OSCR

Desegregation of neuronal predictive processing.

Code ↔ Paper

The paper beside its authors' code: matches between them have not been computed for this paper yet.

Paper

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

The paper is loaded when this pane is shown.

The authors' code

MATLAB · 233 lines · 9.1 KB · no license

  1. (* ::Package:: *)
  3. IneuronRate[lambda_,mu_,p_,size_]:=Module[{theta,b,deltax,deltay,idxdiag,e,mulnormal,w,v,iemis,iemisy,iefull,rmis,rmisy,rfull,s,sign,stimVec,J0,J0p,side,chiral,idp,a0,thre,a,hImis,hImisy,hIfull,kappa,interp},
  4. theta = 0;
  5. b=150;
  6. deltax[b_,x_,y_]:=((1+b/2) x-b/2 Sqrt[mu] y)/(1+b+(1-mu) b^2/4);
  7. deltay[b_,x_,y_]:=((1+b/2) y-b/2 Sqrt[mu] x)/(1+b+(1-mu) b^2/4);
  8. idxdiag = Table[{i,i},{i,1,size}];
  9. e =Table[1,{i,1,size}];
  10. (*Calculate the weight*)
  11. mulnormal = RandomVariate[\[ScriptD]=MultinormalDistribution[{3,3},{{1,Sqrt[mu]},{Sqrt[mu],1}}],{p,size}];
  12. w = Ramp[mulnormal[[;;,;;,1]]];
  13. v = Ramp[mulnormal[[;;,;;,2]]];
  14. (*Calculate E neurons' response*)
  15. iemis=(w[[1]]-Mean[w[[1]]])*deltax[b,1,0]+(v[[1]]-Mean[v[[1]]])*deltay[b,1,0];
  16. iemisy=(w[[1]]-Mean[w[[1]]])*deltax[b,0,1]+(v[[1]]-Mean[v[[1]]])*deltay[b,0,1];
  17. iefull=(w[[1]]-Mean[w[[1]]])*deltax[b,1,1]+(v[[1]]-Mean[v[[1]]])*deltay[b,1,1];
  18. rmis=b*Ramp[iemis-theta];
  19. rmisy=b*Ramp[iemisy-theta];
  20. rfull=b*Ramp[iefull-theta];
  21. s=(rmis-Mean[rmis])*(rfull-Mean[rfull]);
  23. (*Calculate stimulus input vector and put in the matrix*)
  24. stimVec = Join[Table[w[[i]],{i,1,p}],Table[v[[i]],{i,1,p}]];
  25. J0 = IdentityMatrix[size];
  26. side=stimVec[[1;;2 p,2p+1;;]];
  27. J0p=stimVec[[1;;2 p,1;;2 p]];
  28. (*The order*)
  29. chiral = Det[J0p];
  30. idp=IdentityMatrix[2 p];
  31. If[chiral<= 0,
  32. idp=ReplacePart[idp,{1->idp[[2]],2->idp[[1]]}]
  33. ];
  35. (*Diagonal elements*);
  36. thre=0.8-Log[0.5]/4;
  37. a0=HeavisideTheta[-s](1.4+12Exp[1.5s])+0.2HeavisideTheta[s](0.01+HeavisideTheta[s-thre]);
  38. kappa =lambda;
  39. a =kappa*a0+(1-kappa)*e;
  40. interp=kappa*J0p/size+(1-kappa)*idp;
  41. J0=ReplacePart[J0,{i_,i_}:>a[[i]]];
  42. J0[[1;;2 p,1;;2 p]] = interp;
  43. J0[[1;;2 p,2p+1;;]] = kappa*side/size;
  44. hImis = J0 . rmis;
  45. hImisy = J0 . rmisy;
  46. hIfull = J0 . rfull;
  47. {hImis,hImisy,hIfull}
  48. ]
  51. IneuronJie[lambda_,mu_,p_,size_]:=Module[{theta,b,deltax,deltay,idxdiag,e,mulnormal,w,v,Jout,iemisx,iemisy,iefull,rmisx,rmisy,rfull,s,sign,stimVec,J0p,Jie,Jei,side,chiral,idp,a0,thre,a,hImisx,hImisy,hIfull,alambda,kappa,interp},
  52. theta = 0;
  53. b=150;
  54. deltax[b_,x_,y_]:=((1+b/2) x-b/2 Sqrt[mu] y)/(1+b+(1-mu) b^2/4);
  55. deltay[b_,x_,y_]:=((1+b/2) y-b/2 Sqrt[mu] x)/(1+b+(1-mu) b^2/4);
  56. idxdiag = Table[{i,i},{i,1,size}];
  57. e =Table[1,{i,1,size}];
  58. (*Calculate the weight*)
  59. mulnormal = RandomVariate[\[ScriptD]=MultinormalDistribution[{3,3},{{1,Sqrt[mu]},{Sqrt[mu],1}}],{p,size}];
  60. w = Ramp[mulnormal[[;;,;;,1]]];
  61. v = Ramp[mulnormal[[;;,;;,2]]];
  62. Jout = (w\[Transpose] . w+v\[Transpose] . v)/size;
  63. (*Calculate E neurons' response*)
  64. iemisx=(w[[1]]-Mean[w[[1]]])*deltax[b,1,0]+(v[[1]]-Mean[v[[1]]])*deltay[b,1,0];
  65. iemisy=(w[[1]]-Mean[w[[1]]])*deltax[b,0,1]+(v[[1]]-Mean[v[[1]]])*deltay[b,0,1];
  66. iefull=(w[[1]]-Mean[w[[1]]])*deltax[b,1,1]+(v[[1]]-Mean[v[[1]]])*deltay[b,1,1];
  67. rmisx=b*Ramp[iemisx-theta];
  68. rmisy=b*Ramp[iemisy-theta];
  69. rfull=b*Ramp[iefull-theta];
  70. s=(rmisx-Mean[rmisx])*(rfull-Mean[rfull]);
  72. (*Calculate stimulus input vector and put in the matrix*)
  73. stimVec = Join[Table[w[[i]],{i,1,p}],Table[v[[i]],{i,1,p}]];
  74. side=stimVec[[1;;2 p,2p+1;;]];
  75. J0p=stimVec[[1;;2 p,1;;2 p]];
  76. (*The order*)
  77. chiral = Det[J0p];
  78. idp=IdentityMatrix[2 p];
  79. If[chiral<= 0,
  80. idp=ReplacePart[idp,{1->idp[[2]],2->idp[[1]]}]
  81. ];
  83. (*Diagonal elements*);
  84. thre=0.8-Log[0.5]/4;
  85. a0=HeavisideTheta[-s](1.4+12Exp[1.5s])+0.2HeavisideTheta[s](0.01+HeavisideTheta[s-thre]);
  86. Jie = IdentityMatrix[size];
  87. Jei = ConstantArray[0,{size,size}];
  88. Jei[[All,1;;2p]]=stimVec\[Transpose];
  90. kappa =lambda;
  91. a =kappa*a0+(1-kappa)*e;
  92. interp=kappa*J0p/size+(1-kappa)*idp;
  93. Jie=ReplacePart[Jie,{i_,i_}:>a[[i]]];
  94. Jie[[1;;2 p,1;;2 p]]=interp;
  95. Jie[[1;;2 p,2p+1;;]] = kappa*side/size;
  96. hImisx =Jie . rmisx;
  97. hImisy =Jie . rmisy;
  98. hIfull =Jie . rfull;
  99. alambda = DiagonalMatrix[(1-kappa)/a];
  100. Jei[[All,2p+1;;]]=Jout[[All,2p+1;;]] . alambda[[2p+1;;,2p+1;;]];
  101. {{iemisx,iemisy,iefull,hImisx,hImisy,hIfull},Jie,Jei}
  102. ]
  105. EImeanRate[mu_,p_,size_]:=Module[{theta,b,deltax,deltay,e,mulnormal,w,v,iemis,iemisy,iefull,rmis,rmisy,rfull,Jee,Ji,Jfull},
  106. theta = 0;
  107. b=150;
  108. deltax[b_,x_,y_]:=((1+b/2) x-b/2 Sqrt[mu] y)/(1+b+(1-mu) b^2/4);
  109. deltay[b_,x_,y_]:=((1+b/2) y-b/2 Sqrt[mu] x)/(1+b+(1-mu) b^2/4);
  110. e =Table[1,{i,1,size}];
  111. (*Calculate the weight*)
  112. mulnormal = RandomVariate[\[ScriptD]=MultinormalDistribution[{3,3},{{1,Sqrt[mu]},{Sqrt[mu],1}}],{p,size}];
  113. w = Ramp[mulnormal[[;;,;;,1]]];
  114. v = Ramp[mulnormal[[;;,;;,2]]];
  115. (*Calculate E neurons' response*)
  116. iemis=(w[[1]]-Mean[w[[1]]])*deltax[b,1,0]+(v[[1]]-Mean[v[[1]]])*deltay[b,1,0];
  117. iemisy=(w[[1]]-Mean[w[[1]]])*deltax[b,0,1]+(v[[1]]-Mean[v[[1]]])*deltay[b,0,1];
  118. iefull=(w[[1]]-Mean[w[[1]]])*deltax[b,1,1]+(v[[1]]-Mean[v[[1]]])*deltay[b,1,1];
  119. rmis=b*Ramp[iemis-theta];
  120. rmisy=b*Ramp[iemisy-theta];
  121. rfull=b*Ramp[iefull-theta];
  122. Jee=((w\[Transpose] . w+v\[Transpose] . v)+Mean[w[[1]]] (w\[Transpose] . {e}+{e}\[Transpose] . w)+Mean[v[[1]]](v\[Transpose] . {e}+{e}\[Transpose] . v))/size;
  123. Ji=(2(w\[Transpose] . w+v\[Transpose] . v)+Mean[w[[1]]]^2+Mean[v[[1]]]^2)/size;
  124. {Jee . rmis,Jee . rmisy,Jee . rfull,Ji . rmis,Ji . rmisy,Ji . rfull,iemis,iemisy,iefull}
  125. ]
  128. IneuronJieEvolve1[lambda_,mu_,p_,size_,x0_,y0_,z0_]:=Module[{theta,b,deltax,deltay,idxdiag,e,mulnormal,w,v,wv,Jout,iemisx,iemisy,iefull,rmisx,rmisy,rfull,s,sign,stimVec,J0p,Jie,Jei,side,chiral,idp,a0,thre,a,hImisx,hImisy,hIfull,alambda,kappa,interp},
  129. theta = 0;
  130. b=150;
  131. deltax[b_,x_,y_]:=((1+b/2) x-b/2 Sqrt[mu] y)/(1+b+(1-mu) b^2/4);
  132. deltay[b_,x_,y_]:=((1+b/2) y-b/2 Sqrt[mu] x)/(1+b+(1-mu) b^2/4);
  133. idxdiag = Table[{i,i},{i,1,size}];
  134. e =Table[1,{i,1,size}];
  135. (*Calculate the weight*)
  136. wv=Table[{3+Sqrt[Sqrt[mu]]x0[[i]]+Sqrt[1-Sqrt[mu]]y0[[i]],3+Sqrt[Sqrt[mu]]x0[[i]]+Sqrt[1-Sqrt[mu]]z0[[i]]},{i,1,Length[x0]}];
  137. w = {Ramp[wv[[All,1]]]};
  138. v = {Ramp[wv[[All,2]]]};
  139. Jout = (w\[Transpose] . w+v\[Transpose] . v)/size;
  140. (*Calculate E neurons' response*)
  141. iemisx=(w[[1]]-Mean[w[[1]]])*deltax[b,1,0]+(v[[1]]-Mean[v[[1]]])*deltay[b,1,0];
  142. iemisy=(w[[1]]-Mean[w[[1]]])*deltax[b,0,1]+(v[[1]]-Mean[v[[1]]])*deltay[b,0,1];
  143. iefull=(w[[1]]-Mean[w[[1]]])*deltax[b,1,1]+(v[[1]]-Mean[v[[1]]])*deltay[b,1,1];
  144. rmisx=b*Ramp[iemisx-theta];
  145. rmisy=b*Ramp[iemisy-theta];
  146. rfull=b*Ramp[iefull-theta];
  147. s=(rmisx-Mean[rmisx])*(rfull-Mean[rfull]);
  149. (*Calculate stimulus input vector and put in the matrix*)
  150. stimVec = Join[Table[w[[i]],{i,1,p}],Table[v[[i]],{i,1,p}]];
  151. side=stimVec[[1;;2 p,2p+1;;]];
  152. J0p=stimVec[[1;;2 p,1;;2 p]];
  153. (*The order*)
  154. chiral = Det[J0p];
  155. idp=IdentityMatrix[2 p];
  156. If[chiral<= 0,
  157. idp=ReplacePart[idp,{1->idp[[2]],2->idp[[1]]}]
  158. ];
  160. (*Diagonal elements*);
  161. thre=0.8-Log[0.5]/4;
  162. a0=HeavisideTheta[-s](1.4+12Exp[1.5s])+0.2HeavisideTheta[s](0.01+HeavisideTheta[s-thre]);
  163. Jie = IdentityMatrix[size];
  164. Jei = ConstantArray[0,{size,size}];
  165. Jei[[All,1;;2p]]=stimVec\[Transpose];
  167. kappa =lambda;
  168. a =kappa*a0+(1-kappa)*e;
  169. interp=kappa*J0p/size+(1-kappa)*idp;
  170. Jie=ReplacePart[Jie,{i_,i_}:>a[[i]]];
  171. Jie[[1;;2 p,1;;2 p]]=interp;
  172. Jie[[1;;2 p,2p+1;;]] = kappa*side/size;
  173. hImisx =Jie . rmisx;
  174. hImisy =Jie . rmisy;
  175. hIfull =Jie . rfull;
  176. alambda = DiagonalMatrix[(1-kappa)/a];
  177. Jei[[All,2p+1;;]]=Jout[[All,2p+1;;]] . alambda[[2p+1;;,2p+1;;]];
  178. {{iemisx,iemisy,iefull,hImisx,hImisy,hIfull},Jie,Jei}
  179. ]
  182. IneuronJieEvolve2[lambda_,mu_,p_,size_,x0_,y0_]:=Module[{theta,b,deltax,deltay,idxdiag,e,mulnormal,w,v,wv,Jout,iemisx,iemisy,iefull,rmisx,rmisy,rfull,s,sign,stimVec,J0p,Jie,Jei,side,chiral,idp,a0,thre,a,hImisx,hImisy,hIfull,alambda,kappa,interp},
  183. theta = 0;
  184. b=150;
  185. deltax[b_,x_,y_]:=((1+b/2) x-b/2 Sqrt[mu] y)/(1+b+(1-mu) b^2/4);
  186. deltay[b_,x_,y_]:=((1+b/2) y-b/2 Sqrt[mu] x)/(1+b+(1-mu) b^2/4);
  187. idxdiag = Table[{i,i},{i,1,size}];
  188. e =Table[1,{i,1,size}];
  189. (*Calculate the weight*)
  190. wv=Table[{3+x0[[i]],3+Sqrt[mu]x0[[i]]+Sqrt[1-mu]y0[[i]]},{i,1,Length[x0]}];
  191. w = {Ramp[wv[[All,1]]]};
  192. v = {Ramp[wv[[All,2]]]};
  193. Jout = (w\[Transpose] . w+v\[Transpose] . v)/size;
  194. (*Calculate E neurons' response*)
  195. iemisx=(w[[1]]-Mean[w[[1]]])*deltax[b,1,0]+(v[[1]]-Mean[v[[1]]])*deltay[b,1,0];
  196. iemisy=(w[[1]]-Mean[w[[1]]])*deltax[b,0,1]+(v[[1]]-Mean[v[[1]]])*deltay[b,0,1];
  197. iefull=(w[[1]]-Mean[w[[1]]])*deltax[b,1,1]+(v[[1]]-Mean[v[[1]]])*deltay[b,1,1];
  198. rmisx=b*Ramp[iemisx-theta];
  199. rmisy=b*Ramp[iemisy-theta];
  200. rfull=b*Ramp[iefull-theta];
  201. s=(rmisx-Mean[rmisx])*(rfull-Mean[rfull]);
  203. (*Calculate stimulus input vector and put in the matrix*)
  204. stimVec = Join[Table[w[[i]],{i,1,p}],Table[v[[i]],{i,1,p}]];
  205. side=stimVec[[1;;2 p,2p+1;;]];
  206. J0p=stimVec[[1;;2 p,1;;2 p]];
  207. (*The order*)
  208. chiral = Det[J0p];
  209. idp=IdentityMatrix[2 p];
  210. If[chiral<= 0,
  211. idp=ReplacePart[idp,{1->idp[[2]],2->idp[[1]]}]
  212. ];
  214. (*Diagonal elements*);
  215. thre=0.8-Log[0.5]/4;
  216. a0=HeavisideTheta[-s](1.4+12Exp[1.5s])+0.2HeavisideTheta[s](0.01+HeavisideTheta[s-thre]);
  217. Jie = IdentityMatrix[size];
  218. Jei = ConstantArray[0,{size,size}];
  219. Jei[[All,1;;2p]]=stimVec\[Transpose];
  221. kappa =lambda;
  222. a =kappa*a0+(1-kappa)*e;
  223. interp=kappa*J0p/size+(1-kappa)*idp;
  224. Jie=ReplacePart[Jie,{i_,i_}:>a[[i]]];
  225. Jie[[1;;2 p,1;;2 p]]=interp;
  226. Jie[[1;;2 p,2p+1;;]] = kappa*side/size;
  227. hImisx =Jie . rmisx;
  228. hImisy =Jie . rmisy;
  229. hIfull =Jie . rfull;
  230. alambda = DiagonalMatrix[(1-kappa)/a];
  231. Jei[[All,2p+1;;]]=Jout[[All,2p+1;;]] . alambda[[2p+1;;,2p+1;;]];
  232. {{iemisx,iemisy,iefull,hImisx,hImisy,hIfull},Jie,Jei}
  233. ]

IneuronRep.m at commit 01ccaa7, no license · at the source

Overview

Authors: Bin Wang1,2, Nicholas J Audette3, David M Schneider3, Johnatan Aljadeff4
ORCID iDs: Bin Wang, Johnatan Aljadeff
  1. Department of Physics, University of California San Diego, La Jolla, CA USA
  2. Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Kavli Institute for Brain Science, Columbia University, NY New York, USA
  3. Center for Neural Science, New York University, New York, NY USA
  4. Department of Neurobiology, University of California San Diego, La Jolla, CA USA
Institutions: University of California San Diego (United States); Mortimer B. Zuckerman Mind Brain Behavior Institute (United States); Columbia University (United States); New York University (United States)
Journal: Nature communications, volume 17, issue 1, article 3919
Dates: received 25 June 2025; accepted 24 February 2026; published online 13 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-70347-w · PMID 41826318 · PMCID PMC13129095 · OpenAlex W7135245668
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), none (in silico) (organism), systems (subfield)
Methods: Statistics, Connectivity
Keywords: Network models, Neural encoding, Sensorimotor processing, Cortex, Complex networks
MeSH: Brain*, Models, Neurological*, Nerve Net*, Neurons*, Animals, Recurrent Neural Networks (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: U.S. Department of Health & Human Services | National Institutes of Health (NIH) (1R01-DC018802, K99-DC020770); NINDS NIH HHS (R01 NS135853); NSF | Directorate for Mathematical & Physical Sciences | Division of Mathematical Sciences (DMS) (DMS-1929284); NSF | Directorate for Mathematical & Physical Sciences | Division of Physics (PHY) (PHY-1748958); United States Department of Defense | Defense Advanced Research Projects Agency (DARPA) (D21AP10162-00); NSF | BIO | Division of Biological Infrastructure (DBI) (DBI-2229929); U.S. Department of Energy (DOE) (DE-SC002204); Gordon and Betty Moore Foundation (Gordon E. and Betty I. Moore Foundation) (2919.02)
Citations: not cited yet (Europe PMC); 87 references in the paper

Abstract

Neural circuits construct internal ‘world-models’ to guide behavior. The predictive processing framework posits that neural activity signaling sensory predictions and concurrently computing prediction-errors is a signature of those internal models. To understand how the brain generates predictions for complex sensorimotor signals, we investigate the emergence of high-dimensional, multi-modal predictive representations in recurrent networks. Contrary to previous proposals of functionally specialized cell-types, stimulus and prediction-error representations are desegregated in networks performing robust predictive processing. We confirmed these model predictions by using a rich stimulus-set to violate animals’ learned expectations. We propose that predictive processing is optimal when excitation/inhibition balance is loose, and reveal distinct functional roles of excitatory and inhibitory neurons. Together, we demonstrate that neural representations of internal models are highly distributed, yet structured to support flexible readout of behaviorally-relevant information. Our results advance the understanding of how internal models are computed, by incorporating different computations into a unifying model.

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

Repository

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

BinW3233/HDPC_code

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 01ccaa7e2e2de6128ad7693cc0e1fc8262ff37bf, 9 May 2026
Languages: MATLAB (6), Mathematica (1)
Size: 8 files, 7 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (5 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
8 files
At the source: github.com/BinW3233/HDPC_code.git

Code availability

Computer code to reproduce model simulations is available in the Github repository: https://github.com/BinW3233/HDPC_code.git.

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 7 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 availability

No new experimental data was collected in this study. Source Data files are provided for all figures.

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 5 keywords, 6 MeSH terms, 8 funders, 79 references.

Cite

This paper

Wang, B., Audette, N. J., Schneider, D. M., & Aljadeff, J. (2026). Desegregation of neuronal predictive processing. Nature communications, 17(1), 3919. https://doi.org/10.1038/s41467-026-70347-w

BibTeX

@article{wang2026desegregation,
author = {Wang, Bin and Audette, Nicholas J and Schneider, David M and Aljadeff, Johnatan},
title = {{Desegregation of neuronal predictive processing}},
journal = {Nature communications},
year = {2026},
month = mar,
volume = {17},
number = {1},
pages = {3919},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-70347-w},
url = {https://doi.org/10.1038/s41467-026-70347-w},
pmid = {41826318},
pmcid = {PMC13129095}
}

RIS

TY - JOUR
AU - Wang, Bin
AU - Audette, Nicholas J
AU - Schneider, David M
AU - Aljadeff, Johnatan
TI - Desegregation of neuronal predictive processing
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/03/13
VL - 17
IS - 1
SP - 3919
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-70347-w
UR - https://doi.org/10.1038/s41467-026-70347-w
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-70347-w",
"type": "article-journal",
"title": "Desegregation of neuronal predictive processing",
"container-title": "Nature communications",
"author": [
{
"family": "Wang",
"given": "Bin"
},
{
"family": "Audette",
"given": "Nicholas J"
},
{
"family": "Schneider",
"given": "David M"
},
{
"family": "Aljadeff",
"given": "Johnatan"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "3919",
"DOI": "10.1038/s41467-026-70347-w",
"PMID": "41826318",
"PMCID": "PMC13129095",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-70347-w",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
13
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1371/journal.pcbi.1014164 [code]
'Backpropagation and the brain' realized in cortical error neuron microcircuits.
Journal: PLoS computational biology
In common: computational modeling (no new data), 17 references
[2] doi:10.1038/s41467-026-70354-x [code]
Global error signal guides local optimization in mismatch calculation.
Journal: Nature communications
In common: Statistics and Machine Learning Toolbox, computational modeling (no new data), 16 references
[3] doi:10.7554/elife.108941 [code]
Visuomotor mismatch EEG responses over occipital cortex of freely moving human subjects.
Journal: eLife
In common: 9 references
[4] doi:10.64898/2026.03.03.705098
Spiking and neuromodulation during active experience shape visuomotor integration in V1 layer 2/3 neurons
Journal: bioRxiv (preprint)
In common: 9 references
[5] doi:10.1371/journal.pone.0354021 [code]
A genetic algorithm for self-supervised models of oscillatory neurodynamics.
Journal: PloS one
In common: computational modeling (no new data), systems, 8 references
[6] doi:10.1126/sciadv.aed6417 [code]
Intrinsic timing, not temporal prediction, underlies ramping dynamics in visual and parietal cortex during passive behavior.
Journal: Science advances
In common: systems, 8 references
[7] doi:10.1126/sciadv.aef3715
A cortical output channel for perceptual categorization.
Journal: Science advances
In common: 7 references
[8] doi:10.1371/journal.pbio.3003915 [code]
Noise-invariant representations of sound emerge along the canonical cortical hierarchy.
Journal: PLoS biology
In common: Statistics and Machine Learning Toolbox, 6 references
[9] doi:10.1038/s42003-026-10319-4 [code]
Neurons in the bat auditory cortex encode class and complexity of future vocalizations.
Journal: Communications biology
In common: systems, 5 references
[10] doi:10.1523/jneurosci.0987-25.2026 [code]
Cell-Type-Specific Synaptic Scaling Mechanisms Differentially Contribute to Associative Learning.
Journal: The Journal of neuroscience : the official journal of the Society for Neuroscience
In common: 5 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.