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Acetylcholine demixes heterogeneous dopamine signals for learning and moving.

Code ↔ Paper

21 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 21 matches · 12 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Logistic regression model ↔ DMS_AChDA/vis/plot_fig6h.m, lines 32 to 117 · score 0.88 · logistic regression, classification error, cross validate, 0–0.5 s, fitclinear, ridge · Report a mismatch
  2. [2] § Results › Dopamine predicts learning when it lags cholinergic dips ↔ DMS_AChDA/vis/plot_fig3g_left.m, lines 1 to 81 · score 0.77 · model predicted RPE, 0–0.5 s, model simulation, dopamine AUC, DA rats, reward cue · Report a mismatch
  3. [3] § Results › Dopamine fails to predict learning when it precedes cholinergic dips ↔ DMS_AChDA/vis/plot_fig4i_left.m, lines 1 to 46 · score 0.72 · short delay, delay period, reward delay distribution, top quartile, long delay, reward port · Report a mismatch
  4. [4] § Results › Dopamine predicts movement vigor when coincident with cholinergic bursts ↔ DMS_AChDA/vis/plot_fig6h.m, lines 32 to 117 · score 0.72 · logistic regression, cross validated, 0–0.5 s, contralateral trial, reward port, speed · Report a mismatch
  5. [5] § Results › Dopamine predicts learning when it lags cholinergic dips ↔ DMS_AChDA/vis/plot_fig3ab_xcorr.m, the whole file · a weak match · score 0.71 · cross correlation, ACh rats, reward delay quartiles, DA rats, reward cue, lag · Report a mismatch
  6. [6] § Results › Dopamine predicts learning when it lags cholinergic dips ↔ DMS_AChDA/vis/plot_fig3ab_xcorr.m, the whole file · a weak match · score 0.69 · Cross correlation, ACh rats, reward delay quartiles, DA rats, reward cue, lag · Report a mismatch
  7. [7] § Methods › Behavioral model ↔ DMS_AChDA/vis/plot_fig3f.m, the whole file · a weak match · score 0.67 · qualitatively best, RPE model, trial initiation, simulate, delay · Report a mismatch
  8. [8] § Methods › Behavioral model ↔ DMS_AChDA/vis/plot_fig3c.m, the whole file · a weak match · score 0.65 · qualitatively best, RPE model, trial initiation, simulate, volume · Report a mismatch
  9. [9] § Methods › Electrophysiology experiment › Neuropixels data acquisition ↔ CorticostriatalProjectionNeurons/Utils/make_ops.m, the whole file · a weak match · score 0.64 · DAQmx, Neuropix PXI, NI, Spike, event · Report a mismatch
  10. [10] § Results › Dopamine predicts movement vigor when coincident with cholinergic bursts ↔ EstrousRPEPaper/process/ProcessData_Figure2.m, the whole file · a weak match · score 0.64 · Wilcoxon signed rank, 0–0.5 s, DA rats, population, accuracy, port · Report a mismatch
  11. [11] § Methods › Electrophysiology experiment › Neuropixels data acquisition ↔ CorticostriatalProjectionNeurons/Utils/extractControllerClock.m, the whole file · a weak match · score 0.61 · Neuropix PXI, OpenEphys, NI · Report a mismatch
  12. [12] § Methods › Trial initiation versus dopamine AUC analysis ↔ EstrousRPEPaper/process/ProcessData_ExtendedData6.m, the whole file · a weak match · score 0.61 · 0–0.5 s, baseline corrected, Event aligned, offer cue, AUC · Report a mismatch
  13. [13] § Methods › Trial initiation versus dopamine AUC analysis ↔ DMS_AChDA/vis/plot_fig3e_right.m, lines 1 to 78 · score 0.61 · 0–0.5 s, dopamine AUC, offer cue, bins, initiation · Report a mismatch
  14. [14] § Results › Evidence for in vivo plasticity ↔ DMS_AChDA/vis/plot_fig5g.m, the whole file · a weak match · score 0.59 · negligible RPE, cells aligned, negative RPE, offer cue, consecutive, FR · Report a mismatch
  15. [15] § Results › Evidence for in vivo plasticity ↔ DMS_AChDA/vis/plot_fig5g.m, the whole file · a weak match · score 0.56 · negligible RPE, negative RPE, firing rate, offer cue, consecutive, DA · Report a mismatch
  16. [16] § Methods › Event-aligned photometry signals ↔ DMS_AChDA/vis/plot_fig2f.m, the whole file · a weak match · score 0.55 · reward port side, Event aligned, baseline correction, split, signals, photometry · Report a mismatch
  17. [17] § Methods › Behavior training and task logic ↔ rat_behavior/Figures/Figure3.m, lines 47 to 109 · score 0.55 · exponential distribution, catch trials, chosen, 15 %, uniform, 25 % · Report a mismatch
  18. [18] § Methods › Logistic regression model ↔ DMS_AChDA/vis/plot_fig6d.m, lines 1 to 66 · score 0.54 · 0–0.5 s, top quartile, reward port, fast, reaction, DMS · Report a mismatch
  19. [19] § Results › Dopamine and acetylcholine show distinct dynamics at reward-associated and movement-associated events ↔ DMS_AChDA/vis/plot_fig2f.m, the whole file · a weak match · score 0.53 · event aligned dopamine, release split, baseline corrected, ipsi, Contra, sensors · Report a mismatch
  20. [20] § Results › Dopamine predicts learning when it lags cholinergic dips ↔ DMS_AChDA/vis/plot_fig3ab_latency.m, lines 1 to 81 · score 0.53 · ACh rats, DA rats, trough, reward cue, offer cue, peak · Report a mismatch
  21. [21] § Results › Dopamine predicts learning when it lags cholinergic dips ↔ DMS_AChDA/vis/plot_fig3ab_latency.m, lines 1 to 81 · score 0.52 · ACh rats, DA rats, trough, reward cue, offer cue, peak · Report a mismatch

Paper

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The authors' code

MATLAB · 121 lines · 3.2 KB · MIT · 2 matches

  1. function plot_fig6h(datadir)
  2. % Plot average cross-validated classification accuracy of a logistic
  3. % regression decoder. N = 18 sessions from 8 rats.
  4. %
  5. % INPUTS
  6. % datadir: file path of data downloaded from Zenodo. Link to download can
  7. % be found in README.
  9. fpath = fullfile(datadir, 'data-published/NpxData/npx_DMS.mat');
  10. load(fpath, 'S', 'SU');
  11. [cvPerf, ~] = decodeSpeed(SU, S);
  13. figure
  14. set(gcf, units='inches', renderer='painter', ...
  15. position=[9,5,1.6,1.9])
  16. tiledlayout('flow');
  17. nexttile;
  18. hold on
  19. jitx = rand(size(cvPerf,1),1)*0.1;
  20. jitx(1:end/2) = -jitx(1:end/2);
  21. plot(1+jitx, cvPerf(:,1), 'ko')
  22. errorbar(1, mean(cvPerf(:,1), 'omitnan'), std(cvPerf(:,1), 'omitnan'), ...
  23. 'k_', capsize=0, linewidth=1.5)
  24. yline(50, 'k--')
  25. xlim([0.5 1.5])
  26. ylabel('Cross-validated accuracy (%)')
  27. xticks([])
  28. set(gca, tickdir='out', xtick=1, fontsize=8)
  30. end
  32. function [cvPerf, ridgeWeights] = decodeSpeed(SU, S)
  33. % calculate cross-validated logistic regression linear classifier error
  34. % of predicting fast vs slow trials from firing rate at Reward Port On.
  35. % Contralateral trials only.
  38. twin = [0 0.5];
  39. cvErr = nan(length(SU),1);
  40. ridgeWeights = nan(length(SU),1);
  42. for sess=1:length(S)
  43. if length(SU{sess}) < 5 % exclude sessions with fewer than 5 cells
  44. continue
  45. else
  46. ratName = S{sess}.RatName;
  48. % get contralateral trials
  49. if sum(strcmpi(ratName, {'j081', 'j076'}))>0
  50. recHemi = 'R';
  51. else
  52. recHemi = 'L';
  53. end
  54. isContra = cellfun(@(x) ~strcmpi(x, recHemi), S{sess}.RewardedSide)';
  56. % For Reward Port On:
  57. % get reaction time
  58. RT = S{sess}.slrt;
  59. % filter outliers
  60. thresh = [];
  61. thres(1) = prctile(RT, 1); % lower threshold
  62. thresh(2) = prctile(RT, 99); % upper threshold
  63. RT(RT<=thresh(1)) = nan;
  64. RT(RT>=thresh(2)) = nan;
  65. % nan ipsi trials
  66. RT(~isContra) = nan;
  68. % bin reaction times
  69. quantiles = [0 1/4 2/4 3/4 1];
  70. bins = quantile(log2(RT), quantiles);
  72. % get fast vs slow trials
  73. isFast = log2(RT)>=bins(1) & log2(RT)<=bins(2);
  74. isSlow = log2(RT)>=bins(end-1) & log2(RT)<=bins(end);
  75. fprintf('Slow: %i trials, Fast: %i trials\n', ...
  76. sum(isSlow), sum(isFast))
  78. % create true/false vector
  79. y = find(isFast | isSlow);
  80. y(ismember(y,find(isFast))) = 1;
  81. y(ismember(y,find(isSlow))) = 0;
  83. % construct trial x neuron x T tensor
  84. X = [];
  86. T = SU{sess}{1}.xvec.SON;
  87. [~,t1] = min(abs(T-twin(1)));
  88. [~,t2] = min(abs(T-twin(2)));
  90. for n=1:length(SU{sess})
  91. these = isFast | isSlow;
  92. X(:,n,:) = SU{sess}{n}.hmat.SON(these,t1:t2);
  93. end
  95. % flatten
  96. X = reshape(X, sum(these), []);
  99. % fit model
  100. Lambda = logspace(-4,-0.2,11);
  101. mdl = fitclinear(X', y, ...
  102. 'ObservationsIn', 'columns', ...
  103. 'CrossVal', 'on', 'Learner', 'logistic', ...
  104. 'Regularization', 'ridge', 'Lambda', Lambda);
  105. [cvErr(sess,1), ridgeWeights(sess,1)] = min(kfoldLoss(mdl));
  107. end
  108. end
  110. %
  111. rmv = isnan(cvErr(:,1));
  112. cvErr(rmv,:) = [];
  113. ridgeWeights(rmv,:) = [];
  114. cvPerf = 100*(1-cvErr);
  117. end

plot_fig6h.m at commit b7b68ca, under MIT · at the source

Overview

Authors: Hee Jae Jang1, Royall McMahon Ward1, Carla E M Golden1, Christine M Constantinople1
ORCID iDs: Carla E M Golden, Christine M Constantinople
  1. Center for Neural Science, New York University, New York City, NY USA
Institutions: New York University (United States)
Journal: Nature neuroscience, volume 29, issue 4, pages 840-850
Dates: received 10 October 2025; accepted 27 January 2026; published online 25 March 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41593-026-02227-x · PMID 41882194 · PMCID PMC13061617 · OpenAlex W7140303701
Open access: hybrid, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: rat (organism), cognitive (subfield)
Methods: Connectivity, Machine learning, Statistics, Preprocessing, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Reward, Basal ganglia, Motivation, Neural circuits
MeSH: Acetylcholine*, Corpus Striatum*, Dopamine*, Learning*, Animals, Dopaminergic Neurons, Male, Movement, Rats, Rats, Long-Evans, Reward (* major topic)
Topic: Neurotransmitter Receptor Influence on Behavior (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: U.S. Department of Health & Human Services | National Institutes of Health (NIH) (R01MH136272); Alfred P. Sloan Foundation; U.S. Department of Health &amp; Human Services | National Institutes of Health (R01MH136272)
Citations: cited by 17 papers (Europe PMC); 80 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 21 matches between paragraphs and lines of code.

constantinoplelab/published

License: MIT
State: the link answers, verified on 31 October 2026
Evidence: files inventoried
Commit: b7b68ca8c107d8ce4a061e16da214307f175c3e5, 25 September 2026
Languages: MATLAB (449), Shell (3)
Size: 499 files, 452 scripts
Software Heritage: archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (111 files), shadedErrorBar (59 files), Optimization Toolbox (7 files), Signal Processing Toolbox (1 file), Violinplot-Matlab (1 file)
Availability: 2 checks, the latest on 31 October 2026: the link answers
  • 31 October 2026: the link answers
  • 30 September 2026: the link answers
454 files
At the source: github.com/constantinoplelab/published/tree/main/DMS_AChDA

Code availability statement

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

Read it in the paper: doi.org/10.1038/s41593-026-02227-x.

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  • 21 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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Data availability statement

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

Read it in the paper: doi.org/10.1038/s41593-026-02227-x.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 4 keywords, 11 MeSH terms, 3 funders, 73 references.

Cite

This paper

Jang, H. J., McMahon Ward, R., Golden, C. E. M., & Constantinople, C. M. (2026). Acetylcholine demixes heterogeneous dopamine signals for learning and moving. Nature neuroscience, 29(4), 840-850. https://doi.org/10.1038/s41593-026-02227-x

BibTeX

@article{jang2026acetylcholine,
author = {Jang, Hee Jae and McMahon Ward, Royall and Golden, Carla E M and Constantinople, Christine M},
title = {{Acetylcholine demixes heterogeneous dopamine signals for learning and moving}},
journal = {Nature neuroscience},
year = {2026},
month = mar,
volume = {29},
number = {4},
pages = {840--850},
publisher = {Nature Portfolio},
issn = {1097-6256},
doi = {10.1038/s41593-026-02227-x},
url = {https://doi.org/10.1038/s41593-026-02227-x},
pmid = {41882194},
pmcid = {PMC13061617}
}

RIS

TY - JOUR
AU - Jang, Hee Jae
AU - McMahon Ward, Royall
AU - Golden, Carla E M
AU - Constantinople, Christine M
TI - Acetylcholine demixes heterogeneous dopamine signals for learning and moving
T2 - Nature neuroscience
J2 - Nat Neurosci
PY - 2026
DA - 2026/03/25
VL - 29
IS - 4
SP - 840
EP - 850
SN - 1097-6256
PB - Nature Portfolio
DO - 10.1038/s41593-026-02227-x
UR - https://doi.org/10.1038/s41593-026-02227-x
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41593-026-02227-x",
"type": "article-journal",
"title": "Acetylcholine demixes heterogeneous dopamine signals for learning and moving",
"container-title": "Nature neuroscience",
"author": [
{
"family": "Jang",
"given": "Hee Jae"
},
{
"family": "McMahon Ward",
"given": "Royall"
},
{
"family": "Golden",
"given": "Carla E M"
},
{
"family": "Constantinople",
"given": "Christine M"
}
],
"container-title-short": "Nat Neurosci",
"volume": "29",
"issue": "4",
"page": "840-850",
"DOI": "10.1038/s41593-026-02227-x",
"PMID": "41882194",
"PMCID": "PMC13061617",
"ISSN": "1097-6256",
"publisher": "Nature Portfolio",
"URL": "https://doi.org/10.1038/s41593-026-02227-x",
"language": "en",
"issued": {
"date-parts": [
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2026,
3,
25
]
]
}
}

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