;
  25. UseAnchors.SimSpeed = true;
  26. UseAnchors.SimAccel = false;
  27. UseAnchors.ExactSpeed = true;
  28. UseAnchors.ExactAccel = false;
  29. UseAnchors.SpeedCrossUp = true;
  31. % 1.3 Filters
  32. Filter.Context = 'A';
  33. Filter.Zone = 'Both';
  34. Filter.Movement = 'RunOnly';
  35. Filter.PreVmRange = [-0.060, -0.040];
  36. Filter.MinDuration = 0.005;
  37. Filter.Kurtosis = false;
  38. Max_Kurtosis = 5;
  40. % 1.4 Features (Toggle True/False)
  41. FeatureParams.UseLog = true;
  42. UseFeatures.Duration = false;
  43. UseFeatures.MeanVm = false;
  44. UseFeatures.MedianVm = false;
  45. UseFeatures.Area = true;
  46. UseFeatures.MaxAmp = false;
  47. UseFeatures.Skew = false;
  48. UseFeatures.Kurtosis = false;
  49. UseFeatures.PreVm = true;
  50. UseFeatures.SignedDist = false;
  51. UseFeatures.RiseTime = false;
  52. UseFeatures.DecayTime = false;
  53. UseFeatures.TemplateMatch = true;
  55. fprintf('Setup Complete. Loading Data...\n');
  58. %% ====================================================================
  59. %% SECTION 2: DATA LOADING
  60. %% ====================================================================
  61. master_file = fullfile(main_folder, 'Master_Project_Data.mat');
  62. if ~exist(master_file, 'file'), errordlg('Data not found.'); return; end
  63. data = load(master_file);
  64. MasterEvents = data.MasterEvents;
  66. if isfield(data, 'SessionStruct'), SessionStruct=data.SessionStruct; else, SessionStruct=data.data_struct; end
  68. % Ensure SignedDist
  69. if ~ismember('SignedDist', MasterEvents.Properties.VariableNames)
  70. d = MasterEvents.DistToReward; z = lower(string(MasterEvents.ZoneTag));
  71. is_neg = contains(z, 'pre') | contains(z, 'far');
  72. d(is_neg) = -abs(d(is_neg)); MasterEvents.SignedDist = d;
  73. end
  75. % Anchor Mapping
  76. FullAnchorMap = {
  77. 'Reward', 'SignedDist', 'Reward';
  78. 'Sim Speed', 'Dist_similar_speed', 'similar_speed';
  79. 'Sim Accel', 'Dist_similar_accel', 'similar_accel';
  80. 'Exact Speed', 'Dist_exact_speed', 'exact_speed';
  81. 'Exact Accel', 'Dist_exact_accel', 'exact_accel';
  82. 'Speed Cross Up', 'Dist_speed_crossing_up', 'speed_crossing_up'
  83. };
  84. selMask = [UseAnchors.Reward; UseAnchors.SimSpeed; UseAnchors.SimAccel; ...
  85. UseAnchors.ExactSpeed; UseAnchors.ExactAccel; UseAnchors.SpeedCrossUp];
  86. AnchorMap = FullAnchorMap(logical(selMask), :);
  88. % Validate Columns
  89. valid_cols = false(size(AnchorMap,1), 1);
  90. for i=1:size(AnchorMap,1)
  91. if ismember(AnchorMap{i,2}, MasterEvents.Properties.VariableNames), valid_cols(i)=true; end
  92. end
  93. AnchorMap = AnchorMap(valid_cols, :);
  95. % Colors
  96. AncColors = lines(size(AnchorMap,1));
  97. rIdx = find(strcmp(AnchorMap(:,1), 'Reward')); if ~isempty(rIdx), AncColors(rIdx,:) = [0 0 0]; end
  99. % Ensure Features Exist
  100. req_cols = {'Kurtosis','RiseTime','DecayTime','MedianVm','Area','MaxAmp','Skew'};
  101. for r=1:length(req_cols), if ~ismember(req_cols{r}, MasterEvents.Properties.VariableNames), MasterEvents.(req_cols{r}) = nan(height(MasterEvents),1); end; end
  102. if all(isnan(MasterEvents.MedianVm)), MasterEvents.MedianVm = MasterEvents.MeanVm; end
  105. %% ====================================================================
  106. %% SECTION 3: FILTERING & TEMPLATES
  107. %% ====================================================================
  108. if strcmpi(Filter.Context, 'A'), MasterEvents = MasterEvents(strcmpi(MasterEvents.GroupTag, 'A'), :);
  109. elseif strcmpi(Filter.Context, 'B'), MasterEvents = MasterEvents(strcmpi(MasterEvents.GroupTag, 'B'), :); end
  110. if ~isempty(Filter.PreVmRange), pvm = MasterEvents.PreVm; MasterEvents = MasterEvents(pvm >= Filter.PreVmRange(1) & pvm <= Filter.PreVmRange(2), :); end
  111. z = lower(string(MasterEvents.ZoneTag));
  112. if strcmpi(Filter.Zone, 'NearOnly'), MasterEvents = MasterEvents((z == "pre") | (z == "post"), :);
  113. elseif strcmpi(Filter.Zone, 'FarOnly'), MasterEvents = MasterEvents((z == "far"), :); end
  114. s = MasterEvents.Speed; s(isnan(s)) = 0;
  115. if strcmpi(Filter.Movement, 'RunOnly'), MasterEvents = MasterEvents(s >= 0.05, :);
  116. elseif strcmpi(Filter.Movement, 'StopOnly'), MasterEvents = MasterEvents(s < 0.05, :); end
  117. if Filter.MinDuration > 0, MasterEvents = MasterEvents(MasterEvents.Duration >= Filter.MinDuration, :); end
  119. % Kurtosis Filter
  120. if Filter.Kurtosis ==1, MasterEvents = MasterEvents(MasterEvents.Kurtosis <= Max_Kurtosis, :); end
  122. if height(MasterEvents) < k_clusters, errordlg('Not enough events.', 'Error'); return; end
  124. % Templates
  125. norm_len = 100; pre_pad = 0;
  126. augmented_mat = nan(height(MasterEvents), pre_pad + norm_len);
  127. for i = 1:height(MasterEvents)
  128. tr = MasterEvents.Trace{i}; pvm = MasterEvents.PreVm(i);
  129. if length(tr) > 2, body = interp1(linspace(0,1,length(tr)), tr, linspace(0,1,norm_len));
  130. augmented_mat(i, :) = [repmat(pvm, 1, pre_pad), body];
  131. end
  132. end
  133. TemplateTrace = nanmean(augmented_mat, 1);
  134. ShapeScore = zeros(height(MasterEvents), 1);
  135. for i = 1:height(MasterEvents)
  136. if ~any(isnan(augmented_mat(i,:))), R = corrcoef(augmented_mat(i,:), TemplateTrace); ShapeScore(i) = R(1,2); end
  137. end
  138. MasterEvents.TemplateMatch = ShapeScore;
  141. %% ====================================================================
  142. %% SECTION 4: FEATURE EXTRACTION & CLUSTERING
  143. %% ====================================================================
  144. X = []; VarNames = {};
  145. if FeatureParams.UseLog, TF = @(x) log10(x); prefix = 'Log'; else, TF = @(x) x; prefix = ''; end
  147. if UseFeatures.Duration, X=[X, TF(MasterEvents.Duration)]; VarNames{end+1}=[prefix 'Dur']; end
  148. if UseFeatures.MeanVm, X=[X, MasterEvents.MeanVm]; VarNames{end+1}='MeanVm'; end
  149. if UseFeatures.MedianVm, X=[X, MasterEvents.MedianVm]; VarNames{end+1}='MedVm'; end
  150. if UseFeatures.Area, X=[X, TF(MasterEvents.Area)]; VarNames{end+1}=[prefix 'Area']; end
  151. if UseFeatures.MaxAmp, X=[X, MasterEvents.MaxAmp]; VarNames{end+1}='MaxAmp'; end
  152. if UseFeatures.Skew, X=[X, MasterEvents.Skew]; VarNames{end+1}='Skew'; end
  153. if UseFeatures.Kurtosis, X=[X, MasterEvents.Kurtosis]; VarNames{end+1}='Kurt'; end
  154. if UseFeatures.PreVm, X=[X, MasterEvents.PreVm]; VarNames{end+1}='PreVm'; end
  155. if UseFeatures.SignedDist, X=[X, MasterEvents.SignedDist]; VarNames{end+1}='Dist'; end
  156. if UseFeatures.RiseTime, X=[X, MasterEvents.RiseTime]; VarNames{end+1}='Rise'; end
  157. if UseFeatures.DecayTime, X=[X, MasterEvents.DecayTime]; VarNames{end+1}='Decay'; end
  158. if UseFeatures.TemplateMatch, X=[X, MasterEvents.TemplateMatch]; VarNames{end+1}='Shape'; end
  160. if isempty(X), error('No features selected.'); end
  161. bad_rows = any(isnan(X) | isinf(X), 2);
  162. if any(bad_rows), X = X(~bad_rows, :); MasterEvents = MasterEvents(~bad_rows, :); end
  163. X_z = zscore(X); X_z(isnan(X_z)) = 0;
  167. %% ====================================================================
  168. %% SECTION 4.5: EVALUATE OPTIMAL K (NEW TEST)
  169. %% ====================================================================
  170. fprintf('Testing optimal cluster number (k=1 to 6)...\n');
  171. max_k_test = 6;
  172. sse_list = zeros(max_k_test, 1);
  173. sil_list = nan(max_k_test, 1); % Silhouette undefined for k=1
  175. % Loop through k=1 to k=6 to test quality
  176. for k_t = 1:max_k_test
  177. % Run k-means for this k (suppress output)
  178. [idx_t, ~, sumd_t] = kmeans(X_z, k_t, 'Replicates', 3, 'Display', 'off');
  179. sse_list(k_t) = sum(sumd_t); % Sum of Squared Errors (Inertia)
  181. if k_t > 1
  182. s_t = silhouette(X_z, idx_t);
  183. sil_list(k_t) = mean(s_t);
  184. end
  185. end
  187. % Plot the results
  188. f_opt = figure('Name', 'Optimal K Test', 'Color', 'w', 'Position', [200 200 900 400]);
  189. FigList(end+1) = struct('h', f_opt, 'name', 'Optimal_K_Test');
  190. t_opt = tiledlayout(1, 2, 'TileSpacing', 'compact', 'Padding', 'compact');
  192. % Plot 1: Elbow Plot (SSE) - Look for the "bend"
  193. nexttile;
  194. plot(1:max_k_test, sse_list, '-o', 'LineWidth', 2, 'Color', 'k', 'MarkerFaceColor', 'b');
  195. title('Elbow Method (SSE)');
  196. xlabel('Number of Clusters (k)'); ylabel('Sum of Squared Errors');
  197. grid on;
  199. % Plot 2: Silhouette Plot - Look for the highest peak
  200. nexttile;
  201. plot(2:max_k_test, sil_list(2:end), '-o', 'LineWidth', 2, 'Color', 'k', 'MarkerFaceColor', 'r');
  202. title('Silhouette Method');
  203. xlabel('Number of Clusters (k)'); ylabel('Mean Silhouette Score');
  204. yline(0.5, 'g--', 'Good Structure'); % Reference line
  205. grid on;
  207. sgtitle('Determining Optimal Cluster Number');
  210. %%
  211. fprintf('Running K-Means Clustering...\n');
  212. [idx, ~] = kmeans(X_z, k_clusters, 'Replicates', 5);
  213. MasterEvents.Cluster = idx;
  214. cmap = lines(k_clusters);
  216. % Separation Metrics
  217. sil_values = silhouette(X_z, idx); GlobalSeparationIndex = mean(sil_values);
  218. num_vars = size(X_z, 2); FishersRatio = zeros(1, num_vars);
  219. X1 = X_z(idx==1, :); X2 = X_z(idx==2, :);
  220. for v = 1:num_vars, m1=mean(X1(:,v)); m2=mean(X2(:,v)); v1=var(X1(:,v)); v2=var(X2(:,v)); FishersRatio(v)=((m1-m2)^2)/(v1+v2); end
  221. ClusterStats.Silhouette = GlobalSeparationIndex; ClusterStats.FishersRatio = FishersRatio;
  224. %% ====================================================================
  225. %% SECTION 5: BASIC PLOTS (Quality Checks)
  226. %% ====================================================================
  228. % Quality
  229. f_stat = figure('Name', 'Cluster Quality', 'Color', 'w', 'Position', [50 50 1000 500]);
  230. FigList(end+1) = struct('h', f_stat, 'name', 'Cluster_Metrics');
  231. subplot(1, 3, 1); silhouette(X_z, idx); title(sprintf('Separation: %.3f', GlobalSeparationIndex));
  232. subplot(1, 3, [2 3]); bar(FishersRatio); set(gca,'XTick',1:num_vars,'XTickLabel',VarNames,'XTickLabelRotation',45); title('Parameter Importance'); grid on;
  234. % Correlation
  235. f_corr = figure('Name', 'Feature Correlation', 'Color', 'w', 'Position', [100 100 600 500]);
  236. FigList(end+1) = struct('h', f_corr, 'name', 'Feature_Correlation');
  237. heatmap(VarNames, VarNames, corr(X), 'Colormap', jet, 'ColorLimits', [-1 1]);
  239. % Main Vis
  240. f_main = figure('Name','Main Cluster Analysis','Color','w', 'Position', [50 50 1400 700]);
  241. FigList(end+1) = struct('h', f_main, 'name', 'Main_Visualization');
  242. if size(X_z,1) > 2000, samp=randsample(size(X_z,1),2000); else, samp=(1:size(X_z,1))'; end
  244. X_in=X_z(samp,:); idx_tsne=idx(samp); try, Y = tsne(X_in); tsne_success=true; catch, tsne_success=false; end
  246. subplot(2,4,1); if UseFeatures.TemplateMatch, gscatter(MasterEvents.Duration, MasterEvents.TemplateMatch, idx, cmap); ylabel('Shape Score'); else, gscatter(MasterEvents.Duration, MasterEvents.MeanVm, idx, cmap); ylabel('MeanVm'); end; title('Clusters'); grid on; legend('off');
  247. subplot(2,4,[2 3]); cents = zeros(k_clusters, size(X_z,2)); for c=1:k_clusters, cents(c,:) = mean(X_z(idx==c,:),1); end; plot(cents','-o', 'LineWidth', 2); set(gca, 'XTick', 1:numel(VarNames), 'XTickLabel', VarNames); title('Z-Score Profiles'); grid on;
  248. subplot(2,4,4); if tsne_success, gscatter(Y(:,1),Y(:,2),idx_tsne,cmap,'.',10); title('t-SNE (Cluster)'); legend('off'); end
  249. subplot(2,4,5); hold on; edges = linspace(-1,1,20); for c=1:k_clusters, histogram(MasterEvents.SignedDist(idx==c), edges, 'FaceColor', cmap(c,:), 'FaceAlpha', 0.4); end; title('Spatial Dist');
  250. subplot(2,4,8); if tsne_success, [~,~,sid_map] = unique(MasterEvents.SessionID(samp)); scatter(Y(:,1), Y(:,2), 15, sid_map, 'filled', 'MarkerFaceAlpha', 0.7); colormap(gca, turbo); title('t-SNE (Session)'); end
  251. subplot(2,4,6); plot(linspace(0, 1, length(TemplateTrace)), TemplateTrace, 'k', 'LineWidth', 2); title('Global Template'); axis tight;
  253. % Raw Traces
  254. for c = 1:k_clusters
  255. f_trace = figure('Name', sprintf('Cluster %d Raw Traces',c), 'Color', 'w', 'Position', [100 100 800 800]);
  256. FigList(end+1) = struct('h', f_trace, 'name', sprintf('Raw_Traces_Cluster_%d', c));
  257. t = tiledlayout(5,5, 'TileSpacing','none', 'Padding', 'compact');
  258. evs = MasterEvents(MasterEvents.Cluster==c,:);
  259. n_show = min(25, height(evs));
  260. p_idx = randsample(height(evs), n_show);
  262. for i=1:numel(p_idx)
  263. nexttile;
  264. tr = evs.Trace{p_idx(i)};
  265. plot(tr, 'Color', cmap(c,:), 'LineWidth', 1.5);
  266. yline(evs.PreVm(p_idx(i)), 'k:', 'LineWidth', 1);
  267. axis off;
  269. % --- SCALE BAR (Adjusted for 24ms events) ---
  270. if i == numel(p_idx)
  271. hold on;
  272. sr = 20000; % Sampling Rate
  273. x_sec = 0.05; % <--- CHANGED to 50ms (0.05s)
  274. y_volts = 0.02; % 20 mV
  276. % Convert to units
  277. x_len = x_sec * sr;
  278. y_len = y_volts;
  280. % Position: Bottom-Right
  281. yl = ylim; xl = xlim;
  282. x_start = xl(2) - x_len - (diff(xl)*0.1);
  283. y_start = yl(1) + (diff(yl)*0.1);
  285. % Draw L-Bar
  286. plot([x_start, x_start + x_len], [y_start, y_start], 'k-', 'LineWidth', 2);
  287. plot([x_start + x_len, x_start + x_len], [y_start, y_start + y_len], 'k-', 'LineWidth', 2);
  289. % Labels
  290. text(x_start + x_len/2, y_start - (diff(yl)*0.1), '50ms', ...
  291. 'HorizontalAlignment', 'center', 'FontSize', 8, 'FontWeight', 'bold');
  292. text(x_start + x_len + (diff(xl)*0.05), y_start + y_len/2, '20mV', ...
  293. 'Rotation', 90, 'VerticalAlignment', 'middle', 'FontSize', 8, 'FontWeight', 'bold');
  294. end
  295. end
  296. sgtitle(sprintf('Cluster %d Examples (n=%d)', c, n_show));
  297. end
  300. %% ====================================================================
  301. %% SECTION 6: MULTI-ANCHOR SPATIAL ANALYSIS (Normalized)
  302. %% ====================================================================
  303. fprintf('Running Spatial Analysis (Normalized per Session)...\n');
  304. nSess = numel(unique(MasterEvents.SessionID)); uSess = unique(MasterEvents.SessionID);
  305. NewEdges = linspace(-1, 1, n_spatial_bins + 1); NewCenters = (NewEdges(1:end-1) + NewEdges(2:end)) / 2;
  306. mask_near = abs(NewCenters) <= 0.4;
  307. PlotData.Spatial = struct();
  309. for c = 1:k_clusters
  310. ClusEvents = MasterEvents(MasterEvents.Cluster == c, :);
  311. PooledAbs=[]; PooledNorm=[]; SessAbsMean=[]; SessAbsSEM=[]; SessNormMean=[]; SessNormSEM=[];
  312. Stats_Near=nan(nSess, size(AnchorMap,1)); Stats_Far=nan(nSess, size(AnchorMap,1));
  313. AnchorDataStruct = struct();
  315. for a = 1:size(AnchorMap,1)
  316. ancName = AnchorMap{a,1}; colName = AnchorMap{a,2}; occField = AnchorMap{a,3};
  317. SessCurve_Abs = nan(nSess, n_spatial_bins); SessCurve_Norm = nan(nSess, n_spatial_bins);
  318. GlobalNumer = zeros(1, n_spatial_bins); GlobalDenom = zeros(1, n_spatial_bins);
  320. for s = 1:nSess
  321. sid = uSess(s);
  322. sIdx=0; for k=1:length(SessionStruct), tid=SessionStruct(k).SessionID; if iscell(tid), tid=tid{1}; end; if tid==sid, sIdx=k; break; end; end
  323. if sIdx==0, continue; end
  324. S = SessionStruct(sIdx); OccData = [];
  325. if strcmpi(ancName, 'Reward'), if isfield(S, 'Occupancy'), OccData = S.Occupancy; end
  326. else, if isfield(S, 'AnchorOccupancy') && isfield(S.AnchorOccupancy, occField), OccData = S.AnchorOccupancy.(occField); end; end
  327. if isempty(OccData) || ~isstruct(OccData), continue; end
  329. if strcmpi(Filter.Movement, 'RunOnly'), if isfield(OccData, 'Hist_Run'), h=OccData.Hist_Run; else, h=[]; end
  330. else, if isfield(OccData, 'Hist_All'), h=OccData.Hist_All; else, h=[]; end; end
  331. if isempty(h) || sum(h) < 1e-6, continue; end
  333. OldEdges = linspace(-1, 1, length(h) + 1); OldCenters = (OldEdges(1:end-1) + OldEdges(2:end)) / 2;
  334. occ_new = interp1(OldCenters, h, NewCenters, 'linear', 0);
  335. occ_final = occ_new * (mean(diff(NewCenters))/mean(diff(OldCenters)));
  336. GlobalDenom = GlobalDenom + occ_final;
  338. s_evs = ClusEvents(ClusEvents.SessionID == sid, :);
  339. if ~isempty(s_evs)
  340. dists = s_evs.(colName); durs = s_evs.Duration;
  341. valid = ~isnan(dists) & abs(dists) <= 1.0; dists = dists(valid); durs = durs(valid);
  342. [~,~,b] = histcounts(dists, NewEdges);
  343. num = zeros(1, n_spatial_bins); for k_ev=1:length(b), if b(k_ev)>0, num(b(k_ev)) = num(b(k_ev)) + durs(k_ev); end; end
  344. GlobalNumer = GlobalNumer + num;
  346. rate = num ./ max(occ_final, 1e-6);
  347. SessCurve_Abs(s,:) = rate;
  348. if sum(rate)>0, norm_prof = rate/sum(rate); SessCurve_Norm(s,:) = norm_prof;
  349. Stats_Near(s,a) = sum(norm_prof(mask_near)); Stats_Far(s,a) = sum(norm_prof(~mask_near));
  350. else, SessCurve_Norm(s,:) = 0; Stats_Near(s,a)=0; Stats_Far(s,a)=0; end
  351. else
  352. SessCurve_Abs(s,:) = 0; SessCurve_Norm(s,:) = 0; Stats_Near(s,a)=0; Stats_Far(s,a)=0;
  353. end
  354. end
  356. P_Abs = GlobalNumer ./ max(GlobalDenom, 1e-6); if sum(P_Abs)>0, P_Norm = P_Abs / sum(P_Abs); else, P_Norm=P_Abs; end
  357. PooledAbs(:,a)=P_Abs; PooledNorm(:,a)=P_Norm;
  358. SessAbsMean(:,a)=mean(SessCurve_Abs,1,'omitnan'); SessAbsSEM(:,a)=std(SessCurve_Abs,0,1,'omitnan')./sqrt(sum(~isnan(SessCurve_Abs(:,1))));
  359. SessNormMean(:,a)=mean(SessCurve_Norm,1,'omitnan'); SessNormSEM(:,a)=std(SessCurve_Norm,0,1,'omitnan')./sqrt(sum(~isnan(SessCurve_Norm(:,1))));
  361. % SAVE SESSION DATA (RAW & NORM)
  362. AnchorDataStruct(a).Name = ancName;
  363. AnchorDataStruct(a).Raw = SessCurve_Abs; % Absolute Rate (Hz)
  364. AnchorDataStruct(a).Norm = SessCurve_Norm; % Normalized Probability
  365. end
  367. % Plotting
  368. f_comb = figure('Name', sprintf('Cluster %d Spatial', c), 'Color', 'w', 'Position', [50 50 1400 900]);
  369. FigList(end+1) = struct('h', f_comb, 'name', sprintf('Cluster%d_Spatial_Anchors', c));
  370. t = tiledlayout(2, 3, 'TileSpacing', 'compact', 'Padding', 'compact');
  371. sgtitle(sprintf('Cluster %d (%s) - Normalized Stats', c, Filter.Movement));
  373. nexttile; hold on; for a=1:size(AnchorMap,1), plot(NewCenters, PooledNorm(:,a), '-o', 'Color', AncColors(a,:), 'LineWidth', 2); end; title('Pooled Normalized'); xline(0,'k--');
  374. nexttile; hold on; for a=1:size(AnchorMap,1), mu=SessNormMean(:,a)'; se=SessNormSEM(:,a)'; fill([NewCenters fliplr(NewCenters)], [mu+se fliplr(mu-se)], AncColors(a,:), 'FaceAlpha', 0.15, 'EdgeColor', 'none'); plot(NewCenters, mu, 'Color', AncColors(a,:), 'LineWidth', 2); end; title('Avg Normalized (+/- SEM)'); xline(0,'k--');
  375. nexttile; hold on; plot_distribution_scatter(Stats_Near, AncColors, AnchorMap(:,1)); title('Near Anchor Prob'); p_n=perform_stats(Stats_Near); subtitle(sprintf('Friedman p=%.3e',p_n));
  376. nexttile; hold on; plot_distribution_scatter(Stats_Far, AncColors, AnchorMap(:,1)); title('Far Anchor Prob'); p_f=perform_stats(Stats_Far); subtitle(sprintf('Friedman p=%.3e',p_f));
  378. PlotData.Spatial.(sprintf('C%d',c)).PooledAbs = PooledAbs; PlotData.Spatial.(sprintf('C%d',c)).PooledNorm = PooledNorm;
  379. PlotData.Spatial.(sprintf('C%d',c)).StatsNear = Stats_Near; PlotData.Spatial.(sprintf('C%d',c)).StatsFar = Stats_Far;
  380. PlotData.Spatial.(sprintf('C%d',c)).AnchorData = AnchorDataStruct;
  381. end
  384. %% ====================================================================
  385. %% SECTION 7: EXPORT INDIVIDUAL SESSION CURVES TO EXCEL
  386. %% ====================================================================
  387. fprintf('\n--- EXPORTING SESSION CURVES TO EXCEL ---\n');
  388. xlFile = fullfile(save_dir, 'Session_Spatial_Curves.xlsx');
  389. if exist(xlFile, 'file'), delete(xlFile); end
  391. % Get Spatial Axis for Headers
  392. binHeaders = cell(1, n_spatial_bins);
  393. for b=1:n_spatial_bins, binHeaders{b} = sprintf('Bin_%d', b); end
  395. for c = 1:k_clusters
  396. cName = sprintf('C%d', c);
  397. if ~isfield(PlotData.Spatial, cName), continue; end
  399. AData = PlotData.Spatial.(cName).AnchorData;
  401. % Create Tables for Raw and Norm
  402. % Format: SessionID | Reward_Bin1... | Reward_Bin10 | Speed_Bin1...
  404. for a = 1:length(AData)
  405. ancName = AData(a).Name;
  406. rawMat = AData(a).Raw;
  407. normMat = AData(a).Norm;
  409. % Sanitize Anchor Name for Sheet
  410. validName = regexprep(ancName, '[^a-zA-Z0-9]', '');
  411. validName = validName(1:min(10, length(validName))); % Keep short
  413. % Create Tables
  414. T_Raw = array2table([uSess, rawMat], 'VariableNames', [{'SessionID'}, binHeaders]);
  415. T_Norm = array2table([uSess, normMat], 'VariableNames', [{'SessionID'}, binHeaders]);
  417. % Write Sheets
  418. sheetRaw = sprintf('%s_%s_Raw', cName, validName);
  419. sheetNorm = sprintf('%s_%s_Norm', cName, validName);
  421. writetable(T_Raw, xlFile, 'Sheet', sheetRaw);
  422. writetable(T_Norm, xlFile, 'Sheet', sheetNorm);
  423. end
  424. end
  425. fprintf('Excel Export Saved: %s\n', xlFile);
  427. %% ====================================================================
  428. %% SECTION 8: SAVING FIGURES & MAT FILE
  429. %% ====================================================================
  430. fprintf('\n--- SAVING MATLAB DATA ---\n');
  431. for i = 1:length(FigList)
  432. if isvalid(FigList(i).h)
  433. fname = fullfile(save_dir, FigList(i).name);
  434. saveas(FigList(i).h, [fname '.fig']);
  435. exportgraphics(FigList(i).h, [fname '.png'], 'Resolution', 300);
  436. end
  437. end
  438. save(fullfile(save_dir, 'Clustering_Results.mat'), 'MasterEvents', 'X', 'X_z', 'VarNames', 'ClusterStats', 'PlotData', 'Filter', 'idx', 'SessionStruct');
  439. ExportTable = removevars(MasterEvents, {'Trace'});
  440. writetable(ExportTable, fullfile(save_dir, 'Events_With_Clusters.csv'));
  441. save(fullfile(save_dir, 'Plot_Data_Curves.mat'), 'PlotData');
  443. fprintf('Analysis Complete. Results saved in: %s\n', save_dir);
  444. msgbox(['Analysis done! Saved to: ' save_dir]);
  445. end
  447. function p = perform_stats(Mat), valid=~any(isnan(Mat),2); Mat=Mat(valid,:); if sum(valid)>5, p=friedman(Mat,1,'off'); else, p=NaN; end, end
  448. function plot_distribution_scatter(Mat, Colors, Labels), nGrps=size(Mat,2); boxplot(Mat, 'Labels', Labels, 'Colors', 'k', 'Symbol', ''); hold on; for i=1:nGrps, x=i+(rand(size(Mat,1),1)-0.5)*0.2; scatter(x, Mat(:,i), 25, Colors(i,:), 'filled', 'MarkerFaceAlpha', 0.6); m=nanmedian(Mat(:,i)); plot([i-0.25 i+0.25], [m m], 'k-', 'LineWidth', 2); end, grid on; end

plateau_clustering_analysis_v3_anchors.m, under CC-BY-4.0 · at the source

Overview

Authors: Aanchal Bhatia1,2, Mohamed Adel2, Christine Grienberger1,3
ORCID iDs: Christine Grienberger
  1. Brandeis University, Department of Biology, Waltham, MA 02453, USA
  2. These authors contributed equally
  3. Lead contact
Institutions: Brandeis University (United States)
Journal: Cell reports, volume 45, issue 8, article 117794
Dates: published online 5 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1016/j.celrep.2026.117794 · PMID 42555356 · PMCID PMC13586939 · OpenAlex W7172538736
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: intracellular / patch clamp (modality)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Hippocampus, Burst firing, Subiculum, Reward Learning, Plateau Potentials, Goal-directed Navigation, In Vivo Whole-cell Recordings, Cp: Neuroscience
Topic: Neurobiology and Insect Physiology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: NINDS NIH HHS (T32 NS007292); National Institute of Neurological Disorders and Stroke (T32 NS007292); A. O. Smith Foundation; National Institutes of Health; National Institute of Mental Health (1DP2 MH136393); NIMH NIH HHS (DP2 MH136393); Richard and Susan Smith Family Foundation
Citations: not cited yet (Europe PMC); 69 references in the paper
Research resources: Mouse: C57BL/6J RRID:IMSR_JAX:000664, MATLAB (2021a, 2023b) RRID:SCR_001622, Graphpad Prism software (version 11.0.2) RRID:SCR_002798

Abstract

The hippocampus is essential for spatial learning, yet how its principal output structure, the subiculum, encodes behaviorally relevant information during navigation remains poorly understood. Because subicular neurons exhibit prominent burst firing, understanding how this bursting arises during behavior is critical. Using in vivo whole-cell recordings from dorsal subiculum neurons in head-fixed mice performing goal-directed navigation, we show that many bursts arise from plateau events. Plateaus were reduced by NMDA receptor blockade, indicating a dependence on synaptic input. Organizing plateaus by membrane potential preceding their onset revealed distinct behavioral associations. Plateaus initiated from hyperpolarized potentials clustered preferentially near reward locations, whereas those initiated from more depolarized potentials were more spatially distributed. Reward-related clustering required a fixed, learned reward location and was dynamically rearranged while learning a new reward location in a familiar environment. Together, these findings identify subicular plateaus as a cellular substrate of hippocampal bursting output, organized by membrane potential state.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

Zenodo 21177036

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (3)
Size: 5 files, 3 scripts
Software Heritage: not checked
Found in: the end of the paper
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
25 files
At the source:

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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;
  • 25 scripts, each with its path and the digest of its content;
  • 6 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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Versions

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Version 3, 28 September 2026

  • Publisher: n/a → Cell Press

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 8 keywords, 7 funders, 68 references, 3 RRIDs.

Cite

This paper

Bhatia, A., Adel, M., & Grienberger, C. (2026). Subicular plateaus signal reward locations during goal-directed behavior. Cell reports, 45(8), 117794. https://doi.org/10.1016/j.celrep.2026.117794

BibTeX

@article{bhatia2026subicular,
author = {Bhatia, Aanchal and Adel, Mohamed and Grienberger, Christine},
title = {{Subicular plateaus signal reward locations during goal-directed behavior}},
journal = {Cell reports},
year = {2026},
month = aug,
volume = {45},
number = {8},
pages = {117794},
publisher = {Cell Press},
issn = {2211-1247},
doi = {10.1016/j.celrep.2026.117794},
url = {https://doi.org/10.1016/j.celrep.2026.117794},
pmid = {42555356},
pmcid = {PMC13586939}
}

RIS

TY - JOUR
AU - Bhatia, Aanchal
AU - Adel, Mohamed
AU - Grienberger, Christine
TI - Subicular plateaus signal reward locations during goal-directed behavior
T2 - Cell reports
J2 - Cell Rep
PY - 2026
DA - 2026/08/05
VL - 45
IS - 8
SP - 117794
SN - 2211-1247
PB - Cell Press
DO - 10.1016/j.celrep.2026.117794
UR - https://doi.org/10.1016/j.celrep.2026.117794
LA - en
ER -

CSL-JSON

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"id": "10.1016/j.celrep.2026.117794",
"type": "article-journal",
"title": "Subicular plateaus signal reward locations during goal-directed behavior",
"container-title": "Cell reports",
"author": [
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"family": "Bhatia",
"given": "Aanchal"
},
{
"family": "Adel",
"given": "Mohamed"
},
{
"family": "Grienberger",
"given": "Christine"
}
],
"container-title-short": "Cell Rep",
"volume": "45",
"issue": "8",
"page": "117794",
"DOI": "10.1016/j.celrep.2026.117794",
"PMID": "42555356",
"PMCID": "PMC13586939",
"ISSN": "2211-1247",
"publisher": "Cell Press",
"URL": "https://doi.org/10.1016/j.celrep.2026.117794",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
5
]
]
}
}

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