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A likelihood-based method for identifying replay from spike sequences.

Code ↔ Paper

3 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 3 matches
  1. [1] § Methods › Neuronal simulation ↔ 1/figure1.m, lines 1–12 · score 0.70 · random walk, peak firing, place field, track, firing rate, simulated
  2. [2] § Results › Validation with mouse calcium imaging ↔ 3/figure3.m, lines 91–163 · score 0.57 · filled bar, replay active, place cells, CCW, Scatter, Lseq
  3. [3] § Methods › Neuronal simulation ↔ 1/Simulation_f1.m, lines 14–34 · score 0.52 · generated spike, place field, Poisson, firing rate, simulated, position

Paper

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The authors' code

MATLAB · 141 lines · 5.4 KB · CC-BY-4.0 · 1 match

  1. %% Basic simulation parameters
  2. n_ne = 100; % Number of neurons
  3. c_L_path = 1000; % Track length (mm, 1000 mm = 1 m)
  4. c_dL_dir = 0.1; % Directed motion step size (mm per ms)
  5. c_fr0 = 1; % Baseline (spontaneous) firing rate (Hz)
  6. n_step = 2 * c_L_path / c_dL_dir; % Maximum steps
  7. x_pf0 = ((1:n_ne)' - 0.5) * c_L_path / n_ne; % regular place fields
  8. n_tr = 20; % Number of trials
  9. c_dL_rw = 1; % Random walk step size (mm/ms)
  10. c_fr_max = 9.99; % Peak firing rate (Hz)
  11. c_std_pf = 100; % Place field width
  12. c_coeff_pf = (c_fr_max - c_fr0) / normpdf(0,0,c_std_pf); % for normalization (peak-pf=1)
  14. %% Generate trajectories and Compute firing rates and generate spikes
  15. % rng(218);
  16. Simulation_f1;
  18. %% Ordered probability matrix (Pairwise temporal ordering)
  19. Make_pMat_f1;
  21. %% Place field rate map
  22. Make_PF_f1;
  24. %% Replay candidate generation
  25. Find_candidate_f1;
  27. %% Replay estimatimation using ordered probability matrix
  28. Estimate_replay_pMat_f1;
  30. %% Replay estimation using place field (Bayesian decoding)
  31. Estimate_replay_PF_f1;
  33. %% Figures
  34. % Raster plot of example spike trains
  35. subplot(3,4,1); hold on;
  36. sp_tr = y2_sp(1,:); % first trial
  37. trange = [0 length(y_pos{1})];
  38. no_sp_tr = length(sp_tr);
  39. for i_sp_tr = 1:no_sp_tr
  40. no_sp = length(sp_tr{i_sp_tr});
  41. for i_sp = 1:no_sp
  42. plot([sp_tr{i_sp_tr}(i_sp), sp_tr{i_sp_tr}(i_sp)], [i_sp_tr-0.4, i_sp_tr+0.4], 'k');
  43. end
  44. end
  45. hold off;
  46. ylim([0 no_sp_tr+1]); xlim(trange);
  47. xticks([0 8000]); yticks([0 100]); xticklabels({'0','8'});
  48. xlabel('Time (s)'); ylabel('Neuron #');
  49. text(-0.45, 1.15, 'A', 'Units','normalized', 'FontWeight','bold','FontSize',12);
  50. set(gca,'FontName','Helvetica','TickDir','out','box','off');
  52. % example replay candidate
  53. subplot(3,4,2);
  54. plot(x2_sync_e(1,:), x2_sync_e(2,:), 'ko','MarkerFaceColor','k','MarkerSize',3);
  55. axis([0 210 0 100]);
  56. xlabel('Time (s)'); ylabel('Neuron #');
  57. xticks([0 100 200]); xticklabels({'0','0.1','0.2'}); yticks([0 40 80]);
  58. text(-0.45, 1.15, 'B', 'Units','normalized', 'FontWeight','bold','FontSize',12);
  59. set(gca,'FontName','Helvetica','TickDir','out','box','off');
  61. % rate map
  62. subplot(3,4,5);
  63. imagesc(x2_rate_map);
  64. ax = gca; ax.YDir = 'normal';
  65. ylabel('Position (m)'); xlabel('Neuron #');
  66. xticks([50 100]); yticks([1 50 100]);
  67. yticklabels({'0','0.5','1'});
  68. text(-0.45, 1.15, 'C', 'Units','normalized', 'FontWeight','bold','FontSize',12);
  69. set(gca,'FontName','Helvetica','TickDir','out');
  71. % decoded rate map from relay candidate
  72. subplot(3,4,6);
  73. imagesc(x2_p_sp_bin0,[0,0.03]);
  74. ax = gca; ax.YDir = 'normal';
  75. xlabel('Time (s)'); ylabel('Position (m)');
  76. xticks([1,5,10]); xticklabels({'0','0.1','0.2'});
  77. yticks([1 50 100]); yticklabels({'0','0.5','1'});
  78. text(-0.45, 1.15, 'D', 'Units','normalized', 'FontWeight','bold','FontSize',12);
  79. set(gca,'FontName','Helvetica','TickDir','out');
  81. % Distribution of weighted corr.
  82. subplot(3,4,7); hold on;
  83. h1 = histogram(x_w_corr_sh,100);
  84. h1.Normalization = 'probability';
  85. h1.FaceColor = 'w'; h1.EdgeColor = 'k';
  86. plot([0,0],[0,0.0399],'k:');
  87. plot(x_w_corr*[1,1],[0,0.0399],'r-'); hold off;
  88. xlabel('W_{corr}'); ylabel('Fraction');
  89. xticks(-0.5:0.5:0.5);
  90. axis([-0.55,0.55,0,0.04]);
  91. text(-0.45, 1.15, 'E', 'Units','normalized', 'FontWeight','bold','FontSize',12);
  92. set(gca,'FontName','Helvetica','TickDir','out','box','off');
  94. % ordered probability matrix
  95. subplot(3,4,9);
  96. imagesc(x2_pMat_glm);
  97. ax = gca; ax.YDir = 'normal';
  98. xlabel('Neuron #'); ylabel('Neuron #');
  99. xticks([50 100]); yticks([50 100]);
  100. text(-0.45, 1.15, 'F', 'Units','normalized', 'FontWeight','bold','FontSize',12);
  101. set(gca,'FontName','Helvetica','TickDir','out');
  103. % order probability matrix of replay candidate
  104. subplot(3,4,10);
  105. x2_pMat_replay = zeros(size(x2_sync_e,2));
  106. for i_ne = 1:size(x2_sync_e,2)
  107. for j_ne = 1:size(x2_sync_e,2)
  108. x2_pMat_replay(i_ne,j_ne) = x2_p(x2_sync_e(2,i_ne),x2_sync_e(2,j_ne));
  109. end
  110. end
  111. imagesc(x2_pMat_replay); axis xy;
  112. xlabel('Neuron #'); ylabel('Neuron #');
  113. xticks([5,10]); yticks([5,10]);
  114. text(-0.45, 1.15, 'G', 'Units','normalized', 'FontWeight','bold','FontSize',12);
  115. set(gca,'FontName','Helvetica','TickDir','out');
  117. % Distribution of sequence likelihood
  118. subplot(3,4,11); hold on;
  119. h1 = histogram(x_p_replay_sh,100);
  120. h1.Normalization = 'probability';
  121. h1.FaceColor = 'w'; h1.EdgeColor = 'k';
  122. plot([0,0],[0,0.0399],'k:');
  123. plot(x_p_replay*[1,1],[0,0.0399],'r-'); hold off;
  124. xlabel('L_{seq}'); ylabel('Fraction');
  125. xticks(-50:50:50); axis([-55,55,0,0.04]);
  126. text(-0.45, 1.15, 'H', 'Units','normalized', 'FontWeight','bold','FontSize',12);
  127. set(gca,'FontName','Helvetica','TickDir','out','box','off');
  129. % relationship between weighted corr. and sequence likelihood
  130. load('result_w_Lseq.mat'); % contains: result_w_Lseq (100 simulated results)
  131. subplot(3,4,12); hold on;
  132. x2_scat = result_w_Lseq;
  133. plot(x2_scat(:,1), x2_scat(:,2), 'k.'); % Scatter plot
  134. [b,d,st] = glmfit(x2_scat(:,1), x2_scat(:,2)); % Linear fit (GLM)
  135. plot([-1,1],[b(1)-b(2), b(1)+b(2)], 'k-'); % Plot regression line
  136. plot([0 0],[-20 100],'k:'); plot([-0.3 1],[0 0],'k:'); hold off;
  137. axis([-0.29 0.89 -19 99]);
  138. xlabel('W_{corr}'); ylabel('L_{seq}');
  139. xticks(-0.4:0.4:1); yticks(0:60:100);
  140. text(-0.45, 1.15, 'I', 'Unit','normalized', 'FontWeight','bold','FontSize',12);
  141. set(gca,'FontName','Helvetica','TickDir','out','box','off');

figure1.m, under CC-BY-4.0 · at the source

Overview

Authors: Namjung Huh1, Injae Yun1,2, Jong Won Lee1, Min Whan Jung1,2
ORCID iDs: Injae Yun, Min Whan Jung
  1. Center for Synaptic Brain Dysfunctions, Institute for Basic Science,Daejeon, Korea
  2. Department of Biological Sciences, Korea Advanced Institute of Science and Technology,Daejeon, Korea
Institutions: Institute for Basic Science (South Korea); Korea Advanced Institute of Science and Technology (South Korea)
Journal: Nature communications, volume 17, issue 1, article 8304
Dates: received 17 September 2025; accepted 12 June 2026; published online 4 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-74822-2 · PMID 42401577 · PMCID PMC13469133 · OpenAlex W7167366658
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), rat (organism), systems (subfield)
Methods: Connectivity, Statistics, Preprocessing, Machine learning, fMRI & imaging, Single-unit activity, calcium imaging, Spectral & time-frequency, Physiology & signal measures, Smoothing, state filtering, decompositions
Keywords: Hippocampus, Cortex
MeSH: Action Potentials*, Hippocampus*, Animals, Likelihood Functions, Male, Mice, Models, Neurological, Neurons, Rats, Visual Cortex (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: This work was supported by the Research Center Program of the Institute for Basic Science (IBSR002-A1; M.W.J.)
Citations: not cited yet (Europe PMC); 46 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 3 matches between paragraphs and lines of code.

figshare 31942599

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 2 files
Software Heritage: not checked
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (8 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
14 files
At the source:

Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-74822-2.

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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;
  • 14 scripts, each with its path and the digest of its content;
  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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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/s41467-026-74822-2.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 2 keywords, 10 MeSH terms, 1 funder, 44 references.

Cite

This paper

Huh, N., Yun, I., Lee, J. W., & Jung, M. W. (2026). A likelihood-based method for identifying replay from spike sequences. Nature communications, 17(1), 8304. https://doi.org/10.1038/s41467-026-74822-2

BibTeX

@article{huh2026likelihood,
author = {Huh, Namjung and Yun, Injae and Lee, Jong Won and Jung, Min Whan},
title = {{A likelihood-based method for identifying replay from spike sequences}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {8304},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74822-2},
url = {https://doi.org/10.1038/s41467-026-74822-2},
pmid = {42401577},
pmcid = {PMC13469133}
}

RIS

TY - JOUR
AU - Huh, Namjung
AU - Yun, Injae
AU - Lee, Jong Won
AU - Jung, Min Whan
TI - A likelihood-based method for identifying replay from spike sequences
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/04
VL - 17
IS - 1
SP - 8304
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74822-2
UR - https://doi.org/10.1038/s41467-026-74822-2
LA - en
ER -

CSL-JSON

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"PMCID": "PMC13469133",
"ISSN": "2041-1723",
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