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Dopaminergic mechanisms of dynamical social specialization.

Code ↔ Paper

3 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 3 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Modelling › Building a behavioural model of e-mouse behaviours in lone and social conditions ↔ ModellingScripts.zip/FULL_MODEL_PARAMETERS.m, lines 1–78 · score 0.86 · inverse temperature, discount factor, mixed box, full model, rooms, transition
  2. [2] § Explore–exploit trade-off shapes roles ↔ ModellingScripts.zip/FULL_MODEL_PARAMETERS.m, lines 1–78 · score 0.72 · temporal discount factor, inverse temperature, transition, mixed, model, social
  3. [3] § Methods › Modelling › Building a behavioural model of e-mouse behaviours in lone and social conditions ↔ ModellingScripts.zip/FULL_MODEL_SIMULATION.m, the whole file · a weak match · score 0.59 · full model, accessible, fatigue, softmax, satiety, reward

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 · 2.9 KB · CC-BY-4.0 · 2 matches

  1. function M = FULL_MODEL_PARAMETERS(M)
  2. M.Verbose = 1;
  3. % Time -------
  4. M.dt = 1; % s
  5. M.dt_real = 2; % s / time in reality per M.dt
  6. M.nt = 10000;
  7. M.t_max = M.nt * M.dt_real;
  8. % Social ------
  9. M.n_Mice = 3;
  10. load Is_Mega_Batch; if Is_Mega_Batch; M.Verbose = 0; load Mega_Batch_n_Mice; M.n_Mice = Mega_Batch_n_Mice; end
  11. load Is_Cluster_Analysis; if Is_Cluster_Analysis; M.Verbose = 0; load Cluster_Analysis_n_Mice; M.n_Mice = Cluster_Analysis_n_Mice; end
  12. M.Ones_n_Mice = ones(M.n_Mice,1);
  13. % Learning Type -------
  14. M.Eat_Rwd_Unit_Value = 1;
  15. M.Eat_Rwd_Unit = M.Eat_Rwd_Unit_Value * M.Ones_n_Mice;
  16. % Learning Parameters -------
  17. M.alpha_Common = 0.05;
  18. M.alpha = M.alpha_Common * M.Ones_n_Mice; % learning rate
  19. M.beta_Male = 2;
  20. M.beta_Female = 0.5;
  21. M.beta_Common = M.beta_Male;
  22. % M.beta_Common = M.beta_Female;
  23. % M.beta_Common = -1; % Mixed box (only for n_mice = 3)
  24. M.beta = M.beta_Common * M.Ones_n_Mice; % inverse temperature factor (choice randomness)
  25. if M.beta_Common == -1; M.beta = [M.beta_Female M.beta_Female M.beta_Male]; end
  26. M.gamma_Common = 0.99;
  27. M.gamma = M.gamma_Common * M.Ones_n_Mice; % temporal discount factor
  28. % Lever On/Off -------
  29. M.Is_Lever_OnOff = 1; % Lever is inoperant for M.Is_Lever_OnOff s after each press (for all Mice)
  30. M.Off_Duration = 5; % s
  31. M.dt_Lever_Off = ceil(M.Off_Duration/M.dt_real); % # of lever Off time steps after on
  32. % Pressing Fatigue -------
  33. M.Pressing_Fatigue = 1;
  34. M.p_Pressing_Max = 1;
  35. M.Delta_p_Pressing_Fatigue = 0.5;
  36. M.Tau_Pressing_Fatigue = 25;
  37. % Eating Fatigue -------
  38. M.Eating_Fatigue = 1;
  39. M.p_Eating_Max = 1;
  40. M.Delta_p_Eating_Fatigue = 0.5;
  41. M.Tau_Eating_Fatigue = 25;
  42. % Eating Sasiety -------
  43. M.Eating_Sasiety = 1;
  44. M.n_Pellets_Satiety = 150*M.Ones_n_Mice; % Max pellets per animal per day
  45. M.alpha_Satiety = 20;
  46. % Space -------
  47. M.Connections = ...
  48. [ 1 1 0 0 0 0; ... % Room 1
  49. 1 1 1 1 0 0; ... % Room 2
  50. 0 1 1 1 0 0; ... % Lever
  51. 0 1 1 0 1 0; ... % Room 3
  52. 0 0 0 1 0 1; ... % Room 4
  53. 0 0 0 0 1 1]; % Dispenser
  54. M.Connections_Dispenser_Occup = ...
  55. [ 1 1 0 0 0 0; ... % Room 1
  56. 1 1 1 1 0 0; ... % Room 2
  57. 0 1 1 1 0 0; ... % Lever
  58. 0 1 1 0 1 0; ... % Room 3
  59. 0 0 0 1 0 0; ... % Room 4
  60. 0 0 0 0 1 1]; % Dispenser
  61. M.n_Pos = size(M.Connections,1);
  62. for k_Pos = 1:M.n_Pos
  63. M.Connect_V{k_Pos} = find(M.Connections(k_Pos,:));
  64. M.Connect_Dispenser_Occup_V{k_Pos} = find(M.Connections_Dispenser_Occup(k_Pos,:));
  65. end
  66. M.CompSeq_Identity = [3 4 5 6];
  67. M.Pos_Room_Idx = [1 2 4 5];
  68. M.n_Pos_Room = length(M.Pos_Room_Idx);
  69. M.Pos_Str = {'Room 1', 'Room 2', 'Lever', 'Room 3', 'Room 4', 'Dispenser'};
  70. M.Pos_Char = {'1','2','L','3','4','F'};
  71. M.n_Pos = size(M.Connections,1);
  72. M.Room_Code = 1;
  73. M.Lever_Code = 3;
  74. M.Dispenser_Code = 6;
  75. M.X = [-1 -1 0 1 1 0.5];
  76. M.Y = [ 1 -1 -2 -1 1 2];
  77. M.n_Possible_Transitions = length(find(M.Connections));
  78. M.Length_CompSeq = length(M.CompSeq_Identity);

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

Overview

Authors: C. Solié1, A. Nicolson1, R. Justo1, Y. Layadi1, B. Morin1, C. Batifol1, L. M. Reynolds1, T. Le Borgne1, S. L. Fayad1, A. Gulmez1, Y. Rodriguez Quevedo1, J. Allegret-Vautrot1, G. Centene Guglielmi1, F. de Chaumont2, S. Didienne1, N. Debray1, J.-P. Hardelin1, B. Girard3, A. Mourot1, J. Naudé4, C. Viollet1, F. Marti1, B. Delord3, Ph. Faure1
ORCID iDs: C. Solié, A. Nicolson, B. Morin, L. M. Reynolds, T. Le Borgne, A. Gulmez, B. Girard, A. Mourot, C. Viollet, B. Delord, Ph. Faure
  1. Brain Plasticity Laboratory, CNRS UMR 8249, ESPCI Paris, PSL Research University,Paris, France
  2. Human Genetics and Cognitive Functions, CNRS UMR UMR3571, Pasteur Institute,Paris, France
  3. Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université, CNRS,Paris, France
  4. Institut de Génomique Fonctionnelle, University of Montpellier, UMR 5203 CNRS, U 1191 Inserm,Montpellier, France
Institutions: Université Paris Sciences et Lettres (France); Institut Pasteur (France); Sorbonne Université (France); Université de Montpellier (France)
Journal: Nature, volume 654, issue 8117, pages 163-172
Dates: received 19 February 2025; accepted 19 February 2026; published online 1 April 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41586-026-10301-4 · PMID 41922757 · PMCID PMC13233320 · OpenAlex W7147373508
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), mouse (organism), computational (subfield)
Methods: Statistics, Preprocessing, Single-unit activity, calcium imaging
Keywords: Social behaviour, Reward, Dynamical systems, Learning algorithms, Neural circuits
MeSH: Behavior, Animal*, Dopamine*, Social Behavior*, Ventral Tegmental Area*, Animals, Competitive Behavior, Cooperative Behavior, Dopaminergic Neurons, Female, Male, Mice, Reinforcement Machine Learning, Sex Characteristics (* major topic)
Topic: Neuroendocrine regulation and behavior (Social Psychology, Psychology), according to OpenAlex
Citations: cited by 2 papers (Europe PMC); 61 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

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Zenodo 17121126

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 8 files
Software Heritage: not checked
Found in: “Code availability”
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)
10 files
At the source:

Code availability statement

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  • it points to the authors' code: Zenodo 17121126

Read it in the paper: doi.org/10.1038/s41586-026-10301-4.

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Data

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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 17121126

Read it in the paper: doi.org/10.1038/s41586-026-10301-4.

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 24 authors, 5 keywords, 13 MeSH terms, 58 references.

Cite

This paper

Solié, C., Nicolson, A., Justo, R., Layadi, Y., Morin, B., Batifol, C., Reynolds, L. M., Le Borgne, T., Fayad, S. L., Gulmez, A., Rodriguez Quevedo, Y., Allegret-Vautrot, J., Centene Guglielmi, G., de Chaumont, F., Didienne, S., Debray, N., Hardelin, J.-P., Girard, B., Mourot, A., . . . Faure, P. (2026). Dopaminergic mechanisms of dynamical social specialization. Nature, 654(8117), 163-172. https://doi.org/10.1038/s41586-026-10301-4

BibTeX

@article{solie2026dopaminergic,
author = {Solié, C. and Nicolson, A. and Justo, R. and Layadi, Y. and Morin, B. and Batifol, C. and Reynolds, L. M. and Le Borgne, T. and Fayad, S. L. and Gulmez, A. and Rodriguez Quevedo, Y. and Allegret-Vautrot, J. and Centene Guglielmi, G. and de Chaumont, F. and Didienne, S. and Debray, N. and Hardelin, J.-P. and Girard, B. and Mourot, A. and Naudé, J. and Viollet, C. and Marti, F. and Delord, B. and Faure, Ph.},
title = {{Dopaminergic mechanisms of dynamical social specialization}},
journal = {Nature},
year = {2026},
month = apr,
volume = {654},
number = {8117},
pages = {163--172},
publisher = {Nature Portfolio},
issn = {0028-0836},
doi = {10.1038/s41586-026-10301-4},
url = {https://doi.org/10.1038/s41586-026-10301-4},
pmid = {41922757},
pmcid = {PMC13233320}
}

RIS

TY - JOUR
AU - Solié, C.
AU - Nicolson, A.
AU - Justo, R.
AU - Layadi, Y.
AU - Morin, B.
AU - Batifol, C.
AU - Reynolds, L. M.
AU - Le Borgne, T.
AU - Fayad, S. L.
AU - Gulmez, A.
AU - Rodriguez Quevedo, Y.
AU - Allegret-Vautrot, J.
AU - Centene Guglielmi, G.
AU - de Chaumont, F.
AU - Didienne, S.
AU - Debray, N.
AU - Hardelin, J.-P.
AU - Girard, B.
AU - Mourot, A.
AU - Naudé, J.
AU - Viollet, C.
AU - Marti, F.
AU - Delord, B.
AU - Faure, Ph.
TI - Dopaminergic mechanisms of dynamical social specialization
T2 - Nature
J2 - Nature
PY - 2026
DA - 2026/04/01
VL - 654
IS - 8117
SP - 163
EP - 172
SN - 0028-0836
PB - Nature Portfolio
DO - 10.1038/s41586-026-10301-4
UR - https://doi.org/10.1038/s41586-026-10301-4
LA - en
ER -

CSL-JSON

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