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Toroidal topology of grid-cell activity precedes spatial navigation during development

Code ↔ Paper

8 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 8 matches · 5 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Visualization of toroidal and ring manifolds ↔ SUA analysis/get_stPR.m, the whole file · a weak match · score 0.60 · population rate, Gaussian kernel, firing rate, convolved, neurons, variables
  2. [2] § Methods › Identification of putative inhibitory connections ↔ SUA analysis/computeXcorrHGonly.m, lines 31–163 · score 0.60 · hollowed gaussian kernel, probabilistic, convolving, subtracted, window, lag
  3. [3] § Methods › Spike sorting and single-unit selection ↔ klusta/loadKlusta.m, the whole file · a weak match · score 0.58 · refractory period violations, MUA, waveform, Spike
  4. [4] § Methods › Clustering ↔ SUA analysis/get_stPR.m, the whole file · a weak match · score 0.56 · Population coupling, Gaussian kernel, firing rate, correlation
  5. [5] § Results › Emergence of toroidal manifolds reflects maturation of cortical network dynamics ↔ SUA analysis/computeXcorrHGonly.m, lines 31–163 · score 0.54 · cross correlograms, spike train, probability, fast
  6. [6] § Methods › Desynchronization analysis ↔ SUA analysis/deprecated_fasterTilingCoeff.m, the whole file · a weak match · score 0.54 · Tiling Coefficient, spike trains, STTC, correlations
  7. [7] § Methods › Clustering ↔ Wrappers/MainPopulationCoupling.m, lines 7–26 · score 0.54 · Population coupling, Gaussian kernel
  8. [8] § Methods › Desynchronization analysis ↔ SUA analysis/deprecated_fastTC.m, the whole file · a weak match · score 0.52 · Tiling Coefficient, spike trains, STTC, correlations

Paper

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

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The authors' code

MATLAB · 94 lines · 4.4 KB · GPL-3.0 · 2 matches

  1. function [stPR, pop_coupling, pop_coupling_1sthalf, pop_coupling_2ndhalf] ...
  2. = get_stPR(spike_matrix, max_lag, animal_name, repeat_calc, save_data, output_folder)
  4. %% by Mattia 09.19
  5. % compute stPR (spike-triggered population rate) and population coupling
  6. % as described in Okun et al., 2015 Nature.
  8. % inputs:
  9. % - animal_name (string): to save/loda stuff
  10. % - spike_matrix (2D matrix): as computed by getSpikeMatrixKlusta
  11. % - max_lag (value): in ms (tipical values are 100-500-1000ms)
  12. % - repeat_calc (0 or 1): 0=no, 1=yes
  13. % - save_data (0 or 1): 0=no, 1=yes
  14. % - output_folder (string): main folder to save results
  16. % outputs
  17. % - stPR (array, num_units*(max_lag*2+1)): spike triggered population rate
  18. % - pop_coupling (array, num_units): population coupling
  19. % - pop_coupling_1sthalf array, num_units): population coupling first half (quality control,
  20. % shouldn't differ from second half)
  21. % - pop_coupling_2ndhalf (array, num_units): population coupling second half
  24. if repeat_calc == 0 && ...
  25. exist(strcat(output_folder, animal_name, '.mat'), 'file')
  26. load(strcat(output_folder, animal_name))
  27. stPR = pop_coupling_stuff.stPR;
  28. pop_coupling = pop_coupling_stuff.pop_coupling;
  29. pop_coupling_1sthalf = pop_coupling_stuff.pop_coupling_1sthalf;
  30. pop_coupling_2ndhalf = pop_coupling_stuff.pop_coupling_2ndhalf;
  31. else
  32. Gwindow = gausswin(101, 8.3); % gaussian window of 100ms with stdev of 12ms as in paper
  33. Gwindow = Gwindow / sum(Gwindow); % normalize the gaussian kernel
  34. num_units = size(spike_matrix, 1);
  35. half_rec = round(length(spike_matrix) / 2);
  37. % initialize variables
  38. stPR = zeros(num_units, 2 * max_lag + 1);
  39. pop_coupling = zeros(num_units, 1);
  40. pop_coupling_1sthalf = pop_coupling;
  41. pop_coupling_2ndhalf = pop_coupling;
  43. if ~ isnan(spike_matrix)
  44. % loop over units
  45. for unit_idx = 1 : num_units
  46. % compute the population rate, excluding the unit for which you
  47. % are calculating the stPR
  48. if num_units > 2
  49. population_rate = sum(spike_matrix(~ ismember(1 : num_units, unit_idx), :));
  50. else
  51. population_rate = spike_matrix(~ ismember(1 : num_units, unit_idx), :);
  52. end
  53. % convolve with gaussian window
  54. population_rate = conv(full(population_rate), Gwindow, 'same');
  55. % subtract mean
  56. population_rate = population_rate - mean(population_rate);
  57. % convolve also firing of the single neuron
  58. firing_rate_neuron = conv(full(spike_matrix(unit_idx, :)), Gwindow, 'same');
  59. % compute xcorr between the two vectors (stPR)
  60. stPR(unit_idx, :) = xcorr(firing_rate_neuron, population_rate, max_lag);
  61. % compute population coupling as in the paper (stPR at 0 normalized by
  62. % number of spikes fired by the neuron).
  63. pop_coupling(unit_idx) = stPR(unit_idx, max_lag + 1) / sum(firing_rate_neuron);
  64. % to compute reliability of pop_coupling, compute it for first and
  65. % second half separately, to than check how they correlate to each
  66. % other
  67. stPR_1sthalf = XcorrNB(cat(1, firing_rate_neuron(1 : half_rec), ...
  68. population_rate(1 : half_rec)), 0, 0);
  69. pop_coupling_1sthalf(unit_idx) = stPR_1sthalf / sum(firing_rate_neuron(1 : half_rec));
  70. stPR_2ndhalf = XcorrNB(cat(1, firing_rate_neuron(half_rec + 1 : end), ...
  71. population_rate(half_rec + 1 : end)), 0, 0);
  72. pop_coupling_2ndhalf(unit_idx) = stPR_2ndhalf / sum(firing_rate_neuron(half_rec + 1 : end));
  74. % put stuff into a structure
  75. pop_coupling_stuff = struct;
  76. pop_coupling_stuff.stPR = stPR;
  77. pop_coupling_stuff.pop_coupling = pop_coupling;
  78. pop_coupling_stuff.pop_coupling_1sthalf = pop_coupling_1sthalf;
  79. pop_coupling_stuff.pop_coupling_2ndhalf = pop_coupling_2ndhalf;
  80. end
  81. else
  82. pop_coupling_stuff = struct;
  83. pop_coupling_stuff.stPR = NaN;
  84. pop_coupling_stuff.pop_coupling = NaN;
  85. pop_coupling_stuff.pop_coupling_1sthalf = NaN;
  86. pop_coupling_stuff.pop_coupling_2ndhalf = NaN;
  87. end
  88. if save_data == 1
  89. save(strcat(output_folder, animal_name), 'pop_coupling_stuff')
  90. else
  91. disp('Data not saved!')
  92. end
  93. end
  94. end

get_stPR.m at commit f7f3432, under GPL-3.0 · at the source

Overview

Authors: Matteo Guardamagna1, Erik Hermansen2, Jordan Carpenter1, Christine M. Lykken1, Benjamin A. Dunn2, Edvard I. Moser1, May-Britt Moser1
ORCID iDs: Matteo Guardamagna, Erik Hermansen, Jordan Carpenter, Christine M. Lykken, Benjamin A. Dunn, Edvard I. Moser, May-Britt Moser
  1. Kavli Institute for Systems Neuroscience and Centre for Algorithms in the Cortex, Fred Kavli Building, Norwegian University of Science and Technology, NO-7491, Trondheim, Norway
  2. Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway
Institutions: Kavli Institute for Systems Neuroscience (Norway); Norwegian University of Science and Technology (Norway)
Journal: bioRxiv (preprint)
Dates: published online 11 March 2026
Type: Preprint
License: CC BY-NC-ND
Identifiers: DOI 10.64898/2026.03.10.710908 · OpenAlex W7134946257
Open access: green, a free copy (OpenAlex)
Status: code verified
Categories: developmental (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging, Single-unit activity, calcium imaging
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: European Research Council (951319)
Citations: not cited yet (Europe PMC); 72 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 8 matches between paragraphs and lines of code.

OpatzLab/HanganuOpatzToolbox

License: GPL-3.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: f7f3432336de958a02094306469fb66a7a40c5cd, 4 October 2024
Languages: MATLAB (534), Python (37), C/C++ (11), C (9), C++ (1), Java (1), Shell (1)
Size: 1,120 files, 594 scripts
Software Heritage: not archived
Found in: the text, “Desynchronization analysis”
Holds: README, license file, environment (Fooof/setup.cfg, Fooof/setup.py), tests, documentation
Not found: CITATION.cff, continuous integration
Tools: Chronux (70 files), specparam (formerly FOOOF) (25 files), Signal Processing Toolbox (22 files), Statistics and Machine Learning Toolbox (20 files), NumPy (19 files), FieldTrip (9 files), SciPy (4 files), Image Processing Toolbox (3 files), boundedline (1 file), CircStat (1 file), Matplotlib (1 file), Violinplot-Matlab (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
596 files
At the source: github.com/OpatzLab/HanganuOpatzToolbox

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Read it in the paper: doi.org/10.64898/2026.03.10.710908.

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Read it in the paper: doi.org/10.64898/2026.03.10.710908.

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Version 1, 30 September 2026: the first record

Recorded: type, journal, dates, 7 authors, 1 funder, 62 references.

Cite

This paper

Guardamagna, M., Hermansen, E., Carpenter, J., Lykken, C. M., Dunn, B. A., Moser, E. I., & Moser, M.-B. (2026). Toroidal topology of grid-cell activity precedes spatial navigation during development. bioRxiv (preprint). https://doi.org/10.64898/2026.03.10.710908

BibTeX

@article{guardamagna2026toroidal,
author = {Guardamagna, Matteo and Hermansen, Erik and Carpenter, Jordan and Lykken, Christine M. and Dunn, Benjamin A. and Moser, Edvard I. and Moser, May-Britt},
title = {{Toroidal topology of grid-cell activity precedes spatial navigation during development}},
journal = {bioRxiv (preprint)},
year = {2026},
month = mar,
publisher = {bioRxiv},
issn = {2692-8205},
doi = {10.64898/2026.03.10.710908},
url = {https://doi.org/10.64898/2026.03.10.710908}
}

RIS

TY - JOUR
AU - Guardamagna, Matteo
AU - Hermansen, Erik
AU - Carpenter, Jordan
AU - Lykken, Christine M.
AU - Dunn, Benjamin A.
AU - Moser, Edvard I.
AU - Moser, May-Britt
TI - Toroidal topology of grid-cell activity precedes spatial navigation during development
T2 - bioRxiv (preprint)
J2 - bioRxiv
PY - 2026
DA - 2026/03/11
SN - 2692-8205
PB - bioRxiv
DO - 10.64898/2026.03.10.710908
UR - https://doi.org/10.64898/2026.03.10.710908
ER -

CSL-JSON

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