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Directional dynamics in the entorhinal cortex of male mice driven by behavioral constraints.

Code ↔ Paper

4 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 4 matches
  1. [1] § Methods › Pre-processing of 2-photon calcium signal ↔ packages/MCMC/utilities/get_next_spikes.m, lines 1–24 · score 0.65 · calcium transients, standard deviation, amplitude, signals, fluorescence, noise
  2. [2] § Methods › Pre-processing of 2-photon calcium signal ↔ packages/oasis/constrained_oasisAR1.m, lines 1–150 · score 0.57 · standard deviation, OASIS, baseline, signals, fluorescence, noise
  3. [3] § Methods › Correlation between cell pairs ↔ codes/imaging/calculate_cellpair_correlation_matrices.m, lines 7–50 · score 0.57 · correlation matrix, halves, Pearson, HDF, HDI, HDA
  4. [4] § Results › Population code reconfiguration ↔ codes/imaging/calculate_cellpair_correlation_matrices.m, lines 7–50 · score 0.55 · correlation matrix, HDI cells, halves, HDF, HDA, activity

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 96 lines · 3.2 KB · no license · 2 matches

  2. % This code used to calculate cell-pair correlation matrices.
  3. % Used for Fig.2b-d
  5. clear;
  7. %%
  8. % load example data
  9. pth = fullfile('..','..','example_data');
  10. neuronactivity_of = load(fullfile(pth,'NeuronActivity_OF.mat')).NeuronActivity;
  11. neuronactivity_car = load(fullfile(pth,'NeuronActivity_CAR.mat')).NeuronActivity;
  12. behav_of = load(fullfile(pth,'behav_OF.mat')).behav;
  13. behav_car = load(fullfile(pth,'behav_CAR.mat')).behav;
  14. output_of = load(fullfile(pth,'output_OF.mat')).output;
  15. output_car = load(fullfile(pth,'output_CAR.mat')).output;
  16. fr_of = neuronactivity_of.Event_filtered_exp2;fr_of = fr_of>std(fr_of);
  17. fr_car = neuronactivity_car.Event_filtered_exp2;fr_car = fr_car>std(fr_car);
  18. hd_list = find(output_of.curMVL>0.44); % just for example, in Fig 2 we divided each session into two halves and use HDF/HDA/HDI cells
  19. fr_of = fr_of(:,hd_list); fr_car = fr_car(:,hd_list);
  21. % load peak direction during free condition
  22. pd = zeros(1,length(hd_list));
  23. for cellidx = 1:length(hd_list)
  24. stat_cell = output_of.tcStat{hd_list(cellidx)};
  25. pd(cellidx) = stat_cell.peakDirection;
  26. end
  28. % smooth in time
  29. gw=20;
  30. fr_of_smooth=cell2mat(arrayfun(@(z) mov_gauss1D(fr_of(:,z)',gw)',1:size(fr_of,2),'UniformOutput',0));
  31. fr_car_smooth=cell2mat(arrayfun(@(z) mov_gauss1D(fr_car(:,z)',gw)',1:size(fr_car,2),'UniformOutput',0));
  33. % reorder cells
  34. [~,indx]=sort(pd);
  35. fr_of_smooth=fr_of_smooth(:,indx);
  36. fr_car_smooth=fr_car_smooth(:,indx);
  38. % correlation matrices
  39. c_of=corr(fr_of_smooth,'Type','Pearson'); % correlation of spike train for each cell
  40. c_of(1:size(fr_of_smooth,2)+1:end)=nan;
  41. c_car=corr(fr_car_smooth,'Type','Pearson');
  42. c_car(1:size(fr_car_smooth,2)+1:end)=nan;
  44. % visualization
  45. subplot(121)
  46. imagesc(c_of);colormap('jet');
  47. caxis([min([c_of(:);c_car(:)]) max([c_of(:);c_car(:)])]);
  48. subplot(122)
  49. imagesc(c_car);colormap('jet');
  50. caxis([min([c_of(:);c_car(:)]) max([c_of(:);c_car(:)])]);
  52. %%
  53. % calculate in- vs. off-diagonal ratio
  54. % select nd elements around the diagonal and around the off diagonal
  55. N=size(fr_of_smooth,2);
  56. nd=max(1,round(N/20)); % select 10 % closest to/ furthest apart from the diagonal
  57. sel=false(N); seloff=sel;
  58. sel1=sel(:,1);
  59. sel1([2:1+nd N+1-nd:N])=true;
  60. sel1off=sel1([round(N/2)+1:N 1:round(N/2)]); % e.g. [16-29 1-15]
  61. for j=1:N
  62. sel(:,j)=sel1([N+2-j:N 1:N+1-j]);
  63. seloff(:,j)=sel1off([N+2-j:N 1:N+1-j]);
  64. end
  65. c_of = abs(c_of); c_car = abs(c_car);
  67. onoff_of = nanmean(c_of(sel(:)))/nanmean(c_of(seloff(:)));
  68. onoff_car = nanmean(c_car(sel(:)))/nanmean(c_car(seloff(:)));
  70. %%
  71. % calculate frobenius
  72. % normalization
  73. frob_of = c_of./nanmean(c_of,'all');
  74. frob_car = c_car./nanmean(c_car,'all');
  76. % calculate distance
  77. c_frobenius = [];
  78. for tempi1=1:N
  79. for tempi2=tempi1+1:N
  80. c_frobenius = [c_frobenius,(frob_of(tempi1,tempi2)-frob_car(tempi1,tempi2))^2];
  81. end
  82. end
  83. c_frobenius_session = nanmean(c_frobenius)
  85. % shuffle
  86. shuffletimes = 3; % just for example
  87. for shufi=1:shuffletimes
  88. rowrank=randperm(size(c_of,1));colrank=randperm(size(c_of,2));
  89. frob_of_shuf = frob_of(rowrank,colrank);
  90. c_frobenius_shuf = [];
  91. for tempi1=1:N
  92. for tempi2=tempi1+1:N
  93. c_frobenius_shuf = [c_frobenius_shuf,(frob_of_shuf(tempi1,tempi2)-frob_car(tempi1,tempi2))^2];
  94. end
  95. end
  96. end

calculate_cellpair_correlation_matrices.m at commit d88bc1f, no license · at the source

Overview

Authors: Ruojin Liu1,2,3, Jun Hao2,3,4, Xiang Zhang2,3, Shidan Wen2,3, Youran Yang1,2,3, Haiqian Cai1,2,3, Kai Gao1,2,3, Emilio Kropff5, Chenglin Miao1,2,3,6
ORCID iDs: Ruojin Liu, Xiang Zhang, Chenglin Miao
  1. Peking-Tsinghua Center for Life Sciences, Beijing, China
  2. PKU-IDG/McGovern Institute for Brain Research, Peking university, Beijing, China
  3. State Key Laboratory of Membrane Biology, School of Life Science, Peking university, Beijing, China
  4. Present Address: School of Life Sciences, Westlake University, Hangzhou, China
  5. Leloir Institute/IIBBA-CONICET, Buenos Aires, Argentina
  6. Chinese Institute for Brain Research (CIBR), Beijing, China
Institutions: Peking University (China); Center for Life Sciences (China); State Key Laboratory of Membrane Biology; Westlake University (China); Consejo Nacional de Investigaciones Científicas y Técnicas (Argentina); Fundación Instituto Leloir (Argentina); Chinese Institute for Brain Research (China)
Journal: Nature communications, volume 17, issue 1, article 3679
Dates: received 11 May 2025; accepted 24 February 2026; published online 9 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-70289-3 · PMID 41803123 · PMCID PMC13099953 · OpenAlex W7134254680
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), systems (subfield)
Methods: Connectivity, Statistics, Machine learning, Preprocessing, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Cortex, Spatial memory
MeSH: Behavior, Animal*, Entorhinal Cortex*, Neurons*, Animals, Male, Mice, Mice, Inbred C57BL (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 70 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.

Repositories

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

zhoupc/OASIS_matlab

License: GPL-3.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 6f42ebd0e2cbbbeeb2a73c537d2e02a54f5f34fc, 22 October 2020
Languages: MATLAB (851), C (71), JavaScript (9), C/C++ (4)
Size: 1,474 files, 935 scripts
Software Heritage: not archived
Found in: the text, “Pre-processing of 2-photon calcium signal”
Holds: README, license file, documentation
Not found: CITATION.cff, environment file, tests, continuous integration
Tools: Signal Processing Toolbox (4 files), Statistics and Machine Learning Toolbox (4 files), Image Processing Toolbox (2 files), Optimization Toolbox (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
937 files
At the source: github.com/zhoupc/OASIS_matlab

RuojinLiu/assisted_movement

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: d88bc1f23a139bbb23ba91218b8cb69a5c411625, 21 October 2025
Languages: MATLAB (10)
Size: 13 files, 10 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
11 files
At the source: github.com/RuojinLiu/assisted_movement

Code availability statement

The paper has a code 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/s41467-026-70289-3.

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;
  • 945 scripts, each with its path and the digest of its content;
  • 4 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

Datasets cited

Data availability statement

The paper has a 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/s41467-026-70289-3.

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, 9 authors, 2 keywords, 7 MeSH terms, 66 references.

Cite

This paper

Liu, R., Hao, J., Zhang, X., Wen, S., Yang, Y., Cai, H., Gao, K., Kropff, E., & Miao, C. (2026). Directional dynamics in the entorhinal cortex of male mice driven by behavioral constraints. Nature communications, 17(1), 3679. https://doi.org/10.1038/s41467-026-70289-3

BibTeX

@article{liu2026directional,
author = {Liu, Ruojin and Hao, Jun and Zhang, Xiang and Wen, Shidan and Yang, Youran and Cai, Haiqian and Gao, Kai and Kropff, Emilio and Miao, Chenglin},
title = {{Directional dynamics in the entorhinal cortex of male mice driven by behavioral constraints}},
journal = {Nature communications},
year = {2026},
month = mar,
volume = {17},
number = {1},
pages = {3679},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-70289-3},
url = {https://doi.org/10.1038/s41467-026-70289-3},
pmid = {41803123},
pmcid = {PMC13099953}
}

RIS

TY - JOUR
AU - Liu, Ruojin
AU - Hao, Jun
AU - Zhang, Xiang
AU - Wen, Shidan
AU - Yang, Youran
AU - Cai, Haiqian
AU - Gao, Kai
AU - Kropff, Emilio
AU - Miao, Chenglin
TI - Directional dynamics in the entorhinal cortex of male mice driven by behavioral constraints
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/03/09
VL - 17
IS - 1
SP - 3679
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-70289-3
UR - https://doi.org/10.1038/s41467-026-70289-3
LA - en
ER -

CSL-JSON

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