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Global error signal guides local optimization in mismatch calculation.

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.

The 2 matches
  1. [1] § Methods › Data analysis ↔ analyze/vml_Fig2_John.m, lines 187–241 · score 0.58 · fitted GMMs, favor, BIC, pseudo, component, Gaussian
  2. [2] § Results › Three-factor learning rule optimizes prediction-error computation ↔ scripts/class_main_nPE_perturbe.py, lines 13–66 · score 0.50 · anti Hebbian, Hebbian learning, match, training

Paper

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The authors' code

MATLAB · 291 lines · 10 KB · no license · 1 match

  1. clearvars -except agg* proj*
  2. close all
  3. %%
  4. pp=fieldnames(aggregateData);
  5. iP=1;
  6. corr_thr=0.02;
  7. %%
  8. parameters_main;
  9. sumIDX=35:45;
  10. normalization=19:29;
  11. TP = 1;
  12. cl=[-.2 .2];
  13. grayscale=.97;
  15. pp=fieldnames(aggregateData);
  16. % ?? for iP=7
  17. for iP=1
  18. IDX = aggregateData.(pp{iP}).expType{TP};
  19. MM = aggregateData.(pp{iP}).avgMM{TP};
  20. MMR= aggregateData.(pp{iP}).avgMMRandom{TP};
  21. CM = aggregateData.(pp{iP}).corrM{TP};
  22. CP = aggregateData.(pp{iP}).corrP{TP};
  23. FF = aggregateData.(pp{iP}).ROIinfo{TP};
  24. %MM=MM-MMR;
  25. mm=mean(MM(meanPost,:))-mean(MM(meanPre,:));
  26. tmp=aggregateData.(pp{iP}).avgMM{TP}-aggregateData.(pp{iP}).avgMMRandom{TP};
  27. mm=mean(tmp(meanPost,:))-mean(tmp(meanPre,:));
  29. FF=FF(:,1);
  31. for type=0:1
  32. % type=1;
  33. figure(type+1)
  35. %mm=mean(MM(sumIDX,:));
  36. actIDX=FF>4;
  37. realP=~isnan(CP);
  38. realM=~isnan(CM);
  40. d = -0.6:0.025:0.6;
  42. MIDX_CT=IDX==type & actIDX & realP & realM;
  45. aCT=CM(MIDX_CT);
  46. bCT=CP(MIDX_CT);
  47. fprintf('Corr: %0.3f',corr(aCT,bCT))
  48. cCT=mm(MIDX_CT);
  49. m=length(cCT);
  50. n = fix(m/2);
  51. x = n~=(m/2);
  52. %r = [(0:1:n-1)/n,ones(1,n+x)];
  53. %g = [(0:1:n-1)/n,ones(1,x),(n-1:-1:0)/n];
  54. %bl = [ones(1,n+x),(n-1:-1:0)/n];
  55. r = grayscale*[(0:1:n-1)/n,ones(1,n+x)];
  56. %r(r<.5)=.5;
  57. bl = grayscale*[(0:1:n-1)/n,ones(1,x),(n-1:-1:0)/n];
  58. g = grayscale*[ones(1,n+x),((n-1:-1:0)/n)];
  59. g(g<.65)=.65;
  61. cmap = [r(:),g(:),bl(:)];
  63. [~,sidx]=sort(cCT);
  64. %cCT(cCT<0)=2*cCT(cCT<0);
  65. sidx=1:length(cCT);
  67. % subplot(1,2,1+type)
  68. hold on
  69. scatter(bCT(sidx),aCT(sidx),15,cCT(sidx),'filled','MarkerEdgeColor', 'k')
  71. % Compute angles in radians
  72. % norm_bCT=bCT(sidx)-mean(bCT(sidx));
  73. % norm_aCT=aCT(sidx)-mean(aCT(sidx));
  74. norm_bCT=bCT(sidx);
  75. norm_aCT=aCT(sidx);
  76. dist_all= sqrt(norm_bCT.^2+norm_aCT.^2);
  79. set(gcf, 'Position', [100, 100, 400, 300]); % [x, y, width, height]
  81. set(gca,'CLim',cl,'XTick',0,'YTick',0)
  82. colormap(cmap);
  83. % colorbar
  84. axis image
  85. xlim([-.7 .7])
  86. ylim([-.7 .7])
  87. hold on
  88. grid on
  90. file_name = ['./Figures/' pp{1} '_type_' num2str(type),'_scattor' ]; % Use the first field name for naming
  91. % Save the figure in different formats
  92. saveas(gcf, [file_name, '.fig']); % Save as MATLAB .fig file
  93. saveas(gcf, [file_name, '.svg']); % Save as SVG
  95. sig_thr=(norm_bCT.^2+norm_aCT.^2)>corr_thr;
  96. % sig_thr=(norm_bCT.^2+norm_aCT.^2)<corr_thr;
  97. num_cell=sum(sig_thr);
  98. norm_aCT_sig=norm_aCT(sig_thr);
  99. norm_bCT_sig=norm_bCT(sig_thr);
  100. % theta_corr = atan2(norm_bCT(sig_thr),norm_aCT(sig_thr) );
  101. theta_corr = atan2(norm_bCT,norm_aCT );
  102. % figure(666) % Find the optimal threshold.
  103. % clf
  104. %
  105. %
  106. % % Define more bins for higher resolution
  107. % num_bins = 50; % Increase number of bins
  108. % bin_edges = logspace(log10(min(dist_all)), log10(max(dist_all)), num_bins); % Log-spaced bins
  109. %
  110. % % Plot histogram
  111. % histogram(dist_all, 'BinEdges', bin_edges, 'Normalization', 'probability', ...
  112. % 'FaceColor', 'blue', 'EdgeColor', 'black', 'FaceAlpha', 0.7);
  113. %
  114. % set(gca, 'XScale', 'log');
  115. % ylabel('Probability Density');
  116. % title('Histogram of distance (Log-Scale X-Axis)');
  117. %
  118. % % Set axes properties
  119. % set(gca, 'FontSize', 12);
  122. % Adjust angles that are less than -3/4 * pi
  123. theta_corr(theta_corr < -3/4 * pi) = theta_corr(theta_corr < -3/4 * pi) + 2 * pi;
  125. % Create figure
  126. bin_width = pi / 10.0;
  127. bin_edges = -3 * pi / 4 : bin_width : (5/4 * pi + bin_width); % Define bins
  128. % Compute weighted histogram manually
  129. [counts, edges] = histcounts(theta_corr, bin_edges, 'Normalization', 'probability');
  130. weighted_counts = accumarray(discretize(theta_corr, bin_edges), dist_all, [length(counts), 1], @sum);
  131. weighted_counts = weighted_counts/sum(weighted_counts) ;
  134. % Use bin_centers as the "data", weighted by weighted_counts
  135. bin_centers = (edges(1:end-1) + edges(2:end)) / 2;
  138. % Initialize pseudo sample container
  139. theta_pseudo = [];
  140. sum_pseudo_weight=0;
  141. sum_weight= sum(dist_all);
  142. rng(1); % Set seed for reproducibility
  143. % Loop through each point
  144. for i = 1:length(theta_corr)
  145. % Repeat theta_corr(i) 100 times with inclusion probability dist_all(i)
  146. % if theta_corr(i)/pi-1/4>3/4 || theta_corr(i)/pi-1/4<-3/4
  147. % continue
  148. % end
  149. if theta_corr(i)/pi-1/4>1/2 || theta_corr(i)/pi-1/4<-1/2
  150. continue
  151. end
  152. if dist_all(i)<0.1
  153. continue
  154. end
  155. sum_pseudo_weight=sum_pseudo_weight+dist_all(i);
  156. n_repeat = sum(rand(1, 200) < dist_all(i)); % Number of times to include this point
  157. theta_pseudo = [theta_pseudo; repmat(theta_corr(i), n_repeat, 1)];
  158. end
  159. % Get BIC scores
  160. Neff = (sum(dist_all))^2 / sum(dist_all.^2);
  161. options= statset('MaxIter', 1000);
  162. gm1 = fitgmdist(theta_pseudo-pi/4, 1, 'RegularizationValue', 1e-5,'Options', options);
  163. gm2 = fitgmdist(theta_pseudo-pi/4, 2, 'RegularizationValue', 1e-5,'Options', options);
  165. bic1 = gm1.BIC;
  166. bic2 = gm2.BIC;
  167. aic1 = gm1.AIC;
  168. aic2 = gm2.AIC;
  169. logL1 = gm1.NegativeLogLikelihood * -1;
  170. logL2 = gm2.NegativeLogLikelihood * -1;
  171. k1 = gm1.NumComponents;
  172. k2 = gm2.NumComponents;
  173. % bic1 = -2 * logL1 + k1 * log(Neff);
  174. % bic2 = -2 * logL2 + k2 * log(Neff);
  176. bic1_eff = -2 * logL1 + k1 * log(Neff);
  177. bic2_eff = -2 * logL2 + k2 * log(Neff);
  179. fprintf('BIC (1 comp): %.2f\n', bic1);
  180. fprintf('BIC (2 comp): %.2f\n', bic2);
  181. fprintf('AIC (1 comp): %.2f\n', aic1);
  182. fprintf('AIC (2 comp): %.2f\n', aic2);
  183. fprintf('BIC eff (1 comp): %.2f\n', bic1_eff);
  184. fprintf('BIC eff (2 comp): %.2f\n', bic2_eff);
  187. figure(type+21)
  188. clf
  189. histogram(theta_pseudo-pi/4, 'Normalization', 'pdf', 'BinWidth', 0.05);
  190. set(gcf, 'Position', [200,300,560, 420]); % [left, bottom, width, height]
  191. hold on;
  192. xgrid = linspace(min(theta_pseudo-pi/4), max(theta_pseudo-pi/4), 200)';
  193. y1 = pdf(gm1, xgrid);
  194. y2 = pdf(gm2, xgrid);
  195. plot(xgrid, y1, 'r-', 'LineWidth', 2);
  196. plot(xgrid, y2, 'b-', 'LineWidth', 2);
  197. % Plot vertical lines at component means
  198. means1 = gm1.mu;
  199. means2 = gm2.mu;
  201. for m = 1:length(means1)
  202. xline(means1(m), 'r--', 'LineWidth', 1.5);
  203. end
  205. for m = 1:length(means2)
  206. xline(means2(m), 'b--', 'LineWidth', 1.5);
  207. end
  209. legend('Data', '1-comp GMM', '2-comp GMM');
  210. xticks([-pi / 2, 0, pi/2]);
  211. xticklabels({'-\pi/2', '0', '\pi/2'});
  212. set(gca, 'FontSize', 18, 'XMinorTick', 'on'); % Adjust font size if needed
  214. % Add BIC difference in the title
  215. BIC_diff = gm1.BIC - gm2.BIC; % Positive value favors gm2 (2-component)
  216. if BIC_diff > 0
  217. preferred = '2-comp GMM';
  218. else
  219. preferred = '1-comp GMM';
  220. end
  221. box off
  222. title(sprintf('Gaussian Mixture Model Fit (ΔBIC = %.2f, Preferred: %s)', BIC_diff, preferred));
  223. file_name = ['./Figures/' pp{1} '_type_' num2str(type),'_fittedGMM' ]; % Use the first field name for naming
  224. % Save the figure in different formats
  225. saveas(gcf, [file_name, '.fig']); % Save as MATLAB .fig file
  226. saveas(gcf, [file_name, '.svg']); % Save as SVG
  230. figure(type+11)
  231. clf
  232. hold on
  233. % bar((bin_edges(1:end-1)+bin_edges(2:end))-pi/4, weighted_counts/bin_width, 'FaceColor', 'blue', 'EdgeColor', 'black', 'FaceAlpha', 0.7, 'BarWidth', 1);
  234. bar(bin_edges(1:end-1)-pi/4+bin_width/2, weighted_counts/bin_width, 'FaceColor', 'blue', 'EdgeColor', 'black', 'FaceAlpha', 0.7, 'BarWidth', 1);
  236. set(gcf, 'Position', [200,300,300, 225]); % [left, bottom, width, height]
  238. xgrid=linspace(-pi, pi,200)';
  239. xgrid_width=2*pi/200;
  240. y1 = pdf(gm1, xgrid);
  241. y2 = pdf(gm2, xgrid);
  244. % y1_norm=y1;
  245. % y2_norm=y2;
  247. y1_norm=y1*sum_pseudo_weight/sum_weight;
  248. y2_norm=y2*sum_pseudo_weight/sum_weight;
  250. % plot(xgrid, y1_norm, 'k-', 'LineWidth', 2);
  251. % plot(xgrid, y2_norm, 'r-', 'LineWidth', 2);
  252. box off
  253. % % Plot histogram
  254. % histogram(theta_corr, 'BinEdges', bin_edges, 'Normalization', 'probability', ...
  255. % 'FaceColor', 'blue', 'EdgeColor', 'black', 'FaceAlpha', 0.7,'Weights',dist_all(sig_thr));
  257. % Customize x-ticks
  258. % xticks([-3 * pi / 4, -pi / 4, pi / 4, 3/4 * pi, 5/4 * pi]);
  259. xticks([-pi, -pi / 2, 0, pi/2, pi]);
  260. xticklabels({'-\pi', '-\pi/2', '0', '\pi/2', '\pi'});
  262. % Set axis labels (optional)
  264. hold off
  265. % Set axes properties
  266. set(gca, 'FontSize', 12, 'XMinorTick', 'on'); % Adjust font size if needed
  267. file_name = ['./Figures/' pp{1} '_type_' num2str(type),'_hist' ]; % Use the first field name for naming
  268. % Save the figure in different formats
  269. saveas(gcf, [file_name, '.fig']); % Save as MATLAB .fig file
  270. saveas(gcf, [file_name, '.svg']); % Save as SVG
  271. % Save raw data to .mat file
  272. % file_name = ['./Figures/' pp{1} '_type_' num2str(type) ]; % Use the first field name for naming
  274. % save([file_name, '_data.mat'], 'bCT', 'aCT');
  277. end
  278. end
  281. % % Silverman test.
  282. % [p, h_crit, h_boot] = silverman_test(theta_pseudo, dist_all,200);
  283. %
  284. % % Optional: visualize bootstrap distribution of critical bandwidths
  285. % figure;
  286. % histogram(h_boot, 'Normalization', 'pdf');
  287. % hold on;
  288. % xline(h_crit, 'r--', 'LineWidth', 2);
  289. % xlabel('Critical Bandwidth');
  290. % ylabel('PDF');
  291. % title(sprintf("Silverman's Test p = %.3f", p));

vml_Fig2_John.m at commit aac6733, no license · at the source

Overview

Authors: John Hongyu Meng1, Xiao-Jing Wang1
ORCID iDs: John Hongyu Meng, Xiao-Jing Wang
  1. Center for Neural Science, New York University,New York, NY USA
Institutions: New York University (United States)
Journal: Nature communications, volume 17, issue 1, article 3868
Dates: received 22 August 2025; accepted 24 February 2026; published online 12 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-70354-x · PMID 41820382 · PMCID PMC13125295 · OpenAlex W7135064761
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), mouse (organism)
Methods: Single-unit activity, calcium imaging, Connectivity, Smoothing, state filtering, decompositions
Keywords: Learning algorithms, Sensorimotor processing, Network models
MeSH: Models, Neurological*, Acoustic Stimulation, Animals, Learning, Mice, Neurons, Synapses (* major topic)
Topic: Statistical and numerical algorithms (Applied Mathematics, Mathematics), according to OpenAlex
Funding: ONR (N00014-23-1-2040); National Institute of Mental Health (R01MH062349)
Citations: cited by 2 papers (Europe PMC); 78 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

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

johnhongyumeng/DuetPredictiveCoding_Plasticity

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: aac67332eb4b7b0bfe459ff72d610b560b2eab64, 28 January 2026
Languages: Python (21), MATLAB (1)
Size: 33 files, 22 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (21 files), Matplotlib (15 files), SciPy (10 files), Brian 2 (4 files), Statistics and Machine Learning Toolbox (1 file), Numba (1 file), scikit-learn (1 file), seaborn (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
23 files
At the source: github.com/johnhongyumeng/DuetPredictiveCoding_Plasticity

Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-70354-x.

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-70354-x.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 3 keywords, 7 MeSH terms, 2 funders, 73 references.

Cite

This paper

Meng, J. H., & Wang, X.-J. (2026). Global error signal guides local optimization in mismatch calculation. Nature communications, 17(1), 3868. https://doi.org/10.1038/s41467-026-70354-x

BibTeX

@article{meng2026global,
author = {Meng, John Hongyu and Wang, Xiao-Jing},
title = {{Global error signal guides local optimization in mismatch calculation}},
journal = {Nature communications},
year = {2026},
month = mar,
volume = {17},
number = {1},
pages = {3868},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-70354-x},
url = {https://doi.org/10.1038/s41467-026-70354-x},
pmid = {41820382},
pmcid = {PMC13125295}
}

RIS

TY - JOUR
AU - Meng, John Hongyu
AU - Wang, Xiao-Jing
TI - Global error signal guides local optimization in mismatch calculation
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/03/12
VL - 17
IS - 1
SP - 3868
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-70354-x
UR - https://doi.org/10.1038/s41467-026-70354-x
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-70354-x",
"type": "article-journal",
"title": "Global error signal guides local optimization in mismatch calculation",
"container-title": "Nature communications",
"author": [
{
"family": "Meng",
"given": "John Hongyu"
},
{
"family": "Wang",
"given": "Xiao-Jing"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "3868",
"DOI": "10.1038/s41467-026-70354-x",
"PMID": "41820382",
"PMCID": "PMC13125295",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-70354-x",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
12
]
]
}
}

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