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Neural competition between prefrontal and auditory cortex constrains novel sound strategy learning.

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The authors' code

MATLAB · 247 lines · 4.7 KB · CC-BY-4.0

  3. %%
  4. %Evaluate Model A--use cum all perf -independent 2 loops, with random noise
  5. %plot fit performance
  9. load mod2lp_control_result
  11. parm_results2=parm_results;
  12. parm_results2(:,2:4)=parm_results2(:,2:4)/10e7;
  15. rmsd_all=[];
  16. for qq=1:size(parm_results,1)
  17. qq=parm_results(qq,1);
  18. param=parm_results(qq,2:5);
  19. Disc=parm_results(qq,end);
  21. load model_chem_control.mat
  27. ResModData=modeldata{qq,1};
  28. side=ResModData(:,1);
  29. choice=ResModData(:,2);
  30. perf=ResModData(:,3);
  35. % weight
  37. delta1=param(1);
  38. spw=param(2);
  39. delta2=param(3);
  40. aw=0.001;
  41. side=ResModData(:,1);
  42. choice=ResModData(:,2);
  43. perf=ResModData(:,3);
  44. % resall=ResModData(:,4);
  46. [perf_s awhist spwhist choice_s]=model_act_2lp_rand(delta1,delta2,aw,spw,side);
  48. sidediff=diff(side);
  49. row=find(sidediff~=0);
  50. audperf=perf(row+1);
  51. audperf_s=perf_s(row+1);
  53. row=find(sidediff==0);
  54. ssperf=perf(row+1);
  55. ssperf_s=perf_s(row+1);
  57. figure;
  58. subplot(2,2,1)
  59. hold;
  60. plot(cumsum(perf_s),'r-','linewidth',3)
  61. plot(cumsum(perf),'b-','linewidth',3)
  62. sd=(cumsum(perf_s)-cumsum(perf)).^ 2 ;
  63. rmsd1= sqrt( sum(sd) / numel(sd) ) ;
  64. subplot(2,2,2)
  65. hold;
  66. plot(cumsum(audperf_s),'r-','linewidth',3)
  67. plot(cumsum(audperf),'b-','linewidth',3)
  68. sd=(cumsum(audperf_s)-cumsum(audperf)).^ 2 ;
  69. rmsd2= sqrt( sum(sd) / numel(sd) ) ;
  70. subplot(2,2,3)
  71. hold;
  72. plot(cumsum(ssperf_s),'r-','linewidth',3)
  73. plot(cumsum(ssperf),'b-','linewidth',3)
  74. sd=(cumsum(ssperf_s)-cumsum(ssperf)).^ 2 ;
  75. rmsd3= sqrt( sum(sd) / numel(sd) ) ;
  76. rmsd_all=[rmsd_all;[rmsd1 rmsd2 rmsd3]];
  78. end
  79. rmsd_all1=rmsd_all;
  82. %%
  86. %%
  87. %Evaluate Model B--only auditory weight, all perf
  88. %plot fit performance
  91. load mod_audonly_control_result
  93. parm_results2=parm_results;
  94. parm_results2(:,2:4)=parm_results2(:,2:4)/10e7;
  97. rmsd_all=[];
  98. for qq=1:size(parm_results,1)
  100. qq=parm_results(qq,1);
  101. param=parm_results(qq,2:5);
  102. Disc=parm_results(qq,end);
  104. load model_chem_control.mat
  110. ResModData=modeldata{qq,1};
  111. side=ResModData(:,1);
  112. choice=ResModData(:,2);
  113. perf=ResModData(:,3);
  115. delta1=param(1);
  116. aw=param(2);
  117. delta2=param(3);
  118. % aw=0.001;
  119. side=ResModData(:,1);
  120. choice=ResModData(:,2);
  121. perf=ResModData(:,3);
  122. % resall=ResModData(:,4);
  124. [perf_s awhist choice_s phist]=model_act_onlyaud(delta1,delta2,aw,side);
  126. sidediff=diff(side);
  127. row=find(sidediff~=0);
  128. audperf=perf(row+1);
  129. audperf_s=perf_s(row+1);
  131. row=find(sidediff==0);
  132. ssperf=perf(row+1);
  133. ssperf_s=perf_s(row+1);
  135. % figure
  136. % plot(perf_s)
  137. % figure
  138. % plot(phist)
  139. %
  142. figure;
  143. subplot(2,2,1)
  144. hold;
  145. plot(cumsum(perf_s),'r-','linewidth',3)
  146. plot(cumsum(perf),'b-','linewidth',3)
  147. sd=(cumsum(perf_s)-cumsum(perf)).^ 2 ;
  148. rmsd1= sqrt( sum(sd) / numel(sd) ) ;
  149. subplot(2,2,2)
  150. hold;
  151. plot(cumsum(audperf_s),'r-','linewidth',3)
  152. plot(cumsum(audperf),'b-','linewidth',3)
  153. sd=(cumsum(audperf_s)-cumsum(audperf)).^ 2 ;
  154. rmsd2= sqrt( sum(sd) / numel(sd) ) ;
  155. subplot(2,2,3)
  156. hold;
  157. plot(cumsum(ssperf_s),'r-','linewidth',3)
  158. plot(cumsum(ssperf),'b-','linewidth',3)
  159. sd=(cumsum(ssperf_s)-cumsum(ssperf)).^ 2 ;
  160. rmsd3= sqrt( sum(sd) / numel(sd) ) ;
  161. rmsd_all=[rmsd_all;[rmsd1 rmsd2 rmsd3]];
  162. end
  163. rmsd_all2=rmsd_all;
  165. %%
  168. %%
  170. %%
  171. %Evaluate Model C--only auditory weight, all perf,+ aversion to sound
  172. %plot fit performance
  176. load mod_audonly_aversion_control_result
  178. parm_results2=parm_results;
  179. parm_results2(:,2:4)=parm_results2(:,2:4)/10e7;
  182. rmsd_all=[];
  183. for qq=1:size(parm_results,1)
  184. qq=parm_results(qq,1);
  185. param=parm_results(qq,2:5);
  186. Disc=parm_results(qq,end);
  188. load model_chem_control.mat
  194. ResModData=modeldata{qq,1};
  195. side=ResModData(:,1);
  196. choice=ResModData(:,2);
  197. perf=ResModData(:,3);
  199. delta1=param(1);
  200. aw=param(2);
  201. delta2=param(3);
  202. aversion=param(4);
  203. % aw=0.001;
  204. side=ResModData(:,1);
  205. choice=ResModData(:,2);
  206. perf=ResModData(:,3);
  207. % resall=ResModData(:,4);
  209. [perf_s awhist choice_s]=model_act_onlyaud_aversion(delta1,delta2,aw,aversion,side);
  211. sidediff=diff(side);
  212. row=find(sidediff~=0);
  213. audperf=perf(row+1);
  214. audperf_s=perf_s(row+1);
  216. row=find(sidediff==0);
  217. ssperf=perf(row+1);
  218. ssperf_s=perf_s(row+1);
  220. figure;
  221. subplot(2,2,1)
  222. hold;
  223. plot(cumsum(perf_s),'r-','linewidth',3)
  224. plot(cumsum(perf),'b-','linewidth',3)
  225. sd=(cumsum(perf_s)-cumsum(perf)).^ 2 ;
  226. rmsd1= sqrt( sum(sd) / numel(sd) ) ;
  227. subplot(2,2,2)
  228. hold;
  229. plot(cumsum(audperf_s),'r-','linewidth',3)
  230. plot(cumsum(audperf),'b-','linewidth',3)
  231. sd=(cumsum(audperf_s)-cumsum(audperf)).^ 2 ;
  232. rmsd2= sqrt( sum(sd) / numel(sd) ) ;
  233. subplot(2,2,3)
  234. hold;
  235. plot(cumsum(ssperf_s),'r-','linewidth',3)
  236. plot(cumsum(ssperf),'b-','linewidth',3)
  237. sd=(cumsum(ssperf_s)-cumsum(ssperf)).^ 2 ;
  238. rmsd3= sqrt( sum(sd) / numel(sd) ) ;
  239. rmsd_all=[rmsd_all;[rmsd1 rmsd2 rmsd3]];
  242. end
  243. rmsd_all3=rmsd_all;
  247. % save('control_three_model_results')

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

Overview

Authors: Kai Lu1, Kelvin T Wong1, Chengcheng J Yang1, Lin N Zhou1, Yike T Shi1, Maya L Costello1, Robert C Liu1
ORCID iDs: Kai Lu, Kelvin T Wong, Chengcheng J Yang, Lin N Zhou, Yike T Shi, Maya L Costello, Robert C Liu
  1. Department of Biology, Emory University, Atlanta, GA 30322, USA
Institutions: Emory University (United States)
Journal: Science advances, volume 12, issue 32, article eaeb3005
Dates: received 7 August 2025; accepted 2 July 2026; published online 7 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1126/sciadv.aeb3005 · PMID 42566525 · PMCID PMC13450224 · OpenAlex W7201840508
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism)
Methods: Spectral & time-frequency, Connectivity, Statistics, Machine learning, Preprocessing, fMRI & imaging, Single-unit activity, calcium imaging
MeSH: Auditory Cortex*, Auditory Perception*, Learning*, Neurons*, Prefrontal Cortex*, Acoustic Stimulation, Animals, Male, Mice, Sound (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIMH NIH HHS (P50 MH100023); NIDCD NIH HHS (R01 DC008343)
Citations: not cited yet (Europe PMC); 84 references in the paper

Abstract

In nature, animals learn to replace predisposed behaviors with new strategies, yet the neural constraints on these transitions are unclear. Using an ethological search task in mice, we reveal medial prefrontal cortical (mPFC) neural correlates of a predisposed win-stay strategy that decays as animals learn to follow a more reliable auditory cue. Auditory cortex (ACx) activity predicts correct trial-by-trial sound-guided search, even on day one of training. This prognostic coding strengthens with learning and emerges from suppressed spiking, most pronounced in neurons tuned laterally to the cue’s spectrum. Chemogenetic disruption reveals ACx contributions to improving performance. Unexpectedly, the global silencing of mPFC accelerates successful usage of sound-tracking, contrary to its canonical role in flexible or stimulus-dependent behavior. Instead, a decentralized multiexpert competition model best predicts behavior and causal perturbations. These findings suggest that mPFC implements a default strategy based on prior knowledge, which actively hinders the expression of more efficient strategies.

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

Repositories

Its files are read in the Code ↔ Paper reader above.

Zenodo 18947789

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
24 files
At the source: doi.org/10.5281/zenodo.18947789

rcbliu/prefrontal-and-auditory-cortex-compete-in-learning

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 79eb28fdd6a7b5e969ef56d89c96e1326497bb62, 10 March 2026
Languages: MATLAB (23)
Size: 46 files, 23 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: Statistics and Machine Learning Toolbox (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
24 files
At the source: github.com/rcbliu/Prefrontal-and-auditory-cortex-compete-in-learning/tree/v1.1

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;
  • 46 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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.

Data, code, and materials availability

All data and code needed to evaluate and reproduce the conclusions in the paper are present in the paper and/or the Supplementary Materials. These have been deposited at https://doi.org/10.5281/zenodo.18947789. No new materials were generated in this study. The viruses used in this study (pAAV-CaMKIIa-hM4D(Gi)-mCherry and pAAV-CaMKIIa-EGFP) can be provided by Addgene pending scientific review and a completed material transfer agreement. Requests for these materials should be submitted through www.addgene.org/.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 10 MeSH terms, 2 funders, 72 references.

Cite

This paper

Lu, K., Wong, K. T., Yang, C. J., Zhou, L. N., Shi, Y. T., Costello, M. L., & Liu, R. C. (2026). Neural competition between prefrontal and auditory cortex constrains novel sound strategy learning. Science advances, 12(32), eaeb3005. https://doi.org/10.1126/sciadv.aeb3005

BibTeX

@article{lu2026neural,
author = {Lu, Kai and Wong, Kelvin T and Yang, Chengcheng J and Zhou, Lin N and Shi, Yike T and Costello, Maya L and Liu, Robert C},
title = {{Neural competition between prefrontal and auditory cortex constrains novel sound strategy learning}},
journal = {Science advances},
year = {2026},
month = aug,
volume = {12},
number = {32},
pages = {eaeb3005},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aeb3005},
url = {https://doi.org/10.1126/sciadv.aeb3005},
pmid = {42566525},
pmcid = {PMC13450224}
}

RIS

TY - JOUR
AU - Lu, Kai
AU - Wong, Kelvin T
AU - Yang, Chengcheng J
AU - Zhou, Lin N
AU - Shi, Yike T
AU - Costello, Maya L
AU - Liu, Robert C
TI - Neural competition between prefrontal and auditory cortex constrains novel sound strategy learning
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/08/07
VL - 12
IS - 32
SP - eaeb3005
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aeb3005
UR - https://doi.org/10.1126/sciadv.aeb3005
LA - en
ER -

CSL-JSON

{
"id": "10.1126/sciadv.aeb3005",
"type": "article-journal",
"title": "Neural competition between prefrontal and auditory cortex constrains novel sound strategy learning",
"container-title": "Science advances",
"author": [
{
"family": "Lu",
"given": "Kai"
},
{
"family": "Wong",
"given": "Kelvin T"
},
{
"family": "Yang",
"given": "Chengcheng J"
},
{
"family": "Zhou",
"given": "Lin N"
},
{
"family": "Shi",
"given": "Yike T"
},
{
"family": "Costello",
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},
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"given": "Robert C"
}
],
"container-title-short": "Sci Adv",
"volume": "12",
"issue": "32",
"page": "eaeb3005",
"DOI": "10.1126/sciadv.aeb3005",
"PMID": "42566525",
"PMCID": "PMC13450224",
"ISSN": "2375-2548",
"publisher": "American Association for the Advancement of Science",
"URL": "https://doi.org/10.1126/sciadv.aeb3005",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
7
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]
}
}

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