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Neural population dynamics and temporal context cells in macaque medial parietal cortex support temporal order memory.

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 › mPPC neurons mediate temporal order judgment processes ↔ Figure_2B.m, lines 195–284 · score 0.59 · motor responses, TOJ decision, TOJ cells, epoch, Poisson, GLM
  2. [2] § Quantification and statistical analysis › Representational similarity analysis ↔ Figure 5H.m, the whole file · a weak match · score 0.56 · Pearson correlation, score normalized
  3. [3] § Quantification and statistical analysis › Population temporal decoding using linear discriminant analysis ↔ Figure5Ato5F_S2.m, lines 1–47 · score 0.56 · Temporal decoding, iteration, classifier, discriminant, permutation, trained
  4. [4] § Results › Cell ensembles carry information about passage of time during encoding and predict subsequent memory performance ↔ Figure 5H.m, the whole file · a weak match · score 0.55 · Pearson correlations, behavioral accuracy, scored, predicted
  5. [5] § Results › Cell ensembles carry information about passage of time during encoding and predict subsequent memory performance ↔ Figure5Ato5F_S2.m, lines 1–47 · score 0.54 · linear discriminant, Temporal decoding, S2, accuracy, trained, bin
  6. [6] § Quantification and statistical analysis › Poisson generalized linear models for TOJ-related neural activity › Control GLM Incorporating Eye-Movement Parameters. ↔ Figure S3.m, lines 80–127 · score 0.51 · eye movement, TOJ period, saccade, fixation, duration, neurons

Paper

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The authors' code

MATLAB · 140 lines · 5.4 KB · CC-BY-4.0 · 2 matches

  1. % MATLAB Code for Correlation Analysis with Normalization
  2. % Analyzing correlation between 3rd column (mean_cv_abserror ÷ 4) and last column (beh_Accuracy)
  3. clear all
  5. % Read the Excel file
  6. filename = 'Jupiter_Mercury.xlsx';
  7. data = readtable(filename);
  9. % Display basic information about the data
  10. fprintf('Data loaded successfully!\n');
  11. fprintf('Number of rows: %d\n', height(data));
  12. fprintf('Number of columns: %d\n', width(data));
  13. fprintf('Column names: ');
  14. disp(data.Properties.VariableNames);
  16. % Extract the 3rd and last columns
  17. third_col_raw = data{:, 1}; % mean_cv_abserror (raw)
  18. third_col = third_col_raw ;
  19. last_col = data{:, 5}; % beh_Accuracy
  21. fprintf('\nOriginal Data Summary:\n');
  22. fprintf('=====================\n');
  23. fprintf('Third column (mean_cv_abserror) - raw:\n');
  24. fprintf(' Min: %.6f, Max: %.6f, Mean: %.6f, Std: %.6f\n', ...
  25. min(third_col_raw), max(third_col_raw), mean(third_col_raw), std(third_col_raw));
  26. fprintf('Third column (mean_cv_abserror) - after dividing by 4:\n');
  27. fprintf(' Min: %.6f, Max: %.6f, Mean: %.6f, Std: %.6f\n', ...
  28. min(third_col), max(third_col), mean(third_col), std(third_col));
  29. fprintf('Last column (beh_Accuracy):\n');
  30. fprintf(' Min: %.6f, Max: %.6f, Mean: %.6f, Std: %.6f\n', ...
  31. min(last_col), max(last_col), mean(last_col), std(last_col));
  33. % Method 1: Standardization (Z-score normalization) - RECOMMENDED
  34. % Transform to mean=0, std=1
  35. third_col_std = (third_col - mean(third_col)) / std(third_col);
  36. last_col_std = (last_col - mean(last_col)) / std(last_col);
  38. % Method 2: Min-Max normalization (0-1 scaling) - Alternative
  39. third_col_minmax = (third_col - min(third_col)) / (max(third_col) - min(third_col));
  40. last_col_minmax = (last_col - min(last_col)) / (max(last_col) - min(last_col));
  42. % Verify standardization worked correctly
  43. fprintf('\nAfter Standardization:\n');
  44. fprintf('======================\n');
  45. fprintf('Third column: Mean = %.6f, Std = %.6f\n', ...
  46. mean(third_col_std), std(third_col_std));
  47. fprintf('Last column: Mean = %.6f, Std = %.6f\n', ...
  48. mean(last_col_std), std(last_col_std));
  50. % Calculate correlations
  51. fprintf('\nCorrelation Results:\n');
  52. fprintf('===================\n');
  54. % Original data correlation
  55. [r_original, p_original] = corrcoef(third_col, last_col);
  56. fprintf('Original data correlation: r = %.4f, p-value = %.4f\n', ...
  57. r_original(1,2), p_original(1,2));
  59. % Standardized data correlation
  60. [r_std, p_std] = corrcoef(third_col_std, last_col_std);
  61. fprintf('Standardized data correlation: r = %.4f, p-value = %.4f\n', ...
  62. r_std(1,2), p_std(1,2));
  64. % Min-max normalized data correlation
  65. [r_minmax, p_minmax] = corrcoef(third_col_minmax, last_col_minmax);
  66. fprintf('Min-max normalized correlation: r = %.4f, p-value = %.4f\n', ...
  67. r_minmax(1,2), p_minmax(1,2));
  69. % Note: All three correlations should be identical since Pearson correlation
  70. % is scale-invariant
  71. fprintf('\nNote: All correlation values should be identical since Pearson correlation is scale-invariant.\n');
  73. % Create visualization with fitting line and standard error
  74. figure('Position', [100, 100, 800, 600]);
  76. % Calculate linear fit
  77. p = polyfit(third_col, last_col, 1); % Linear polynomial fit
  78. x_fit = linspace(min(third_col), max(third_col), 100);
  79. y_fit = polyval(p, x_fit);
  81. % Calculate standard error of the regression
  82. y_pred = polyval(p, third_col);
  83. residuals = last_col - y_pred;
  84. n = length(third_col);
  85. s_residual = sqrt(sum(residuals.^2) / (n - 2)); % Residual standard error
  87. % Calculate standard error for prediction intervals
  88. x_mean = mean(third_col);
  89. ss_x = sum((third_col - x_mean).^2);
  90. se_fit = s_residual * sqrt(1/n + (x_fit - x_mean).^2 / ss_x);
  92. % Plot the data points
  93. scatter(third_col, last_col, 60, 'filled', 'MarkerFaceColor', [0.2 0.4 0.8], ...
  94. 'MarkerEdgeColor', 'k', 'LineWidth', 0.5, 'MarkerFaceAlpha', 0.7);
  95. hold on;
  97. % Plot confidence interval (standard error shadow)
  98. fill([x_fit, fliplr(x_fit)], [y_fit + se_fit, fliplr(y_fit - se_fit)], ...
  99. [0.8 0.8 0.8], 'FaceAlpha', 0.3, 'EdgeColor', 'none');
  101. % Plot the fitting line
  102. plot(x_fit, y_fit, 'k-', 'LineWidth', 2);
  104. % Formatting
  105. xlabel('Mean Cross Validation Accuracy', 'FontSize', 12, 'FontWeight', 'bold');
  106. ylabel('Behavior Accuracy', 'FontSize', 12, 'FontWeight', 'bold');
  107. title(sprintf('Correlation Analysis: mean\_cv\_abserror (÷4) vs beh\_Accuracy\nr = %.3f, p = %.3f', ...
  108. r_original(1,2), p_original(1,2)), 'FontSize', 14, 'FontWeight', 'bold');
  109. %grid on;
  110. %grid minor;
  112. % Add legend
  113. legend('Data points', 'Standard Error', 'Fit line', 'Location', 'best');
  115. % Set axis properties
  116. set(gca, 'FontSize', 11);
  117. box on;
  119. % Statistical significance interpretation
  120. fprintf('\nStatistical Significance:\n');
  121. fprintf('========================\n');
  122. if p_original(1,2) < 0.001
  123. fprintf('Correlation is highly significant (p < 0.001)\n');
  124. elseif p_original(1,2) < 0.01
  125. fprintf('Correlation is very significant (p < 0.01)\n');
  126. elseif p_original(1,2) < 0.05
  127. fprintf('Correlation is significant (p < 0.05)\n');
  128. else
  129. fprintf('Correlation is not statistically significant (p >= 0.05)\n');
  130. end
  132. % Effect size interpretation
  133. abs_r = abs(r_original(1,2));
  134. if abs_r >= 0.7
  135. fprintf('Effect size: Large correlation (|r| >= 0.7)\n');
  136. elseif abs_r >= 0.3
  137. fprintf('Effect size: Medium correlation (0.3 <= |r| < 0.7)\n');
  138. else
  139. fprintf('Effect size: Small correlation (|r| < 0.3)\n');
  140. end

Figure 5H.m, under CC-BY-4.0 · at the source

Overview

Authors: Shuzhen Zuo1,2,3, Chenyu Wang4,5, Lei Wang1,6, Zhiyong Jin1,4, Xufeng Zhou1,4, Ning Su1,4, Jianhua Liu1,4, Thomas J McHugh2,3, Makoto Kusunoki7,8, Sze Chai Kwok1,4
ORCID iDs: Sze Chai Kwok
  1. Shanghai Key Laboratory of Brain Functional Genomics, Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
  2. Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
  3. Laboratory for Circuit and Behavioral Physiology, RIKEN Center for Brain Science, Wako-shi, Saitama, Japan
  4. Phylo-Cognition Laboratory, Duke Kunshan University - The First People’s Hospital of Kunshan Joint Brain Sciences Laboratory, Division of Natural and Applied Sciences, Digital Innovation Research Center, Duke Institute for Brain Sciences, Duke Kunshan University, Kunshan, Jiangsu, China
  5. Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts, United States of America
  6. Institute for Translational Neuroscience of Nantong First People’s Hospital, Affiliated Hospital of Southeast University, Jiangsu, China
  7. MRC Cognition and Brain Sciences Unit, University of Cambridge, United Kingdom
  8. Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
Institutions: RIKEN Center for Brain Science (Japan); East China Normal University (China); The University of Tokyo (Japan); Boston University (United States); First People's Hospital of Kunshan (China); Duke Kunshan University (China); Southeast University (China); MRC Cognition and Brain Sciences Unit (United Kingdom); University of Cambridge (United Kingdom); University of Oxford (United Kingdom)
Journal: PLoS biology, volume 24, issue 4, article e3003759
Dates: received 26 January 2026; accepted 1 April 2026; published online 17 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pbio.3003759 · PMID 41996451 · PMCID PMC13108878 · OpenAlex W7154742082
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: non-human primate (organism)
Methods: Spectral & time-frequency, Connectivity, Statistics, Machine learning, Preprocessing, Graphs, fMRI & imaging, Single-unit activity, calcium imaging, Physiology & signal measures
MeSH: Memory*, Memory, Episodic*, Neurons*, Parietal Lobe*, Animals, Macaca mulatta, Male, Saccades (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Kunshan Municipal Government (24KKSGR017); Duke University Provost Fund for Duke‒DKU Collaborations (25KINTL013)
Citations: cited by 1 paper (Europe PMC); 70 references in the paper

Abstract

Episodic memory involves encoding and remembering the order of events experienced over time. Previous work examining the mechanisms of temporal order memories has focused primarily on the hippocampus and prefrontal cortices, with comparatively less attention paid to population-level memory signals in the medial posterior parietal cortex (mPPC). Combining in vivo multi-unit electrophysiology and a temporal order judgment task with naturalistic cinematic material in macaques, we show that population activity in mPPC exhibits temporally structured dynamics during both encoding and retrieval. During encoding, mPPC neuronal ensembles exhibit gradually evolving activity patterns consistent with temporal context representations embedded in the unfolding video episodes, whereas during retrieval these neurons engage in coordinated, synchronous activity preceding memory-guided decisions. Moreover, trial-by-trial similarity between population activity patterns during encoding and retrieval predicts temporal order judgment performance. A separate control experiment further ruled out eye saccades, fixation patterns, and scan paths as confounding factors contributing to the observed neural dynamics. Together, these findings suggest that mPPC contributes to temporal order memory through population-level representations that integrate temporally extended experience with retrieval-related decision processes, rather than through simple sensory-driven or motor-related responses.

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 6 matches between paragraphs and lines of code.

Zenodo 19280257

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: MATLAB (29)
Size: 60 files, 29 scripts
Software Heritage: not checked
Found in: “Data Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (11 files), shadedErrorBar (4 files)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
30 files
At the source: doi.org/10.5281/zenodo.19280257

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 29 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);
  • 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

Analysis code and processed data supporting the conclusions of this study are deposited on Zenodo, see https://doi.org/10.5281/zenodo.19280257.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 8 MeSH terms, 2 funders, 70 references.

Cite

This paper

Zuo, S., Wang, C., Wang, L., Jin, Z., Zhou, X., Su, N., Liu, J., McHugh, T. J., Kusunoki, M., & Kwok, S. C. (2026). Neural population dynamics and temporal context cells in macaque medial parietal cortex support temporal order memory. PLoS biology, 24(4), e3003759. https://doi.org/10.1371/journal.pbio.3003759

BibTeX

@article{zuo2026neural,
author = {Zuo, Shuzhen and Wang, Chenyu and Wang, Lei and Jin, Zhiyong and Zhou, Xufeng and Su, Ning and Liu, Jianhua and McHugh, Thomas J and Kusunoki, Makoto and Kwok, Sze Chai},
title = {{Neural population dynamics and temporal context cells in macaque medial parietal cortex support temporal order memory}},
journal = {PLoS biology},
year = {2026},
month = apr,
volume = {24},
number = {4},
pages = {e3003759},
publisher = {PLOS},
issn = {1544-9173},
doi = {10.1371/journal.pbio.3003759},
url = {https://doi.org/10.1371/journal.pbio.3003759},
pmid = {41996451},
pmcid = {PMC13108878}
}

RIS

TY - JOUR
AU - Zuo, Shuzhen
AU - Wang, Chenyu
AU - Wang, Lei
AU - Jin, Zhiyong
AU - Zhou, Xufeng
AU - Su, Ning
AU - Liu, Jianhua
AU - McHugh, Thomas J
AU - Kusunoki, Makoto
AU - Kwok, Sze Chai
TI - Neural population dynamics and temporal context cells in macaque medial parietal cortex support temporal order memory
T2 - PLoS biology
J2 - PLoS Biol
PY - 2026
DA - 2026/04/17
VL - 24
IS - 4
SP - e3003759
SN - 1544-9173
PB - PLOS
DO - 10.1371/journal.pbio.3003759
UR - https://doi.org/10.1371/journal.pbio.3003759
LA - en
ER -

CSL-JSON

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