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A widespread animal communication tempo may resonate with the receiver's brain.

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 · all tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Field data ↔ PlotFig1AB.m, the whole file · a weak match · score 0.72 · Firefly flash, Golay, camera, smoothed, video, filter
  2. [2] § Methods › Data from previously published work and established databases ↔ find_freqs.m, the whole file · a weak match · score 0.72 · land mammals, standard deviation, came, intervals, canto, xeno
  3. [3] § Methods › Field data ↔ PlotFigS2.m, the whole file · a weak match · score 0.70 · Firefly flash, Golay, camera, smoothed, video, filter
  4. [4] § Results › Data ↔ PlotFig1C.m, the whole file · a weak match · score 0.64 · sea lions, apes, fish, amphibians, insects, body
  5. [5] § Methods › Field data ↔ PlotFig1AB.m, the whole file · a weak match · score 0.58 · highpass filter, envelope, window, audio, peak, spectrogram
  6. [6] § Results › Computational experiments ↔ PlotFig4C.m, the whole file · a weak match · score 0.58 · Kuramoto oscillators, coupling strength, parameter space, seeds, external, forcing
  7. [7] § Methods › Field data ↔ PlotFigS2.m, the whole file · a weak match · score 0.56 · highpass filter, envelope, window, audio, peak, spectrogram
  8. [8] § Results › Data ↔ plot_freqs.m, the whole file · a weak match · score 0.54 · log normal, delta band, fit, median, rejected, peak

Paper

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

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The authors' code

MATLAB · 89 lines · 3.3 KB · CC-BY-4.0 · 2 matches

  1. %%% script for plotting spectrograms of the cricket chirping (audio signal)
  2. %%% and the firefly flashing (video) from our field recordings
  5. % File to analyze:
  6. InputVideo = 'cricket_and_firefly_video.mp4';
  7. firefly_data_file = 'firefly_flash_matrix.csv';
  10. % Load info about frames per second (FPS) etc from video
  11. v = VideoReader(InputVideo);
  12. Nframes = v.NumFrames;
  13. Fs_vid = v.FrameRate; %should be 30 fps
  14. %TimeDur_vid = v.Duration; % turns out to be unnecessary, use audio info
  15. clear v; % no need to keep the video in memory
  17. % Load firefly flash time matrix (extracted from video as part of another
  18. % project). The format is a 21 x 1811 matrix, where each row represents a
  19. % single firefly and each column represents a frame of the video. If the
  20. % firefly's lantern is on(i.e., it's visible) in that frame, then the
  21. % matrix contains a 1, otherwise it's zero.
  22. firefly_flash_matrix = readmatrix(firefly_data_file);
  25. % load audio data (which comes from same input video file):
  26. [audio,Fs_aud] = audioread(InputVideo); % read the audio file (first needs to be converted)
  27. audio = audio(:, 1); % only need mono data, not stereo, so dispose of second audio channel
  30. % Construct time vector for audio
  31. dt = 1/Fs_aud;
  32. t = (0:dt:(length(audio)-1)*dt)';
  33. TimeDur_aud = (length(audio)-1)*dt;
  36. % highpass filter: pass frequencies over 5000 Hz (so mostly cricket sounds)
  37. filtered = highpass(audio, 5000, Fs_aud);
  40. % get the envelope of the filtered audio signal. require that peaks must
  41. % be at least 3000 samples apart (which is around 0.07 seconds)
  42. [FiltUpEnv, FiltBtmEnv] = envelope(abs(filtered), 3000, 'peak');
  44. % Because of audio sync, rounding, or encoding issues from the original camera,
  45. % audio and video durations may not match perfectly. This is not important
  46. % for our purposes, but to avoid having to extrapolate when downsampling
  47. % make sure the video time duration is set at (or slightly lower than) the
  48. % audio time duration.
  49. TimeDur_vid = TimeDur_aud - 1e-6;
  51. % downsample the audio envelope to match the (much much lower) video frame rate
  52. vidtimes = linspace(0, TimeDur_vid, Nframes);
  53. DownSampled = interp1(t, FiltUpEnv, vidtimes, 'linear');
  56. % Convert firefly flash matrix to a time signal of # illuminated vs. time:
  57. TotalFlash = sum(firefly_flash_matrix);
  58. % Smooth the signal (Savitsky Golay FIR filter of quadratic order, 9 point moving window)
  59. TotalFlashSmooth = sgolayfilt(TotalFlash,2,9);
  61. % Last step before plotting spectrograms: remove means from each signal
  62. DownSampled = DownSampled - mean(DownSampled);
  63. TotalFlashSmooth = TotalFlashSmooth - mean(TotalFlashSmooth);
  65. % Now plot spectrograms: first audio data (crickets) then video data (fireflies)
  66. figure;
  67. tiledlayout(2,1);
  68. nexttile;
  69. % Set Hamming window to 100 samples = 100/Fs = 3.3 seconds, but 80 overlapped
  70. % samples means each bin is (100-80)/Fs = 0.67 seconds in time. In
  71. % frequency, plot 100 points in the DFT (which extends up to Fs_vid/2 = 15 Hz).
  72. spectrogram(DownSampled,100,80,100,Fs_vid,'yaxis');
  73. % Adjust axes...
  74. ylim([0 4]);
  75. xlabel('');
  76. title('Crickets');
  77. clim([-80 -40]);
  78. set(gca, 'FontSize', 20);
  80. nexttile;
  81. % Same approach as other spectrogram
  82. spectrogram(TotalFlashSmooth,100,80,100,Fs_vid,'yaxis');
  83. ylim([0 4]);
  84. xlabel('t (s)');
  85. title('Fireflies');
  86. clim([-50 10]);
  87. set(gca, 'FontSize', 20);

PlotFig1AB.m, under CC-BY-4.0 · at the source

Overview

Authors: Guy Amichay1,2,3, Vijay Balasubramanian4,5,6, Daniel M Abrams1,2,3,7
ORCID iDs: Guy Amichay
  1. Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, Illinois, United States of America
  2. Northwestern Institute on Complex Systems, Northwestern University, Evanston, Illinois, United States of America
  3. National Institute for Theory and Mathematics in Biology, Northwestern University, Evanston, Illinois, United States of America
  4. David Rittenhouse Laboratory, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
  5. Santa Fe Institute, Santa Fe, New Mexico, United States of America
  6. Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford, United Kingdom
  7. Department of Physics and Astronomy, Northwestern University, Evanston, Illinois, United States of America
Institutions: Northwestern University (United States); Santa Fe Institute (United States); University of Oxford (United Kingdom); University of Pennsylvania (United States)
Journal: PLoS biology, volume 24, issue 4, article e3003735
Dates: received 30 June 2025; accepted 14 March 2026; published online 14 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pbio.3003735 · PMID 41980041 · PMCID PMC13078620 · OpenAlex W7154396347
Open access: gold, a free copy (OpenAlex)
Status: code verified
Methods: Spectral & time-frequency, Smoothing, state filtering, decompositions
MeSH: Animal Communication*, Brain*, Gryllidae*, Animals, Neurons, Thailand (* major topic)
Topic: Animal Vocal Communication and Behavior (Developmental Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Simons Foundation (MPS-NITMB-00005320); Buffett Institute for Global Affairs, Northwestern University; NSF (DMS-2235451); Northwestern Institute on Complex Systems; Eastman Professorship Balliol College, University of Oxford; NSF-Simons National institute for theory and mathematics in biology
Citations: cited by 1 paper (Europe PMC); 60 references in the paper
Notices: A comment on this paper has been published (42268844, from Europe PMC)

Abstract

During fieldwork in Thailand, we observed nearly identical tempos of co-located flashing fireflies and chirping crickets. Motivated by this, we survey published data showing that an abundance of evolutionarily distinct species communicate isochronously at ~0.5–4 Hz, suggesting that this might be a tempo “hotspot.” We hypothesize that this timescale may have a universal basis in the biophysics of the receiver’s neurons. We test this by demonstrating that small receiver circuits constructed from elements representing typical neurons will be most responsive in the observed tempo range.

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 8 matches between paragraphs and lines of code.

Zenodo 19069908

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: MATLAB (10)
Size: 19 files, 10 scripts
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (2 files), Statistics and Machine Learning Toolbox (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
13 files
At the source: doi.org/10.5281/zenodo.19069908

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;
  • 13 scripts, each with its path and the digest of its content;
  • 8 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

Data and code to accompany this paper can be found at https://doi.org/10.5281/zenodo.19069908.

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, 3 authors, 6 MeSH terms, 6 funders, 50 references, 1 integrity notice.

Cite

This paper

Amichay, G., Balasubramanian, V., & Abrams, D. M. (2026). A widespread animal communication tempo may resonate with the receiver's brain. PLoS biology, 24(4), e3003735. https://doi.org/10.1371/journal.pbio.3003735

BibTeX

@article{amichay2026widespread,
author = {Amichay, Guy and Balasubramanian, Vijay and Abrams, Daniel M},
title = {{A widespread animal communication tempo may resonate with the receiver's brain}},
journal = {PLoS biology},
year = {2026},
month = apr,
volume = {24},
number = {4},
pages = {e3003735},
publisher = {PLOS},
issn = {1544-9173},
doi = {10.1371/journal.pbio.3003735},
url = {https://doi.org/10.1371/journal.pbio.3003735},
pmid = {41980041},
pmcid = {PMC13078620}
}

RIS

TY - JOUR
AU - Amichay, Guy
AU - Balasubramanian, Vijay
AU - Abrams, Daniel M
TI - A widespread animal communication tempo may resonate with the receiver's brain
T2 - PLoS biology
J2 - PLoS Biol
PY - 2026
DA - 2026/04/14
VL - 24
IS - 4
SP - e3003735
SN - 1544-9173
PB - PLOS
DO - 10.1371/journal.pbio.3003735
UR - https://doi.org/10.1371/journal.pbio.3003735
LA - en
ER -

CSL-JSON

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"container-title-short": "PLoS Biol",
"volume": "24",
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"page": "e3003735",
"DOI": "10.1371/journal.pbio.3003735",
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