OSCR

Cortical representation of pitch perception in mice.

Code ↔ Paper

5 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 5 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and Methods › Auditory filter model ↔ auditoryFilterModel.m, lines 147–259 · score 0.90 · iterated ripple noises, 4–48 kHz, Fundamental frequencies, harmonic complex tones, pulse trains, auditory filter
  2. [2] § Materials and Methods › Auditory filter model ↔ generateStimulus.m, the whole file · a weak match · score 0.89 · amplitude modulated noises, iterated ripple noises, 4–48 kHz, harmonic complex tones, pulse trains, delay
  3. [3] § Materials and Methods › Auditory filter model ↔ auditoryFilterModel.m, lines 63–145 · score 0.84 · half wave rectified, zero phase gammatone, low pass filtered, phase locking, gammatone filter, CB
  4. [4] § Results › Modeled temporal pitch salience predicts F0 discrimination sensitivity in mice ↔ auditoryFilterModel.m, lines 1–19 · score 0.77 · half wave rectified, zero phase gammatone, low pass filtered, phase locking, modeled, compressive
  5. [5] § Materials and Methods › Acoustic stimulus presentation ↔ generateStimulus.m, the whole file · a weak match · score 0.68 · bandpass filtered, cosine, delay, depth, pi, summing

Paper

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

The paper is loaded when this pane is shown.

The authors' code

MATLAB · 259 lines · 7 KB · no license · 3 matches

  1. %% Mouse Auditory Filter Model from Putnam and Kumar et al 2026
  2. % This is a gammatone auditory filterbank model. It processes four different stimulus classes (Pulse
  3. % Trains, Harmonic Complex Tones, Amplitude Modulated Noise, and Iterated Ripple Noise).
  4. %
  5. % Processing pipeline:
  6. % 1) Stimulus synthesis
  7. % 2) Zero-phase gammatone filtering
  8. % 3) Half-wave rectification and compression
  9. % 4) Low-pass filtering to phase-locking limit (1 kHz)
  10. % 5) Temporal pitch strength via autocorrelation function (ACF) peak around the F0 period
  11. % 6) Visualization
  12. %
  13. % Author: Nikolas Francis, 2026
  15. clear; clc; close all
  17. % Add model path
  18. codepath = fileparts(mfilename('fullpath'));
  19. addpath(genpath(codepath));
  21. %% Parameters
  23. % Sampling rate (Hz)
  24. fs = 200e3;
  26. % Stimulus duration (s)
  27. duration = 0.5;
  29. % Time vector
  30. t = 0:1/fs:(duration - 1/fs);
  32. % Duration of onset/offset ramp (s)
  33. rampDur = 0.005;
  35. % Hanning window for ramp
  36. ramp = hanning(round(rampDur*fs*2));
  37. ramp = ramp(1:floor(end/2))';
  38. rampOff = fliplr(ramp);
  40. % Filter spacing (Hz)
  41. filtspace = 500;
  43. % Phase-locking cutoff frequency (Hz)
  44. phaseLockHz = 1000;
  46. % Compression factor (0-1, 1 = no compression)
  47. compfact = 0.5;
  49. % Mouse cochlear bandwidth data (Ehret, 1976)
  50. f = [5 10 15 20 30 40 50]*1e3;
  51. CB_ehret = [3.89 4.44 6.39 7.94 15.67 17.72 21.94]*1e3;
  53. % Fundamental frequencies (Hz)
  54. F0s = [55, 330];
  56. % Compute filterbank center frequencies
  57. f_interp = 4000:filtspace:48000;
  59. % Linear fit to Ehret CBs
  60. P = polyfit(f, CB_ehret, 1);
  61. CB = P(1)*f_interp + P(2);
  63. %% Loop through each stimulus configuration
  64. % Preallocate storage
  65. Stim = cell(2,4);
  66. auditoryFilterOutput = cell(2,4);
  67. auditoryFilterSpectrum = cell(2,4);
  68. pitchStrength = zeros(2,4);
  69. for s = 1:4
  70. for F0idx = 1:2
  72. % Display status
  73. stimNames = {'Pulse Train','Harmonic Complex Tone','AM Noise','IRN'};
  74. fprintf('Processing stimulus type %d (%s), F0 = %d Hz\n', s, stimNames{s}, F0s(F0idx));
  76. % Select F0
  77. f0 = F0s(F0idx);
  79. % Generate stimulus
  80. [stim, ~] = generateStimulus(f0, s, fs, t);
  82. % Apply onset/offset ramps
  83. stim(1:length(ramp)) = stim(1:length(ramp)) .* ramp;
  84. stim(end-length(ramp)+1:end) = stim(end-length(ramp)+1:end) .* rampOff;
  86. % Normalize stimulus
  87. stim = stim ./ max(abs(stim));
  88. Stim{F0idx,s} = stim;
  90. % Auditory filter processing (zero-phase gammatone)
  91. filtStim = zeros(length(stim), length(f_interp));
  92. parfor i = 1:length(f_interp)
  93. cf = f_interp(i);
  94. erb = CB(i);
  95. filtered = zerophaseGammatoneFilter(stim(:), fs, cf, erb);
  96. filtered = filtered - mean(filtered);
  97. filtStim(:,i) = filtered;
  98. end
  100. % Half-wave rectification and compression
  101. filtStim(filtStim < 0) = 0;
  102. D = (filtStim'.^compfact) / max(abs(filtStim(:)));
  103. clear filtStim
  105. % Low-pass filtering (phase locking limit)
  106. [b2,a2] = butter(6, phaseLockHz/(fs/2), 'low');
  107. X = double(D.');
  108. auditoryFilterOutput{F0idx, s} = filtfilt(b2, a2, X)';
  109. clear D
  111. % Auditory spectrum (max over time, normalized)
  112. d = max(auditoryFilterOutput{F0idx,s}, [], 2);
  113. auditoryFilterSpectrum{F0idx,s} = d ./ max(abs(d));
  115. % Temporal pitch strength (ACF peak around the period)
  117. % FFT-based autocorrelation for speed
  118. X = auditoryFilterOutput{F0idx,s};
  119. [C,T] = size(X);
  120. w = hamming(T).';
  121. X = X - mean(X,2);
  122. X = X .* w;
  123. X = single(X);
  124. p = round((1/f0)*fs);
  125. kmin = max(0, floor(0.8*p));
  126. kmax = min(T-1, ceil(1.2*p));
  127. Nfft = 2^nextpow2(2*T-1);
  128. F = fft(X, Nfft, 2);
  129. Sxx = real(F .* conj(F));
  130. acf = ifft(Sxx, [], 2, 'symmetric');
  132. % Keep nonnegative lags only
  133. acf = acf(:,1:T);
  135. % Normalize by zero-lag
  136. zlag = max(acf(:,1), eps('single'));
  137. acf = bsxfun(@rdivide, acf, zlag);
  139. % Max within ±20% window around fundamental period (1/F0)
  140. corrP = max(acf(:,kmin+1:kmax+1), [], 2);
  142. pitchStrength(F0idx,s) = mean(corrP,'omitnan');
  144. end
  145. end
  147. %% Plot stimuli auditory filter output using Ehret CBs
  148. figure
  150. % Subplot indexes for Stim 1 Target
  151. pos{1,1} = [1 2 3];
  152. pos{1,3} = [17:19 33:35];
  153. pos{1,4} = [49:51];
  154. pos{1,5} = [20 36];
  156. % Subplot indexes for Stim 1 Non-target
  157. pos{2,1} = [1 2 3] + 64;
  158. pos{2,3} = [17:19 33:35] + 64;
  159. pos{2,4} = [49:51] + 64;
  160. pos{2,5} = [20 36] + 64;
  162. % Display duration (s)
  163. dispDur = 0.1;
  165. for s = 1:4
  166. for F0 = 1:2
  168. % Fundamental frequency selection and color
  169. if F0 == 1
  170. f0 = F0s(1);
  171. col = 'r';
  172. else
  173. f0 = F0s(2);
  174. col = 'b';
  175. end
  177. % Frequency axis for plots (kHz)
  178. f_interp = (4000:filtspace:48000) ./1000;
  180. % Stimulus waveform
  181. subplot(9,16, pos{F0,1} + ((s-1)*4))
  182. w = Stim{F0,s}(1,1:ceil(fs*dispDur));
  183. w = w ./ max(abs(w));
  184. t = 0:1/fs:duration-(1/fs);
  185. plot(t(1:ceil(fs*dispDur)), w, col, 'linewidth',1)
  186. ylim([-1.25 1.25])
  187. xlim([0 dispDur])
  188. set(gca,'xticklabel',[],'ytick',[-1 1])
  189. box off
  190. if F0==1 && s==1
  191. ylabel('Amplitude')
  192. end
  194. % Stimulus title selection
  195. if F0==1
  196. if s == 1
  197. T = 'Pulse Train';
  198. elseif s == 2
  199. T = 'Harmonic Complex Tone';
  200. elseif s == 4
  201. T = 'Iterated Ripple Noise';
  202. elseif s == 3
  203. T = 'AM Noise';
  204. end
  205. title(T)
  206. end
  207. xlabel('Time (s)')
  209. % Auditory Filter Spectrogram
  210. D = abs(auditoryFilterOutput{F0,s});
  211. h = subplot(9,16, pos{F0,3} + ((s-1)*4));
  212. t = 0:1/fs:(size(D,2)./fs) - (1/fs);
  213. imagesc(t, (f_interp'./1000), D);
  214. colormap(flipud(gray))
  215. mD = max(D(:));
  216. D=sum(D)./max(sum(D));
  217. freezeColors(h)
  218. set(gca,'YDir','normal','ytick',[4 48],'xtick',[])
  219. caxis([mD*.01 mD])
  220. xlim([0 dispDur])
  221. ylabel('Frequency (kHz)')
  222. box off
  224. % Auditory filter spectrum
  225. cf = f_interp;
  226. subplot(9,16, pos{F0,5} + ((s-1)*4))
  227. plot(auditoryFilterSpectrum{F0,s}, cf, col, 'linewidth',1)
  228. ylim([4 48])
  229. xlim([0 1])
  230. xlabel('Magnitude')
  231. set(gca,'yticklabel',[],'xtick',[0 1])
  232. box off
  234. % Auditory filter waveform (sum over channels)
  235. subplot(9,16, pos{F0,4} + ((s-1)*4))
  236. plot(t, D, col, 'linewidth',1)
  237. xlim([0 dispDur])
  238. box off
  239. xlabel('Time (s)')
  240. set(gca,'ytick',[0 1])
  241. ylabel('Amplitude')
  242. set(gcf,'position',[1238 615 551 420])
  244. end
  245. end
  247. % Plot pitch strength
  248. figure
  249. plot(1:4, mean(pitchStrength(:,:,1)), 'ko', 'MarkerFaceColor','k')
  250. hold on
  251. plot(1:4, mean(pitchStrength(:,:,1)), 'k')
  252. set(gca,'ytick',[0.5 .75 1])
  253. ylim([0.5 1])
  254. set(gca,'xtick',1:4,'xticklabel',{'CT','HCT','AMN','IRN'})
  255. xlabel('Stimulus')
  256. ylabel('Autocorrelation Peak')
  257. title('Pitch Strength')
  258. xlim([0.5 4.5])
  259. box off

auditoryFilterModel.m at commit f9019f1, no license · at the source

Overview

Authors: Jason W Putnam1,2,3, Abhay Kumar1, Nasiru K Gill1, Jonathan Dinh1, Franshesca Orellana Castellanos1, Sofia Leusch1, Sarah Vaughn1, Nikolas A Francis1,2,3,4
ORCID iDs: Nikolas A Francis
  1. Department of Biology, University of Maryland, College Park, MD USA
  2. Neuroscience and Cognitive Science Graduate Program, University of Maryland, College Park, MD USA
  3. Center for Comparative and Evolutionary Biology of Hearing, University of Maryland, College Park, MD USA
  4. Brain and Behavior Institute, University of Maryland, College Park, MD USA
Institutions: University of Maryland, College Park (United States)
Journal: Communications biology, volume 9, issue 1, article 1008
Dates: received 7 October 2025; accepted 30 April 2026; published online 13 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-10246-4 · PMID 42129426 · PMCID PMC13396396 · OpenAlex W4413975317
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.09.04.674262
Status: code verified
Categories: mouse (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Preprocessing, Single-unit activity, calcium imaging, Smoothing, state filtering, decompositions
Keywords: Cortex, Sensory processing
MeSH: Auditory Cortex*, Pitch Perception*, Acoustic Stimulation, Animals, Male, Mice, Mice, Inbred C57BL, Pitch Discrimination (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: University of Maryland Brain and Behavior Institute Seed Grant; U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (T32DC000046, R21DC017829); U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD) (T32DC000046, R21DC017829); NIDCD NIH HHS (R21 DC017829, T32 DC000046)
Citations: not cited yet (Europe PMC); 87 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 5 matches between paragraphs and lines of code.

Zenodo 19616783

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
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 (3 files)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
5 files
At the source:

soundcortex/francislabafm

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: f9019f1a1aff89f07a10aa43c02d0bf4097b5f00, 16 April 2026
Languages: MATLAB (4)
Size: 6 files, 4 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (3 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
5 files
At the source: github.com/soundCortex/francisLabAFM/tree/v1.0.0

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 8 scripts, each with its path and the digest of its content;
  • 5 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.

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it points to the authors' code: Zenodo 19616783

Read it in the paper: doi.org/10.1038/s42003-026-10246-4.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 2 keywords, 8 MeSH terms, 4 funders, 79 references.

Cite

This paper

Putnam, J. W., Kumar, A., Gill, N. K., Dinh, J., Orellana Castellanos, F., Leusch, S., Vaughn, S., & Francis, N. A. (2026). Cortical representation of pitch perception in mice. Communications biology, 9(1), 1008. https://doi.org/10.1038/s42003-026-10246-4

BibTeX

@article{putnam2026cortical,
author = {Putnam, Jason W and Kumar, Abhay and Gill, Nasiru K and Dinh, Jonathan and Orellana Castellanos, Franshesca and Leusch, Sofia and Vaughn, Sarah and Francis, Nikolas A},
title = {{Cortical representation of pitch perception in mice}},
journal = {Communications biology},
year = {2026},
month = may,
volume = {9},
number = {1},
pages = {1008},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10246-4},
url = {https://doi.org/10.1038/s42003-026-10246-4},
pmid = {42129426},
pmcid = {PMC13396396}
}

RIS

TY - JOUR
AU - Putnam, Jason W
AU - Kumar, Abhay
AU - Gill, Nasiru K
AU - Dinh, Jonathan
AU - Orellana Castellanos, Franshesca
AU - Leusch, Sofia
AU - Vaughn, Sarah
AU - Francis, Nikolas A
TI - Cortical representation of pitch perception in mice
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/05/13
VL - 9
IS - 1
SP - 1008
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10246-4
UR - https://doi.org/10.1038/s42003-026-10246-4
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s42003-026-10246-4",
"type": "article-journal",
"title": "Cortical representation of pitch perception in mice",
"container-title": "Communications biology",
"author": [
{
"family": "Putnam",
"given": "Jason W"
},
{
"family": "Kumar",
"given": "Abhay"
},
{
"family": "Gill",
"given": "Nasiru K"
},
{
"family": "Dinh",
"given": "Jonathan"
},
{
"family": "Orellana Castellanos",
"given": "Franshesca"
},
{
"family": "Leusch",
"given": "Sofia"
},
{
"family": "Vaughn",
"given": "Sarah"
},
{
"family": "Francis",
"given": "Nikolas A"
}
],
"container-title-short": "Commun Biol",
"volume": "9",
"issue": "1",
"page": "1008",
"DOI": "10.1038/s42003-026-10246-4",
"PMID": "42129426",
"PMCID": "PMC13396396",
"ISSN": "2399-3642",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s42003-026-10246-4",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
13
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1126/sciadv.aef3715
A cortical output channel for perceptual categorization.
Journal: Science advances
In common: cognitive, mouse, 6 references
[2] doi:10.1126/sciadv.aeb3005 [code]
Neural competition between prefrontal and auditory cortex constrains novel sound strategy learning.
Journal: Science advances
In common: mouse, 2 references
[3] doi:10.1371/journal.pbio.3003935 [code]
Lateralised neural mechanisms facilitate auditory self-other distinction and coordination in human dyads.
Journal: PLoS biology
In common: Signal Processing Toolbox, cognitive, 1 reference
[4] doi:10.1038/s41598-026-51854-8 [code]
Sound-evoked auditory neurophysiological signals are a window into prodromal functional differences in a preclinical model of Alzheimer's disease.
Journal: Scientific reports
In common: 2 references
[5] doi:10.1371/journal.pbio.3003693 [code]
Thalamic reticular neurons provide cell type-specific modulation of sound processing in the auditory thalamus.
Journal: PLoS biology
In common: Signal Processing Toolbox, mouse, 1 reference
[6] doi:10.1038/s41467-026-71742-z [code]
Hypercapnia dissociates neuronal and hemodynamic responses impairing neurovascular coupling and functional brain connectivity.
Journal: Nature communications
In common: Signal Processing Toolbox, mouse, 1 reference
[7] doi:10.1016/j.isci.2026.115373 [code]
Conserved post-odor dynamics in the olfactory systems of mice and locusts.
Journal: iScience
In common: Signal Processing Toolbox, mouse, 1 reference
[8] doi:10.1523/jneurosci.1104-25.2026 [code]
Timbre Encoding in the Inferior Colliculus.
Journal: The Journal of neuroscience : the official journal of the Society for Neuroscience
In common: Signal Processing Toolbox, 1 reference
[9] doi:10.1002/npr2.70113
Transcranial Calcium Macro-Imaging From the Auditory Cortex of Thy1-Cre-Driven GCaMP8 Transgenic Rats.
Journal: Neuropsychopharmacology reports
In common: 2 references
[10] doi:10.1038/s44271-026-00485-w [code]
Auditory perceptual maps in humans and mice share common structures and predict perceptual decisions in discrimination learning.
Journal: Communications psychology
In common: cognitive, mouse, 1 reference

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.