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Noise-invariant representations of sound emerge along the canonical cortical hierarchy.

Code ↔ Paper

9 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 9 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 › Data analysis › Pairwise correlations. ↔ Single-Cell-Summaries/extras/+noiseCorr/getNoiseCorr.m, lines 1–56 · score 0.67 · Pearson correlation, simultaneously recorded neurons, Noise correlations, coefficient, subtracting, spike
  2. [2] § Results › L5 IT neurons maintain neural manifold geometry ↔ Manifold-Analysis/plotGeometryOutput.m, lines 21–152 · score 0.66 · Wilcoxon signed rank, manifold capacity, manifold dimensionality, radius, geometry, BN
  3. [3] § Results › BN reduces shared neural variability across spatial scales in IT but not ET neural responses ↔ Single-Cell-Summaries/+plotting/corrDistDivide.m, the whole file · a weak match · score 0.64 · Wilcoxon rank sum, short distances, long distances, correlations
  4. [4] § Results › BN attenuates single-neuron responses in L2/3 ↔ Single-Cell-Summaries/supplementalMotion.m, lines 279–332 · score 0.62 · BF centered curves, way ANOVA, intensity tuning, interaction, dimension, stimulus
  5. [5] § Results › BN attenuates single-neuron responses in L2/3 ↔ Single-Cell-Summaries/extras/matchedComparison_old.m, lines 119–157 · score 0.62 · BF centered curves, deconvolved spikes, intensity tuning curves, stimulus
  6. [6] § Materials and methods › Data analysis › Image processing. ↔ Masked-Noise-Decoding/+utils/is_responsive.m, lines 170–244 · score 0.57 · deconvolved spike, evoked responses, baseline, traces, subtraction, window
  7. [7] § Materials and methods › Data analysis › Mutual information. ↔ Single-Cell-Summaries/+plotting/respHist.m, the whole file · a weak match · score 0.53 · mutual information, bin width, histogramming, probability, Stimulus
  8. [8] § Materials and methods › Data analysis › Tuning curves. ↔ Masked-Noise-Decoding/+utils/is_responsive.m, lines 1–73 · score 0.53 · sound onset, response window, FRA, activity, tuning, matrices
  9. [9] § Results › L5 IT neurons maintain neural manifold geometry ↔ Manifold-Analysis/plotManifoldMetrics.m, lines 128–174 · score 0.50 · Wilcoxon signed rank, manifold metrics, BN

Paper

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The authors' code

MATLAB · 308 lines · 14 KB · MIT · 2 matches

  1. function [resp, resp_tuning] = is_responsive(d, which_method, resp_window, spont_window)
  3. % Rebecca Krall 1/25/22
  4. %
  5. % Takes an experimental struct generated by extract_experimental_data
  6. % and determines if each cell is responsive using different methods to
  7. % easily compare heuristic- and statistics-based methods. This assumes
  8. % that d contains information about the onset of stimulus and the frame
  9. % rate
  11. if nargin < 4
  12. spont_window = 1:14;
  13. end
  14. if nargin < 3
  15. resp_window = 15:29;
  16. end
  19. switch which_method
  21. case 1
  22. % Method 1: Use determine_responsive This normalizes the
  23. % average trace by zscoring to a spontaneous window then
  24. % looking for three consecutive points where the zscore is
  25. % above a threshold
  27. threshold = 3;
  28. for i=1:size(d.spike_traces,1)
  29. resp(i) = determine_responsive(nanmean(d.spike_traces(i,:,:),3),resp_window,spont_window,threshold);
  30. end
  31. resp_tuning = nan(size(d.spike_zscores,1), length(unique(d.inner_index)));
  33. case 2
  34. % Method 2: Use determine responsive based on stimuli This is
  35. % the same as above, but instead of looking at the overall
  36. % response for the neuron it separates it by inner_index
  37. % (frequency for FRA parameter files, intensity for RLF). This
  38. % also returns 'tuning' of the neuron which is a n x m boolean
  39. % matrix where n = number of cells and m = number of stimuli
  40. % and A(n,m) = 1 indicates that cell was responsive to that
  41. % stimuli.
  43. threshold = 3;
  44. for i = 1:size(d.spike_traces, 1)
  45. boo = false;
  46. for j = unique(d.inner_index)
  47. resp_tuning(i,j) = utils.determine_responsive(nanmean(d.spike_traces(i,:,d.inner_index == j),3), resp_window, spont_window, threshold);
  48. boo = boo | resp_tuning(i,j);
  49. end
  50. resp(i) = boo;
  51. end
  54. case 3
  55. % Method 3: Caroline Runyan - Biorxiv "Sound responsiveness was
  56. % calculated based on the mean z-scored deconvolved activity of
  57. % each neuron aligned on sound onset. For each neuron, we
  58. % calculated the difference between the mean activity during
  59. % the sound presentation at a certain location (either 1 or 2
  60. % seconds) and the mean activity in the 240 milliseconds prior
  61. % to sound onset. We calculated sound responsiveness separately
  62. % for each sound location as the mean difference in activity
  63. % between these two windows for each neuron. We then compared
  64. % the observed sound responsiveness of each neuron for each
  65. % sound location to a shuffled distribution. Each cell’s
  66. % activity was shifted randomly by at least 5 seconds in time
  67. % relative to sound location time series, and for 1000
  68. % time-shifted iterations, sound responsiveness to each sound
  69. % location was recalculated. Each sound responsive neuron had a
  70. % positive sound responsiveness value for at least one location
  71. % that was greater than the 95th percentile of that cell’s
  72. % shuffled distribution for that location. All other neurons
  73. % were not considered sound responsive."
  75. % The following code uses Caroline's paper as inspiration but
  76. % not direct methods. Similar to Caroline, a change in z-score
  77. % is calculated between a response window and a window prior to
  78. % sound onset. Then this value is compared to 1000 samples of
  79. % randomly shifted responses (5 - 20 second shifts in the
  80. % trace) to misalign the stimulus and response windows. A cell
  81. % is responsive if the change in zscore for at least one
  82. % frequency is greater than the 98th percentile of the randomly
  83. % shifted dataset
  85. %resp_window = d.stim_onset_frame: d.stim_onset_frame + d.fr/2;
  86. %spont_window = 7 : d.stim_onset_frame-1;
  88. for i = 1:size(d.spike_zscores,1)
  89. trace = squeeze(d.spike_zscores(i,:,:));
  90. delta_spikes = squeeze(nanmean(trace(resp_window,:)) - nanmean(trace(spont_window, :)));
  92. flat_trace = trace(:);
  93. for j = 1:1000
  94. shift = randi([30 600]);
  95. if mod(j,2)
  96. new_trace = [nan(shift,1);flat_trace(1:end-shift)];
  97. else
  98. new_trace = [flat_trace(shift+1:end); nan(shift,1)];
  99. end
  100. new_trace = reshape(new_trace, size(trace));
  101. delta_shift(:,j) = squeeze(nanmean(new_trace(resp_window,:)) - nanmean(new_trace(spont_window, :)));
  102. end
  104. boo = false;
  105. for k = unique(d.inner_index)
  106. zspike = nanmean(delta_spikes(d.inner_index == k));
  108. shifts = nanmean(delta_shift(d.inner_index == k, :));
  109. compare = prctile(shifts, 98);
  110. boo = boo | (compare < zspike);
  111. if compare < zspike
  112. resp_tuning(i, k) = 1;
  113. else
  114. resp_tuning(i,k) = 0;
  115. end
  116. end
  117. resp(i) = boo;
  118. end
  120. case 4
  121. % Method 4 - Walker King 'Complexity of frequency receptive
  122. % fields predicts tonotopic variability across species' A
  123. % two-way ANOVA, with tone frequency and sound level as
  124. % predictors, was used to determine if the evoked activity was
  125. % significantly modulated by sound frequency or intensity (α =
  126. % 0.05). Neurons showing a significant main effect of frequency
  127. % or frequency/level interaction were defined as ‘frequency
  128. % sensitive’, and only these neurons were included in further
  129. % analyses.
  131. % rows = frequencies
  132. % columns = sound intensities
  133. count = 1;
  134. for i = 1:size(d.spike_zscores,1)
  135. for j = unique(d.inner_index)
  136. for k = unique(d.outer_index)
  137. choose = d.outer_index == k & d.inner_index == j;
  138. spont = squeeze(nanmean(d.spike_zscores(i, spont_window, choose),2));
  139. response = squeeze(nanmean(d.spike_zscores(i, resp_window, choose),2));
  140. delta(:,j) = response - spont;
  141. end
  142. end
  143. an = anova2(delta, 5, 'off');
  144. if an(1) < 0.05 | an(3) < 0.05
  145. resp(i) = 1;
  146. else
  147. resp(i) = 0;
  148. end
  149. end
  150. resp_tuning = nan(size(d.spike_zscores,1), length(unique(d.inner_index)));
  153. case 5
  154. % Simple t-test between the mean of the response in the
  155. % spontaneous window and the response window
  156. for i = 1:size(d.spike_zscores,1)
  157. smoothed = smooth(mean(d.spike_traces(i,:,:),3));
  158. [~,loc_temp] = max(smoothed(resp_window,:));
  159. peak_latency = resp_window(1)+loc_temp-1;
  161. for j = unique(d.inner_index)
  162. choose = d.inner_index == j;
  163. current_trace = squeeze(d.spike_zscores(i,:,choose));
  165. tuning_window = [peak_latency-1:peak_latency+1];
  166. peak = mean(current_trace(tuning_window, :));
  167. mean_response = mean(current_trace(resp_window, :));
  168. baseline = mean(current_trace(spont_window, :));
  170. %thresh = mode(peak > 1);
  171. [h,~] = ttest(baseline, mean_response, 'Alpha', .01, 'Tail', 'left');
  172. resp_tuning(i,j) = h; %& thresh;
  173. end
  174. end
  176. resp = (sum(resp_tuning,2) > 0)';
  179. case 6
  181. for i = 1:size(d.spike_zscores,1)
  182. z = squeeze(d.spike_zscores(i,:,:));
  183. tr = squeeze(d.spike_traces(i,:,:));
  184. [delta_spikes, lat] = determine_baseline_subtracted_peak(tr, z);
  186. flat_tr = tr(:);
  187. flat_z = z(:);
  189. for j = 1:1000
  190. shift = randi([30 600]);
  191. if mod(j,2)
  192. new_tr = [nan(shift,1);flat_tr(1:end-shift)];
  193. new_z = [nan(shift,1);flat_z(1:end-shift)];
  194. else
  195. new_tr = [flat_tr(shift+1:end); nan(shift,1)];
  196. new_z = [flat_z(shift+1:end); nan(shift,1)];
  197. end
  199. shift_tr = reshape(new_tr, size(tr));
  200. shift_z = reshape(new_z, size(z));
  201. [delta_shift(:,j), ~] = determine_baseline_subtracted_peak(shift_tr, shift_z, lat);
  202. end
  204. boo = false;
  205. for k = unique(d.inner_index)
  206. zspike = nanmean(delta_spikes(d.inner_index == k));
  208. shifts = nanmean(delta_shift(d.inner_index == k,:));
  209. compare = prctile(shifts, 98);
  210. boo = boo | (compare < zspike);
  211. if compare < zspike
  212. resp_tuning(i, k) = 1;
  213. else
  214. resp_tuning(i,k) = 0;
  215. end
  216. end
  217. resp(i) = boo;
  218. end
  221. case 7
  222. % Method 7 - Kato et al, 2017 (Network-Level Control of
  223. % Frequency Tuning in Auditory Cortex) This method is adapted
  224. % from Kato's paper, which uses dF/F, to be applicable to
  225. % deconvolved spikes. Sound-evoked responses are based on two
  226. % criteria:
  227. % (1) At least 50% of trials at a cell's best frequency must
  228. % have 3 consecutive frames exceed 0.5 SD from baseline
  229. % (2) Average tone responses (across all stimuli) must have 3
  230. % consecutive frames exceed 3 SD from baseline
  233. respThres = 0.5; % min no. of SDs above baseline during resp window
  234. respThres_avr = 3; % response threshold for Criteria #1
  235. frAboveThres = 3; % min no. of consecutive frames above respThres
  236. propOfTrials = 0.4; % proportion of trials where response is above threshold
  237. nTrials = size(d.spike_zscores,3); % no. of trials
  240. if isempty(d.inner_sequence) && sum(d.inner_index)==numel(d.inner_index)
  241. inner_sequence = 1;
  242. else
  243. inner_sequence = d.inner_sequence;
  244. end
  246. % Extract BF based on avr z-scored response across resp_window
  247. respWindTuning = zeros(size(d.spike_zscores,1),numel(d.inner_sequence));
  248. for stim = 1:numel(d.inner_sequence)
  249. tStim = find(d.inner_index==stim);
  250. dums = d.spike_zscores(:,resp_window,tStim);
  251. avrTrialResp = mean(dums,3,'omitnan'); % [cells * frames in resp_wind]
  252. respWindTuning(:,stim) = mean(avrTrialResp,2,'omitnan');
  253. end
  255. if inner_sequence==1 & sum(d.inner_index)==numel(d.inner_index)
  256. BF = ones(1,size(d.spike_zscores,1)); % essentially making all cells have same BF (work around)
  257. else
  258. maxResp = max(respWindTuning,[],2,'omitnan');
  259. for neuron = 1:size(respWindTuning,1)
  260. BF(neuron) = find(respWindTuning(neuron,:)==maxResp(neuron));
  261. end
  262. end
  264. % Determine if cell is responsive based on response to BF
  265. % NOTE: only 1D stimuli are applicable for this method
  266. for neuron = 1:size(d.spike_zscores,1)
  268. spikes = squeeze(d.spike_traces(neuron,:,:));
  269. psth_avr = mean(d.spike_traces(neuron,:,:),3,'omitnan');
  270. psth_zscored_avr = smooth((psth_avr-nanmean(psth_avr(spont_window)))/nanstd(psth_avr(spont_window)));
  273. BF_trials = find(d.inner_index==BF(neuron));
  274. % Grab traces
  275. dums = spikes(spont_window,BF_trials);
  276. psth_BF = squeeze(d.spike_traces(neuron,:,BF_trials)); % [frames * trials]
  277. psth_zscored_BF = (psth_BF-nanmean(psth_avr(spont_window))) ./ nanstd(dums(:));
  278. for trial = 1:numel(BF_trials)
  279. psth_smooth(:,trial) = smooth(psth_zscored_BF(:,trial));
  280. end
  281. psth_zscored_smoothBF = psth_smooth;
  283. % Criteria #1
  284. idx_criteriaOne = psth_zscored_smoothBF(resp_window,:) >= respThres;
  285. [~,c] = find(movsum(idx_criteriaOne,frAboveThres) >= frAboveThres);
  286. trials_idx = unique(c);
  288. % Criteria #2
  289. idx_criteriaTwo = psth_zscored_avr(resp_window) >= respThres_avr;
  290. frForCriteriaTwo = find(movsum(idx_criteriaTwo,frAboveThres) >= frAboveThres);
  292. criteriaOne = (numel(trials_idx) / (numel(BF_trials))) >= propOfTrials;
  293. criteriaTwo = ~isempty(frForCriteriaTwo);
  295. if criteriaOne & criteriaTwo
  296. resp(neuron) = 1;
  297. else
  298. resp(neuron) = 0;
  299. end
  300. resp_tuning=[]; % will change this later
  302. end
  304. %%
  307. end % switch/case
  308. end % function

is_responsive.m at commit 520d1f2, under MIT · at the source

Overview

Authors: Tomas Suarez Omedas1,2,3, Ross S. Williamson2,3,4,5,6
ORCID iDs: Ross S. Williamson
  1. Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
  2. Pittsburgh Hearing Research Center, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
  3. Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
  4. Department of Otolaryngology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
  5. Department of Neurobiology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
  6. Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
Institutions: University of Pittsburgh (United States); Center for the Neural Basis of Cognition (United States); Carnegie Mellon University (United States)
Journal: PLoS biology, volume 24, issue 7, article e3003915
Dates: received 19 December 2025; accepted 8 July 2026; published online 20 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pbio.3003915 · PMID 42475408 · PMCID PMC13399537 · OpenAlex W7169786066
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging, Single-unit activity, calcium imaging
MeSH: Auditory Cortex*, Auditory Perception*, Acoustic Stimulation, Animals, Auditory Pathways, Male, Mice, Neurons, Noise, Sound (* major topic)
Journal subjects: Biology and Life Sciences, Neuroscience, Neuronal Tuning, Computational Biology, Computational Neuroscience, Single Neuron Function, Cell Biology, Cellular Types, Animal Cells, Neurons, Cellular Neuroscience, Physical sciences, Mathematics, Geometry, Non-Euclidean geometry, Cognitive Science, Cognitive Psychology, Perception, Sensory Perception, Psychology, Social Sciences, Cognition, Engineering and Technology, Signal Processing, White Noise, Behavior
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute on Deafness and Other Communication Disorders (R21DC018327, R01DC020459); Esther A. and Joseph Klingenstein Fund (EAJK Fund) (Klingenstein-Simons Fellowship Award in Neuroscience)
Citations: not cited yet (Europe PMC); 115 references in the paper

Abstract

Neurons in the auditory system must represent behaviorally relevant sounds in the presence of background noise (BN) to support noise-invariant perception and behavior. Although the primary auditory cortex (ACtx) has been implicated in constructing noise-invariant representations, it remains unclear which excitatory subpopulations within ACtx carry out this transformation from noise-dependent to noise-invariant coding. To address this, we presented pure tones with and without continuous BN to head-fixed mice and used two-photon calcium imaging to record sound-evoked activity from three major excitatory subpopulations in ACtx: layer (L)2/3 intratelencephalic (IT) neurons, L5 IT neurons, and L5 extratelencephalic (ET) neurons. L2/3 IT neurons exhibited strong noise dependence at the level of single-neuron responses, pairwise interactions, and population representations. In contrast, deep-layer pathways showed greater noise invariance, with L5 IT neurons preserving stable representations most consistently and L5 ET neurons exhibiting more limited invariance at the population level. These findings reveal a functional division of labor in ACtx, in which superficial neurons remain noise-dependent and deep-layer broadcast pathways, particularly L5 IT, preferentially carry noise-invariant representations, suggesting that excitatory subpopulations contribute differentially to the construction and propagation of noise-invariant codes.

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

Repositories

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WilliamsonLab-Pitt/BackgroundNoise-Characterization

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 520d1f2cb42eeda3c726caa693fe0d57b209129d, 27 June 2026
Languages: MATLAB (290)
Size: 370 files, 290 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, license file, tests
Not found: CITATION.cff, environment file, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (42 files), Optimization Toolbox (2 files), Curve Fitting Toolbox (1 file), Violinplot-Matlab (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
292 files
At the source: github.com/WilliamsonLab-Pitt/BackgroundNoise-Characterization

RichieHakim/ROICaT

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 487bb6f3c0b9021f10d8fd7581921949d1674c5b, 26 September 2026
Languages: Python (37), Jupyter (9), Shell (2)
Size: 91 files, 48 scripts
Software Heritage: not archived
Found in: the text, “Image processing.”
Holds: README, license file, environment (pyproject.toml, docs/requirements.txt), tests, continuous integration, documentation, 9 notebooks
Not found: CITATION.cff
Tools: NumPy (33 files), PyTorch (22 files), Matplotlib (19 files), SciPy (17 files), Pillow (10 files), scikit-learn (10 files), pandas (9 files), OpenCV (4 files), Numba (2 files), UMAP (2 files), h5py (1 file), scikit-image (1 file), seaborn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
50 files
At the source: github.com/RichieHakim/ROICaT

The paper's code and data availability statement is in the Data section.

Tracing map

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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;
  • 338 scripts, each with its path and the digest of its content;
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Data

Datasets cited

Data Availability

The data generated in this study have been deposited in an open-access Zenodo repository and can be accessed here: https://doi.org/10.5281/zenodo.21111739. Custom scripts used to analyze the data have been deposited to the same Zenodo repository, as well as in a GitHub repository, which can be found here: https://github.com/WilliamsonLab-Pitt/BackgroundNoise-Characterization.

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

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 10 MeSH terms, 2 funders, 112 references.

Cite

This paper

Suarez Omedas, T., & Williamson, R. S. (2026). Noise-invariant representations of sound emerge along the canonical cortical hierarchy. PLoS biology, 24(7), e3003915. https://doi.org/10.1371/journal.pbio.3003915

BibTeX

@article{suarezomedas2026noise,
author = {Suarez Omedas, Tomas and Williamson, Ross S.},
title = {{Noise-invariant representations of sound emerge along the canonical cortical hierarchy}},
journal = {PLoS biology},
year = {2026},
month = jul,
volume = {24},
number = {7},
pages = {e3003915},
publisher = {PLOS},
issn = {1544-9173},
doi = {10.1371/journal.pbio.3003915},
url = {https://doi.org/10.1371/journal.pbio.3003915},
pmid = {42475408},
pmcid = {PMC13399537}
}

RIS

TY - JOUR
AU - Suarez Omedas, Tomas
AU - Williamson, Ross S.
TI - Noise-invariant representations of sound emerge along the canonical cortical hierarchy
T2 - PLoS biology
J2 - PLoS Biol
PY - 2026
DA - 2026/07/20
VL - 24
IS - 7
SP - e3003915
SN - 1544-9173
PB - PLOS
DO - 10.1371/journal.pbio.3003915
UR - https://doi.org/10.1371/journal.pbio.3003915
LA - en
ER -

CSL-JSON

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"DOI": "10.1371/journal.pbio.3003915",
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"PMCID": "PMC13399537",
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[5] doi:10.1038/s42003-026-10957-8 [code]
Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: Optimization Toolbox, UMAP, Numba, 11 other tools, mouse
[6] doi:10.3389/fnsys.2026.1822122 [code]
Convergence-divergence circuits for multimodal integration of innate and learned opponent valences.
Journal: Frontiers in systems neuroscience
In common: UMAP, Numba, scikit-image, 9 other tools, 2 references
[7] doi:10.1038/s41593-026-02232-0 [code]
Entorhinal cortex represents task-relevant remote locations independently of CA1.
Journal: Nature neuroscience
In common: Optimization Toolbox, Numba, OpenCV, 11 other tools, mouse
[8] doi:10.1016/j.patter.2026.101590 [code]
Density-based longitudinal neuron tracking in high-density electrophysiological recordings.
Journal: Patterns (New York, N.Y.)
In common: Violinplot-Matlab, Optimization Toolbox, UMAP, 10 other tools
[9] doi:10.1038/s41467-026-76581-6 [code]
Thalamocortical bursts encode reward contingencies and drive associative learning.
Journal: Nature communications
In common: Violinplot-Matlab, Curve Fitting Toolbox, OpenCV, 8 other tools, mouse, 1 reference
[10] doi:10.1016/j.isci.2026.116825 [code]
Social hierarchy shapes behavioral and transcriptional responses to chronic stress and ketamine in male mice.
Journal: iScience
In common: UMAP, Numba, OpenCV, 10 other tools, mouse

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