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Anterior lateral motor cortex enables contextual decision-making via dynamic reconfiguration of local circuits.

Code ↔ Paper

2 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.

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  1. [1] § STAR★METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Decoding and PCA analysis ↔ fitsvm8T_cos.m, lines 154–193 · score 0.77 · SVM weight vectors, sliding window, odor onset, matrix, cosine, bin
  2. [2] § RESULTS › Generalization of context at the level of choice neurons ↔ fitsvm8T_cos.m, lines 154–193 · score 0.52 · SVM weight vectors, odor onset, cosine

Paper

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The authors' code

MATLAB · 420 lines · 15 KB · no license · 2 matches

  1. function [cors_ab,cors_cd,cors_sh] = fitsvm8T_cos(Ninput,ds0,data0,prm,fe)
  2. % train svm with lick triggered dataset
  3. % train the decode on A/B data and test on C/D
  5. Nfold = 50; %number of iteration
  6. fold = 0.8; % 5 fold cross-validation
  7. if length(ds0) ~= length(data0)
  8. disp('input cell dimension mismatch!')
  9. end
  10. n_ss = length(ds0);
  11. d_wd = prm.d_wd;
  12. stp = prm.stp; st = prm.st; en = prm.en; binsize = prm.binsize;
  13. d_bins = round((d_wd(1)-st)/stp+2):round((d_wd(2)-st)/stp+1);
  14. n_bins = length(d_bins);
  15. cors_ab = nan(n_ss,n_bins);
  16. errs_ab = nan(n_ss,n_bins);
  17. cors_cd = nan(n_ss,n_bins);
  18. errs_cd = nan(n_ss,n_bins);
  19. for i = 1 %n_ss
  20. % fprintf('computing roc for session #%d\n',i);
  21. ds = ds0{i}; data = data0{i};
  23. if size(data,1) ~= size(ds,1)
  24. disp('number of trials mismatch!')
  25. end
  26. N = size(data,3); % number of units in current session
  27. Nmax = Ninput;
  28. if Nmax == 0
  29. Nmax = N;
  30. fprintf('Session #%d has %d units\n',i,N);
  31. elseif Nmax <= N
  32. fprintf('Session #%d has %d units\n',i,N);
  33. elseif Nmax > N
  34. fprintf('Session #%d has %d units, skipped\n',i,N);
  35. continue;
  36. end
  37. sel = ds.correct==1; % correct trials only
  38. % % sel = ds.miss ~= 1; % remove miss trials
  39. % % ds.error = ds.error | ds.xSwitch; % treat switch trials as error
  40. % % sel = ds.miss == 0 | ds.xSwitch == 0; % remove miss and switch trials
  41. % if kp_el == 0 % exclude early licks
  42. % sel = sel & ds.early_lick==0; % no early lick trials
  43. % end
  44. ds = ds(sel,:);
  45. data = data(sel,:,:);
  46. ds_ab = ds(ds.tt<4,:);
  47. ds_cd = ds(ds.tt>=4,:);
  49. data_ab = data(ds.tt<4,:,:);
  50. data_cd = data(ds.tt>=4,:,:);
  52. P = size(ds_ab,1); % number of correct control trials
  53. Ptrain = floor(fold*P); % number of trials for training
  54. ds_ab.AasSample = ds_ab.tt<=1;
  55. ds_cd.CasSample = ds_cd.tt<=5;
  56. ds_ab.AasTest = ismember(ds_ab.tt,[0 3 4 7]);
  57. ds_cd.AasTest = ismember(ds_cd.tt,[0 3 4 7]);
  59. switch fe
  60. case 'sample'
  61. labels_ab = ds_ab.AasSample;
  62. labels_cd = ds_cd.CasSample;
  63. case 'match'
  64. labels_ab = ds_ab.match;
  65. labels_cd = ds_cd.match;
  66. case 'test'
  67. labels_ab = ds_ab.AasTest;
  68. labels_cd = ds_cd.AasTest;
  69. case 'choice'
  70. labels_ab = ds_ab.left;
  71. labels_cd = ds_cd.left;
  72. case 'trialtype'
  73. labels_ab = ds_ab.trial_type;
  74. labels_cd = ds_cd.trial_type - 4;
  75. % case 'correct'
  76. % labels = ds.correct;
  77. case 'context'
  78. labels_ab = ds_ab.context;
  79. labels_cd = ds_cd.context;
  80. end
  81. labels_ab = double(labels_ab);
  82. labels_cd = double(labels_cd);
  83. % fr = squeeze(mean(data(:,wd_bins,:),2));
  85. % Initialize weight storage
  86. svm_weights = nan(n_bins, Nmax,Nfold); % Store SVM weights over time bins
  88. for j = 1:n_bins
  89. fprintf('bin %d of %d\n',j,n_bins);
  90. bi_en = d_bins(j);
  91. bi_st = bi_en - round(binsize/stp) + 1;
  92. cur_data_ab = data_ab(:,bi_st:bi_en,:);
  93. cur_data_cd = data_cd(:,bi_st:bi_en,:);
  94. fr_ab = squeeze(mean(cur_data_ab,2));
  95. fr_cd = squeeze(mean(cur_data_cd,2));
  96. cur_cor_ab = zeros(Nfold,1);
  97. cur_err_ab = zeros(Nfold,1);
  98. cur_cor_cd = zeros(Nfold,1);
  99. cur_err_cd = zeros(Nfold,1);
  100. cur_cor_sh = zeros(Nfold,1);
  101. cur_err_sh = zeros(Nfold,1);
  102. for fi = 1:Nfold
  103. indtrain = randsample(P,Ptrain); %indices of trials to use for training
  104. indtest = setdiff(1:P,indtrain); %indices for testing
  106. labelstrain_ab = labels_ab(indtrain);
  107. datatrain_ab = fr_ab(indtrain,:);
  109. labelstest_ab = labels_ab(indtest);
  110. datatest_ab = fr_ab(indtest,:);
  112. labelshuffle = labels_ab(randperm(length(labels_ab))');
  113. labelstrain_shuffle = labelshuffle(indtrain);
  114. labelstest_shuffle = labelshuffle(indtest);
  116. if Nmax ~= N
  117. cellinds = randsample(N,Nmax);
  118. datatrain_ab = datatrain_ab(:,cellinds);
  119. datatest_ab = datatest_ab(:,cellinds);
  120. end
  122. model = train(labelstrain_ab,sparse(datatrain_ab),'-q');
  123. svm_weights(j, :,fi) = model.w; % Store the weight vector for this time bin
  125. predtest_ab = predict(ones(length(labelstest_ab),1),...
  126. sparse(datatest_ab),model,'-q');
  127. cur_cor_ab(fi) = mean(predtest_ab == labelstest_ab);
  128. cur_err_ab(fi) = mean(predtest_ab ~= labelstest_ab);
  130. pred_cd = predict(ones(length(labels_cd),1),...
  131. sparse(fr_cd),model,'-q');
  132. cur_cor_cd(fi) = mean(pred_cd == labels_cd);
  133. cur_err_cd(fi) = mean(pred_cd ~= labels_cd);
  135. model = train(labelstrain_shuffle,sparse(datatrain_ab),'-q');
  136. predtest_shuffle = predict(ones(length(labelstest_shuffle),1),...
  137. sparse(datatest_ab),model,'-q');
  138. cur_cor_sh(fi) = mean(predtest_shuffle == labelstest_shuffle);
  139. cur_err_sh(fi) = mean(predtest_shuffle ~= labelstest_shuffle);
  141. end %end loop over folds
  142. cors_ab(i,j) = mean(cur_cor_ab);
  143. errs_ab(i,j) = mean(cur_err_ab);
  144. cors_cd(i,j) = mean(cur_cor_cd);
  145. errs_cd(i,j) = mean(cur_err_cd);
  146. cors_sh(i,j) = mean(cur_cor_sh);
  147. errs_sh(i,j) = mean(cur_err_sh);
  148. end % end loop over bins
  149. disp('Done!')
  150. weights = squeeze(mean(svm_weights,3));
  151. peak_perf = find(cors_ab(i,:) > 0.9);
  154. % Define before and after odor bins
  155. test_odor_bin = 36; %find(d_bins == 0, 1); %the location of test odor bin starts, which is 42
  156. before_idx = 11:test_odor_bin-1; % cut the baseline, starts from sample onset
  157. after_idx = test_odor_bin:test_odor_bin+length(before_idx)-1;
  158. % Inputs:
  159. % - SVM_weights: [time_points x feature_dim] matrix of SVM weight vectors
  160. % - time: Vector of time points
  161. % - test_odor_time: Time of test odor onset
  162. before_plot_idx = intersect(before_idx, peak_perf);
  163. after_plot_idx = intersect(after_idx, peak_perf);
  165. window_size = 5; %Number of time bins to average over
  166. before_plot_idx = before_plot_idx - 11 - 4;
  167. after_plot_idx = after_plot_idx - 11 - 4;
  168. num_timepoints_before = length(before_idx);
  169. num_timepoints_after = length(after_idx);
  170. feature_dim = size(weights, 2);
  172. % Compute the number of sliding window steps
  173. num_windows_before = num_timepoints_before - window_size + 1;
  174. num_windows_after = num_timepoints_after - window_size + 1;
  176. % Initialize sliding window weight matrix
  177. windowed_weights_before = nan(num_windows_before, feature_dim); %nan(num_timepoints_before, feature_dim); %
  178. windowed_weights_after = nan(num_windows_after, feature_dim); %nan(num_timepoints_after, feature_dim); %
  179. % Apply sliding window averaging
  180. for w = 1:num_windows_before %num_timepoints_before %
  181. windowed_weights_before(w, :) = mean(weights(before_idx(w):before_idx(w+window_size-1), :), 1); %weights(before_idx(w), :) ;%
  182. end
  184. for w = 1:num_windows_before %num_timepoints_after
  185. windowed_weights_after(w, :) = mean(weights(after_idx(w):after_idx(w+window_size-1), :), 1); %weights(after_idx(w), :) ;%
  186. end
  188. % Initialize cosine similarity matrix
  189. combined_matrix_before = nan(num_windows_before, num_windows_before);
  190. combined_matrix_after = nan(num_windows_after, num_windows_after);
  191. combined_matrix = nan(num_windows_after+ num_windows_after, num_windows_after+ num_windows_after);
  192. % Function to compute cosine similarity
  193. cosine_sim = @(w1, w2) dot(w1, w2) / (norm(w1) * norm(w2));
  195. % Compute cosine similarity Before vs Before
  196. for w = 1:num_windows_before
  197. for x = w:num_windows_before
  198. combined_matrix(w, x) = cosine_sim(windowed_weights_before(w, :), windowed_weights_before(x, :));
  199. combined_matrix(x, w) = combined_matrix(w,x); % Symmetric
  200. end
  201. end
  203. % Compute cosine similarity After vs After
  204. for w = 1:num_windows_after
  205. for x = w:num_windows_after
  206. combined_matrix(num_windows_before+w, num_windows_before+x) = cosine_sim(windowed_weights_after(w, :), windowed_weights_after(x, :));
  207. combined_matrix(num_windows_before+x, num_windows_before+w) = combined_matrix(num_windows_before+w, num_windows_before+x);
  208. end
  209. end
  211. % Compute cosine similarity Before vs After
  212. for w = 1:num_windows_before
  213. for x = 1:num_windows_after
  214. combined_matrix(w, num_windows_before+x) = cosine_sim(windowed_weights_before(w, :), windowed_weights_after(x, :));
  215. combined_matrix(num_windows_before+x, w) = combined_matrix(w, num_windows_before+x);
  216. end
  217. end
  219. % Plot combined heatmap
  220. kernel_size = 1; % Adjust as needed
  221. % calculate the delta cosine similarity
  222. for c = 1:size(combined_matrix,2) %num_windows_before
  223. dcosim1 (c) = mean(combined_matrix(1:num_windows_before,c)) - mean(combined_matrix(num_windows_before+1:end,c));
  224. end
  225. for c = 1:size(combined_matrix,1)
  226. dcosim2 (c) = mean(combined_matrix(c,1:num_windows_before)) - mean(combined_matrix(c, num_windows_before+1:end));
  227. end
  229. figure
  230. plot(dcosim1)
  231. figure
  232. plot(dcosim2)
  233. % Apply Gaussian smoothing
  234. smoothed_matrix = imgaussfilt(combined_matrix, kernel_size);
  236. % try the smoothed matrix
  237. for c = 1:size(smoothed_matrix,2) %num_windows_before
  238. sdcosim1 (c) = mean(smoothed_matrix(1:num_windows_before,c)) - mean(smoothed_matrix(num_windows_before+1:end,c));
  239. end
  240. for c = 1:size(smoothed_matrix,1)
  241. sdcosim2 (c) = mean(smoothed_matrix(c,1:num_windows_before)) - mean(smoothed_matrix(c, num_windows_before+1:end));
  242. end
  243. figure
  244. plot(sdcosim1)
  245. figure
  246. plot(sdcosim2)
  249. figure;
  250. imagesc(smoothed_matrix);
  251. colorbar;
  252. xlabel('Time');
  253. ylabel('Time');
  254. title('Combined Cosine Similarity Matrix');
  256. figure;
  257. subplot(2,2,1)
  258. imagesc(smoothed_matrix(before_plot_idx, before_plot_idx));
  259. colorbar;
  260. xlabel('Time');
  261. ylabel('Time');
  262. title('Combined Cosine Similarity Matrix');
  264. subplot(2,2,2)
  265. imagesc(smoothed_matrix(before_plot_idx, after_plot_idx));
  266. colorbar;
  267. xlabel('Time');
  268. ylabel('Time');
  269. title('Combined Cosine Similarity Matrix');
  271. subplot(2,2,3)
  272. imagesc(smoothed_matrix((27:40),(27:40))) %(after_plot_idx, after_plot_idx));
  273. colorbar;
  274. xlabel('Time');
  275. ylabel('Time');
  276. title('Combined Cosine Similarity Matrix');
  278. subplot(2,2,4)
  279. imagesc(smoothed_matrix(after_plot_idx, before_plot_idx));
  280. colorbar;
  281. xlabel('Time');
  282. ylabel('Time');
  283. title('Combined Cosine Similarity Matrix');
  285. % Extract the relevant cosine similarity subsets from the smoothed matrix
  286. bef_bef = smoothed_matrix(before_plot_idx, before_plot_idx);
  287. bef_aft = smoothed_matrix(before_plot_idx, after_plot_idx);
  288. aft_aft = smoothed_matrix(after_plot_idx, after_plot_idx);
  290. % Flatten the upper triangle (excluding diagonal) of each block
  291. mask_upper = @(M) M(triu(true(size(M)), 1));
  292. sim_bef_bef = mask_upper(bef_bef);
  293. sim_bef_aft = bef_aft(:);
  294. sim_aft_aft = mask_upper(aft_aft);
  295. mean_bef_bef = mean(bef_bef);
  296. mean_bef_aft = mean(bef_aft);
  297. mean_aft_aft = mean(aft_aft);
  298. % Compute actual mean differences
  299. real_diff = mean(sim_bef_bef) - mean(sim_bef_aft);
  300. real_diff2 = mean(sim_aft_aft) - mean(sim_bef_aft);
  302. % Combine and prepare for permutation
  303. combined = [sim_bef_bef; sim_bef_aft];
  304. n_bef = length(sim_bef_bef);
  305. n_aft = length(sim_bef_aft);
  307. combined2 = [sim_aft_aft; sim_bef_aft];
  308. n_aa = length(sim_aft_aft);
  309. n_cross = length(sim_bef_aft);
  311. % Monte Carlo permutation test
  312. % n_perm = 1000;
  313. % perm_diffs = nan(n_perm, 1);
  314. % perm_diffs2 = nan(n_perm, 1);
  315. % for i = 1:n_perm
  316. % % Before-Before vs Before-After
  317. % perm = combined(randperm(length(combined)));
  318. % perm_diffs(i) = mean(perm(1:n_bef)) - mean(perm(n_bef+1:end));
  319. %
  320. % % After-After vs Before-After
  321. % perm2 = combined2(randperm(length(combined2)));
  322. % perm_diffs2(i) = mean(perm2(1:n_aa)) - mean(perm2(n_aa+1:end));
  323. % end
  324. %
  325. % % Calculate two-tailed p-values
  326. % p_val = mean(abs(perm_diffs) >= abs(real_diff));
  327. % p_val2 = mean(abs(perm_diffs2) >= abs(real_diff2));
  328. %
  329. % % Display results
  330. % fprintf('Monte Carlo p-value (Before-Before vs Before-After): %.4f\n', p_val);
  331. % fprintf('Monte Carlo p-value (After-After vs Before-After): %.4f\n', p_val2);
  333. % Plot null distributions
  334. % figure;
  335. % subplot(1,2,1);
  336. % histogram(perm_diffs, 30);
  337. % xline(real_diff, 'r', 'LineWidth', 2);
  338. % title('Null: Before-Before vs Before-After');
  339. % xlabel('\Delta Cosine Similarity'); ylabel('Count');
  340. %
  341. % subplot(1,2,2);
  342. % histogram(perm_diffs2, 30);
  343. % xline(real_diff2, 'r', 'LineWidth', 2);
  344. % title('Null: After-After vs Before-After');
  345. % xlabel('\Delta Cosine Similarity'); ylabel('Count');
  347. % % Add separation lines for before vs after
  348. % test_odor_idx = find(adjusted_time >= test_odor_time, 1);
  349. % hold on;
  350. % plot([test_odor_idx, test_odor_idx], ylim, 'k--', 'LineWidth', 1);
  351. % plot(xlim, [test_odor_idx, test_odor_idx], 'k--', 'LineWidth', 1);
  352. % hold off;
  354. % max_lag = 20;
  355. % cosine_sim_before = nan(length(before_idx), max_lag);
  356. % cosine_sim_after = nan(length(after_idx), max_lag);
  357. %
  358. % % Function to compute cosine similarity
  359. % cosine_sim = @(w1, w2) dot(w1, w2) / (norm(w1) * norm(w2));
  360. %
  361. % % Compute Cosine Similarity Before Odor
  362. % for t = 1:length(before_idx)
  363. % for lag = 1:max_lag
  364. % t_lag = t + lag;
  365. % if t_lag <= length(before_idx)
  366. % cosine_sim_before(t, lag) = cosine_sim(weights(before_idx(t), :), weights(before_idx(t_lag), :));
  367. % end
  368. % end
  369. % end
  370. %
  371. % % Compute Cosine Similarity After Odor
  372. % for t = 1:length(after_idx)
  373. % for lag = 1:max_lag
  374. % t_lag = t + lag;
  375. % if t_lag <= length(after_idx)
  376. % cosine_sim_after(t, lag) = cosine_sim(weights(after_idx(t), :), weights(after_idx(t_lag), :));
  377. % end
  378. % end
  379. % end
  381. % Plot results
  382. % figure;
  383. % subplot(1,2,1);
  384. % imagesc(1:max_lag, before_idx, cosine_sim_before);
  385. % colorbar;
  386. % xlabel('Lag (time bins)');
  387. % ylabel('Time (before odor onset)');
  388. % title('Cosine Similarity Before Test Odor');
  389. %
  390. % subplot(1,2,2);
  391. % imagesc(1:max_lag, after_idx, cosine_sim_after);
  392. % colorbar;
  393. % xlabel('Lag (time bins)');
  394. % ylabel('Time (after odor onset)');
  395. % title('Cosine Similarity After Test Odor');
  396. %
  397. % % Initialize matrix to store cosine similarity
  398. % cosine_sim_between = nan(length(before_idx), length(after_idx));
  399. %
  400. % % Function to compute cosine similarity
  401. % cosine_sim = @(w1, w2) dot(w1, w2) / (norm(w1) * norm(w2));
  402. %
  403. % % Compute cosine similarity between before and after odor onset
  404. % for be = 1:length(before_idx)
  405. % for af = 1:length(after_idx)
  406. % cosine_sim_between(be, af) = cosine_sim(weights(before_idx(be), :), weights(after_idx(af), :));
  407. % end
  408. % end
  409. %
  410. % % Plot heatmap of cosine similarity
  411. % figure;
  412. % imagesc(after_idx, before_idx, cosine_sim_between);
  413. % colorbar;
  414. % xlabel('Time (after odor onset)');
  415. % ylabel('Time (before odor onset)');
  416. % title('Cosine Similarity Between Before and After Odor');
  420. end % end loop over sessions

fitsvm8T_cos.m at commit 5e9d9ad, no license · at the source

Overview

Authors: Jia Shen1,2, Nuttida Rungratsameetaweemana3, Prayshita Sharma4, Darcy S. Peterka1,5, Herbert Zheng Wu6, Michael N. Shadlen1,2,5,7,8,9
ORCID iDs: Herbert Zheng Wu, Michael N. Shadlen
  1. Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
  2. Department of Neuroscience, Columbia University, New York, NY, USA
  3. Department of Biomedical Engineering, Columbia University, New York, NY, USA
  4. PhD Program in Computational Neuroscience, University of Chicago, Chicago, IL, USA
  5. The Kavli Institute for Brain Science, Columbia University, New York, NY, USA
  6. Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
  7. Howard Hughes Medical Institute, Chevy Chase, MD, USA
  8. Grossman Center for the Statistics of the Mind, Columbia University, New York, NY, USA
  9. Lead contact
Institutions: Mortimer B. Zuckerman Mind Brain Behavior Institute (United States); Columbia University (United States); University of Chicago (United States); Icahn School of Medicine at Mount Sinai (United States); Howard Hughes Medical Institute (United States)
Journal: Cell reports, volume 45, issue 6, article 117456
Dates: published online 4 June 2026; in print 23 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.celrep.2026.117456 · PMID 42247294 · PMCID PMC13419430 · OpenAlex W7163506527
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: optical imaging (calcium, voltage, 2-photon) (modality), mouse (organism)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging, Single-unit activity, calcium imaging, Connectivity
Keywords: Cognitive Flexibility, Two-photon Calcium Imaging, Secondary Motor Cortex, Stimulus-response Mapping, Context-dependent Decision-making, Anterior Lateral Motor Cortex, Cp: Neuroscience, Circuit Reconfiguration, Contingency Neurons
MeSH: Decision Making*, Motor Cortex*, Animals, Male, Mice, Mice, Inbred C57BL, Neurons, Odorants, Optogenetics (* major topic)
Topic: Motor Control and Adaptation (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute of Mental Health (R01MH133039); National Institutes of Health (/ NIDA F32); NIMH NIH HHS (R01 MH122513, R01 MH133039); National Institute on Drug Abuse (5F32DA060751, F32); Columbia University; Howard Hughes Medical Institute; Air Force Office of Scientific Research (21RT0878); NIDA NIH HHS (F32 DA060751); NINDS NIH HHS (R01 NS113113)
Citations: not cited yet (Europe PMC); 38 references in the paper
Research resources: Mouse: wild type C57BL/6J RRID:IMSR_JAX

Abstract

Cognitive operations often require flexible implementation of stimulus-response contingencies, depending on context. We developed an olfactory task in which mice learned to associate a test odor with a directional lick response, conditional on a preceding context odor drawn from a different odor set. Two-photon imaging shows that the anterior lateral motor cortex (ALM) contains distinct populations encoding context, test odors, and choice. Optogenetic silencing during the context and delay periods impairs performance, suggesting that ALM contributes to configuring the appropriate contingency. Although context odors that instruct the same mapping are represented by separate populations, their influence converges at the level of choice-selective neurons. A subpopulation of these neurons exhibit dual selectivity for context and choice, forming what we term “contingency neurons.” These findings suggest that ALM supports flexible behavior not by abstracting over context cues but by dynamically reconfiguring local circuits to route sensory input to the appropriate motor output.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

Zenodo 19828551

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (4 files), Image Processing Toolbox (1 file), shadedErrorBar (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
4 files
At the source: doi.org/10.5281/zenodo.19828551

asjessie/shadlen-lab

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 5e9d9ad46c52be5342924a38144ff5f59621566a, 27 April 2026
Languages: MATLAB (4)
Size: 4 files, 4 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (4 files), Image Processing Toolbox (1 file), shadedErrorBar (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
4 files
At the source: github.com/asjessie/Shadlen-lab/tree/v1

The paper's code and data availability statement is in the Data section.

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 8 scripts, each with its path and the digest of its content;
  • 2 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 and code availability

All data reported in this paper will be shared by the lead contact upon request.

All original code has been deposited at GitHub/Zenodo at https://doi.org/10.5281/zenodo.19828551 and is publicly available as of the date of publication.

Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

SUPPLEMENTAL INFORMATION

Supplemental information can be found online at https://doi.org/10.1016/j.celrep.2026.117456.

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 2, 28 September 2026

  • Publisher: n/a → Cell Press

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 9 keywords, 9 MeSH terms, 9 funders, 35 references, 1 RRID.

Cite

This paper

Shen, J., Rungratsameetaweemana, N., Sharma, P., Peterka, D. S., Wu, H. Z., & Shadlen, M. N. (2026). Anterior lateral motor cortex enables contextual decision-making via dynamic reconfiguration of local circuits. Cell reports, 45(6), 117456. https://doi.org/10.1016/j.celrep.2026.117456

BibTeX

@article{shen2026anterior,
author = {Shen, Jia and Rungratsameetaweemana, Nuttida and Sharma, Prayshita and Peterka, Darcy S. and Wu, Herbert Zheng and Shadlen, Michael N.},
title = {{Anterior lateral motor cortex enables contextual decision-making via dynamic reconfiguration of local circuits}},
journal = {Cell reports},
year = {2026},
month = jun,
volume = {45},
number = {6},
pages = {117456},
publisher = {Cell Press},
issn = {2211-1247},
doi = {10.1016/j.celrep.2026.117456},
url = {https://doi.org/10.1016/j.celrep.2026.117456},
pmid = {42247294},
pmcid = {PMC13419430}
}

RIS

TY - JOUR
AU - Shen, Jia
AU - Rungratsameetaweemana, Nuttida
AU - Sharma, Prayshita
AU - Peterka, Darcy S.
AU - Wu, Herbert Zheng
AU - Shadlen, Michael N.
TI - Anterior lateral motor cortex enables contextual decision-making via dynamic reconfiguration of local circuits
T2 - Cell reports
J2 - Cell Rep
PY - 2026
DA - 2026/06/04
VL - 45
IS - 6
SP - 117456
SN - 2211-1247
PB - Cell Press
DO - 10.1016/j.celrep.2026.117456
UR - https://doi.org/10.1016/j.celrep.2026.117456
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.celrep.2026.117456",
"type": "article-journal",
"title": "Anterior lateral motor cortex enables contextual decision-making via dynamic reconfiguration of local circuits",
"container-title": "Cell reports",
"author": [
{
"family": "Shen",
"given": "Jia"
},
{
"family": "Rungratsameetaweemana",
"given": "Nuttida"
},
{
"family": "Sharma",
"given": "Prayshita"
},
{
"family": "Peterka",
"given": "Darcy S."
},
{
"family": "Wu",
"given": "Herbert Zheng"
},
{
"family": "Shadlen",
"given": "Michael N."
}
],
"container-title-short": "Cell Rep",
"volume": "45",
"issue": "6",
"page": "117456",
"DOI": "10.1016/j.celrep.2026.117456",
"PMID": "42247294",
"PMCID": "PMC13419430",
"ISSN": "2211-1247",
"publisher": "Cell Press",
"URL": "https://doi.org/10.1016/j.celrep.2026.117456",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
4
]
]
}
}

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

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