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Timbre Encoding in the Inferior Colliculus.

Code ↔ Paper

22 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 22 matches · 6 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 › Receptive field models ↔ scripts/DoG-model/fitGaussAndDoG.m, the whole file · a weak match · score 0.88 · spontaneous firing rate, squared error, field models, DoG model, Gaussian model, fmincon
  2. [2] § Materials and Methods › Stimuli and analysis ↔ scripts/figures/supp5_time_lapse.m, lines 1–26 · score 0.83 · 50–150 ms, 200–300 ms, quantify, rate profile, synthetic timbre, metric
  3. [3] § Results › Spectral receptive field models with inhibition predicted synthetic-timbre rate profiles ↔ scripts/figures/fig9_dog_analysis.m, lines 1–26 · score 0.81 · receptive field models, excitatory bandwidth, DoG model, Gaussian model, quadrant, ratio
  4. [4] § Materials and Methods › Receptive field models ↔ scripts/figures/fig9_dog_analysis.m, lines 1–26 · score 0.80 · receptive field models, DoG model, Gaussian model, firing rate, excitatory, fit
  5. [5] § Materials and Methods › Stimuli and analysis ↔ scripts/stim-generation/generate_MTF.m, the whole file · a weak match · score 0.79 · modulated noises, random sequence, raised cosine, modulation frequencies, unmodulated, ramps
  6. [6] § Materials and Methods › Computational models for IC responses ↔ scripts/figures/fig10_model_examples.m, lines 1–25 · score 0.79 · frequency inhibition excitation, broad inhibition model, Model fits, goodness, configuration, simulated
  7. [7] § Materials and Methods › Stimuli and analysis ↔ scripts/model-lat-inh/example.m, lines 86–191 · score 0.79 · modulated noises, random sequence, raised cosine, modulation frequencies, unmodulated, ramps
  8. [8] § Materials and Methods › Computational models for IC responses ↔ scripts/figures/supp4_model_temporal.m, lines 1–30 · score 0.71 · frequency inhibition excitation, SFIE model, gammatone, configuration, channels, simulated
  9. [9] § Results › Temporal properties of responses to synthetic-timbre stimuli ↔ scripts/figures/fig5_temporal_examples.m, lines 1–25 · score 0.67 · phase locked, period histogram, vector strength, spectral peak frequencies, temporal, Metrics
  10. [10] § Results › Spectral receptive field models with inhibition predicted synthetic-timbre rate profiles ↔ scripts/DoG-model/fitGaussAndDoG.m, the whole file · a weak match · score 0.65 · field models, DoG model, Gaussian model, receptive, fit, spontaneous
  11. [11] § Results › Temporal properties of responses to synthetic-timbre stimuli ↔ scripts/figures/fig5_temporal_examples.m, lines 1–25 · score 0.64 · phase locking, period histograms, Vector strength, peak frequencies, spectral peaks, synthetic timbre
  12. [12] § Materials and Methods › Stimuli and analysis ↔ scripts/stim-generation/generate_ST.m, lines 1–117 · score 0.63 · harmonic components, raised cosine, ramps, octave, duration, 50 Hz
  13. [13] § Results › Temporal properties of responses to synthetic-timbre stimuli ↔ scripts/figures/supp2_temporal_harms.m, lines 1–27 · score 0.61 · locking metrics, phase locked, vector strength, S2, temporal, neurons
  14. [14] § Materials and Methods › Stimuli and analysis ↔ scripts/helper-functions/analyzeST_Temporal.m, the whole file · a weak match · score 0.59 · phase locking, period histograms, vector strength, Temporal, harmonics, spike
  15. [15] § Results › Basic rate properties of responses to synthetic-timbre stimuli ↔ scripts/model-SFIE/wrapperIC.m, the whole file · a weak match · score 0.59 · population model, BS responses, modulation frequencies, simulate, CFs, IC
  16. [16] § Results › IC models predicted synthetic-rate profiles at one level but failed to describe trends over level ↔ scripts/figures/supp4_model_temporal.m, lines 1–30 · score 0.58 · phase locking, Period histograms, SFIE model, S4, temporal, energy
  17. [17] § Results › IC models predicted synthetic-rate profiles at one level but failed to describe trends over level ↔ scripts/figures/fig10_model_examples.m, lines 1–25 · score 0.58 · broad inhibition models, computational models, neural responses, synthetic timbre, energy, SFIE
  18. [18] § Materials and Methods › Stimuli and analysis ↔ scripts/figures/fig4_rate_examples.m, lines 139–215 · score 0.56 · peak prominence, smoothed rate, findpeaks, CF, Stimuli
  19. [19] § Materials and Methods › Stimuli and analysis ↔ scripts/UR_EAR_2022a/+stimuli/complex_tone.m, the whole file · a weak match · score 0.55 · harmonic complex tone, ramps, components, phase, ear, duration
  20. [20] § Results ↔ scripts/figures/supp1_data_distribution.m, lines 1–23 · score 0.53 · worst modulation frequencies, median, hybrid, BMFs, flat, synthetic timbre
  21. [21] § Results › Basic rate properties of responses to synthetic-timbre stimuli ↔ scripts/stim-generation/generate_ST.m, lines 1–117 · score 0.53 · population model, population response, mid, band, envelope, CFs
  22. [22] § Results › Temporal properties of responses to synthetic-timbre stimuli ↔ scripts/figures/supp2_temporal_harms.m, lines 1–27 · score 0.52 · phase locked, vector strength, S2, temporal, metric, harmonic

Paper

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

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The authors' code

MATLAB · 124 lines · 4.8 KB · no license · 2 matches

  1. function [gaussian_params, dog_params, dog_params2] = fitGaussAndDoG(params, CF, Fs, observed_rate, r0)
  2. % FITGAUSSANDDOG Fits Gaussian and Difference-of-Gaussians (DoG) receptive
  3. % field models to observed neural firing rates using a multi-start fmincon approach.
  4. %
  5. % INPUTS:
  6. % params - Cell array containing stimulus details (params{1}.stim)
  7. % CF - Characteristic Frequency of the neuron
  8. % Fs - Sampling frequency
  9. % observed_rate - The target neural firing rate to fit against
  10. % r0 - Baseline/spontaneous firing rate
  11. %
  12. % OUTPUTS:
  13. % gaussian_params - Optimized parameters for the Gaussian model [center, sigma, gain]
  14. % dog_params - Optimized parameters for DoG Model 1 (independent CFs)
  15. % dog_params2 - Optimized parameters for DoG Model 2 (shared CF)
  17. % --- Initialization & Setup ---
  18. error_type = 2; % 1: Distance-based error, 2: Mean Squared Error (MSE)
  19. stim = params{1}.stim;
  20. log_CF = log10(CF);
  21. timerVal = tic;
  23. % =========================================================================
  24. % 1. FIT GAUSSIAN MODEL (15 multi-starts)
  25. % =========================================================================
  26. best_fval = Inf;
  27. best_gauss_x = [];
  29. % Define optimization options outside the loop to prevent overhead
  30. gauss_options = optimoptions('fmincon', 'Algorithm', 'sqp', ...
  31. 'TolX', 1e-10, 'MaxFunEvals', 10^10, 'MaxIterations', 500, ...
  32. 'ConstraintTolerance', 1e-10, 'StepTolerance', 1e-10, 'Display', 'off');
  34. for istarts = 1:15
  35. % Randomize initial guesses within realistic bounds
  36. s_init = 1 + (4 - 1) * rand(1);
  37. g_init = 1000 * rand(1);
  39. init = [log_CF, s_init, g_init]; % [Center (log_CF), Sigma, Gain]
  40. lb = [log_CF-1, 1, 0]; % Lower bounds
  41. ub = [log_CF+1, 4, Inf]; % Upper bounds
  43. [gaussian_params, fval] = fmincon(@(p) ...
  44. objective_function(p, 'gaussian', Fs, stim, observed_rate, r0, error_type), ...
  45. init, [], [], [], [], lb, ub, [], gauss_options);
  47. % Track the global minimum across starts
  48. if fval < best_fval
  49. best_gauss_x = gaussian_params;
  50. best_fval = fval;
  51. end
  52. end
  53. gaussian_params = best_gauss_x;
  56. % =========================================================================
  57. % 2. FIT DOG MODEL 1 - Independent CFs (15 multi-starts)
  58. % =========================================================================
  59. best_fval = Inf;
  60. best_dog_x = [];
  62. dog_options = optimoptions('fmincon', 'Algorithm', 'sqp', ...
  63. 'TolX', 1e-15, 'MaxFunEvals', 10^15, 'MaxIterations', 800, ...
  64. 'ConstraintTolerance', 1e-15, 'StepTolerance', 1e-15, 'Display', 'off');
  66. for istarts = 1:15
  67. % Randomize initial weights and widths
  68. g_exc_init = 100 + (100000 - 100) * rand(1);
  69. g_inh_init = 100 + (100000 - 100) * rand(1);
  70. s_exc_init = 1 + (4 - 1) * rand(1);
  71. s_inh_init = 1 + (4 - 1) * rand(1);
  73. % Vector: [g_exc, g_inh, s_exc, s_inh, CF_exc, CF_inh]
  74. dog_init = [g_exc_init, g_inh_init, s_exc_init, s_inh_init, log_CF, log_CF];
  75. dog_lb = [100, 100, 1, 1, log_CF-1, log_CF-1];
  76. dog_ub = [100000, 100000, 4, 4, log_CF+1, log_CF+1];
  78. [dog_params, fval] = fmincon(@(p) ...
  79. objective_function(p, 'dog', Fs, stim, observed_rate, r0, error_type), ...
  80. dog_init, [], [], [], [], dog_lb, dog_ub, [], dog_options);
  82. if fval < best_fval
  83. best_dog_x = dog_params;
  84. best_fval = fval;
  85. end
  86. end
  87. dog_params = best_dog_x;
  89. disp(['Gaussian & DoG Model 1 optimization took ', num2str(toc(timerVal)), ' seconds.'])
  92. % =========================================================================
  93. % 3. FIT DOG MODEL 2 - Shared CF (50 multi-starts)
  94. % =========================================================================
  95. best_fval = Inf;
  96. best_dog2_x = [];
  97. timerVal2 = tic; % Reset timer specifically for the second model profile
  99. for istarts = 1:50
  100. g_exc_init = 100 + (100000 - 100) * rand(1);
  101. g_inh_init = 100 + (100000 - 100) * rand(1);
  102. s_exc_init = 1 + (4 - 1) * rand(1);
  103. s_inh_init = 1 + (4 - 1) * rand(1);
  105. % Vector: [g_exc, g_inh, s_exc, s_inh, Shared_CF]
  106. % Note: Ensure your 'objective_function' natively handles 5 parameters for this variation.
  107. dog_init = [g_exc_init, g_inh_init, s_exc_init, s_inh_init, log_CF];
  108. dog_lb = [100, 100, 1, 1, log_CF-1];
  109. dog_ub = [100000, 100000, 4, 4, log_CF+1];
  111. [dog_params2, fval] = fmincon(@(p) ...
  112. objective_function(p, 'dog', Fs, stim, observed_rate, r0, error_type), ...
  113. dog_init, [], [], [], [], dog_lb, dog_ub, [], dog_options);
  115. if fval < best_fval
  116. best_dog2_x = dog_params2;
  117. best_fval = fval;
  118. end
  119. end
  120. dog_params2 = best_dog2_x;
  122. disp(['DoG Model 2 optimization took ', num2str(toc(timerVal2)), ' seconds.'])
  124. end

fitGaussAndDoG.m at commit 312ba33, no license · at the source

Overview

Authors: Johanna B Fritzinger1, Laurel H Carney1,2
ORCID iDs: Johanna B Fritzinger
  1. Departments of Neuroscience, University of Rochester, Rochester, New York 14642
  2. Biomedical Engineering, University of Rochester, Rochester, New York 14642
Institutions: University of Rochester Medicine (United States); University of Rochester (United States)
Journal: The Journal of neuroscience : the official journal of the Society for Neuroscience, volume 46, issue 21, article e1104252026
Dates: received 4 June 2025; accepted 26 March 2026; published online 21 April 2026; in print 27 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1523/jneurosci.1104-25.2026 · PMID 42014202 · PMCID PMC13217535 · OpenAlex W7155052535
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: other (organism), computational (subfield)
Methods: Spectral & time-frequency, Statistics, Connectivity, Preprocessing, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: auditory, auditory midbrain, computational models, inferior colliculus, timbre
MeSH: Auditory Perception*, Inferior Colliculi*, Neurons*, Acoustic Stimulation, Animals, Auditory Threshold, Computer Simulation, Female, Models, Neurological, Rabbits (* major topic)
Topic: Neuroscience and Music Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: HHS | NIH | National Institute on Deafness and Other Communication Disorders (F31-DC020630-03, R01-DC010813)
Citations: not cited yet (Europe PMC); 52 references in the paper

Abstract

Timbre, or the quality of a sound, is a critical component in speech and music. One percept of timbre, brightness, is correlated with the spectral centroid and the spectral envelope of harmonic sounds. Little is known about how this aspect of timbre is encoded in the subcortical auditory system. We used physiological and computational modeling methods to investigate the representation of spectral peaks in a harmonic complex tone with a broad, triangular-shaped spectrum. Extracellular single-neuron recordings were made in the central nucleus of the inferior colliculus (IC) in awake, female Dutch-belted rabbits. A population response to the timbre stimulus was inferred by shifting the stimulus spectrum above and below the characteristic frequency of each neuron. Spectral peaks in the stimulus were encoded in peaks in the average-rate profiles of most neurons, and this representation was robust over a range of suprathreshold levels. Neural discrimination thresholds were also sufficient to describe human behavioral thresholds. Temporal responses were complex and often exhibited phase locking to the fundamental frequency and integer multiples of the fundamental frequency. Computational models that included neural fluctuation sensitivity and amplitude modulation-sensitive broadband inhibition captured the major trends in physiological results. These findings demonstrate that multiple mechanisms may influence robust spectral peak encoding in IC neurons.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 22 matches between paragraphs and lines of code.

urmc.rochester.edu/labs/carney

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Code accessibility”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source: urmc.rochester.edu/labs/carney/publications-code/spike-sorting-code.aspx

OSF uyn56

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code accessibility”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source: osf.io/uyn56

jfritzinger/FritzingerCarney2025-SynthTimbre

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 312ba332330fdb6e18d72dcecd66d42ee1c7beee, 3 July 2026
Languages: MATLAB (207), C (27), C/C++ (1)
Size: 291 files, 235 scripts
Software Heritage: not archived
Found in: “Code accessibility”
Holds: README, tests
Not found: license file, CITATION.cff, environment file, continuous integration, documentation
Tools: Signal Processing Toolbox (17 files), Statistics and Machine Learning Toolbox (12 files), Optimization Toolbox (5 files), CircStat (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
236 files
At the source: github.com/jfritzinger/FritzingerCarney2025-SynthTimbre

Code accessibility

Custom MATLAB code for clustering data (Schwarz et al., 2012) available at https://www.urmc.rochester.edu/labs/carney/publications-code/spike-sorting-code.aspx. Custom MATLAB code for data analysis and modeling along with data files are available at https://osf.io/uyn56. Code is also available on GitHub at https://github.com/jfritzinger/FritzingerCarney2025-SynthTimbre.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

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What the map holds:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 235 scripts, each with its path and the digest of its content;
  • 22 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

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Data

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Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 5 keywords, 10 MeSH terms, 1 funder, 49 references.

Cite

This paper

Fritzinger, J. B., & Carney, L. H. (2026). Timbre Encoding in the Inferior Colliculus. The Journal of neuroscience : the official journal of the Society for Neuroscience, 46(21), e1104252026. https://doi.org/10.1523/jneurosci.1104-25.2026

BibTeX

@article{fritzinger2026timbre,
author = {Fritzinger, Johanna B and Carney, Laurel H},
title = {{Timbre Encoding in the Inferior Colliculus}},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
year = {2026},
month = may,
volume = {46},
number = {21},
pages = {e1104252026},
publisher = {Society for Neuroscience},
issn = {0270-6474},
doi = {10.1523/jneurosci.1104-25.2026},
url = {https://doi.org/10.1523/jneurosci.1104-25.2026},
pmid = {42014202},
pmcid = {PMC13217535}
}

RIS

TY - JOUR
AU - Fritzinger, Johanna B
AU - Carney, Laurel H
TI - Timbre Encoding in the Inferior Colliculus
T2 - The Journal of neuroscience : the official journal of the Society for Neuroscience
J2 - J Neurosci
PY - 2026
DA - 2026/05/27
VL - 46
IS - 21
SP - e1104252026
SN - 0270-6474
PB - Society for Neuroscience
DO - 10.1523/jneurosci.1104-25.2026
UR - https://doi.org/10.1523/jneurosci.1104-25.2026
LA - en
ER -

CSL-JSON

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