OSCR

Neurons embedded in loop-like motifs act as central hubs for brain-wide integration.

Code ↔ Paper

5 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 5 matches
  1. [1] § Materials and methods › Cell assembly detection ↔ CADopti.m, lines 1–46 · score 0.91 · temporal resolution, MaxLags, assembly detected, CADopti, spike trains, cell assemblies
  2. [2] § Materials and methods › Cell assembly detection ↔ Tutorial_CADopti.m, lines 12–33 · score 0.81 · temporal resolution, MaxLags, CADopti, BinSizes, stationarities, detection
  3. [3] § Materials and methods › Cell assembly detection ↔ CADopti.m, lines 1–46 · score 0.71 · temporal resolution, CADopti, cell assemblies, Assembly detection, algorithm, Russo
  4. [4] § Materials and methods › Cell assembly detection ↔ Tutorial_CADopti.m, lines 12–33 · score 0.63 · temporal resolution, CADopti, Assembly detection, algorithm
  5. [5] § Materials and methods › Cell assembly detection ↔ TestPair_ref.m, lines 1–88 · score 0.56 · spike trains, formed assembly, stationarities, parallel, Russo, binning

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 · 290 lines · 11 KB · GPL-3.0 · 2 matches

  1. function [As_across_bins,As_across_bins_index,assembly,Assemblies_all_orders]=CADopti(spM,MaxLags,BinSizes,ref_lag,alph,No_th,O_th,bytelimit)
  2. % this function returns cell assemblies detected in spM spike matrix binned
  3. % at a temporal resolution specified in 'BinSizes' vector and testing for all
  4. % lags between '-MaxLags(i)' and 'MaxLags(i)'
  5. %
  6. % USAGE: [assembly]=Main_assemblies_detection(spM, MaxLags, BinSizes, ref_lag, alph, Dc, No_th, O_th, bytelimit)
  7. %
  8. % ARGUMENTS:
  9. % spM := matrix with population spike trains; each row is the spike train (time stamps, not binned) relative to a unit.
  10. % BinSizes:= vector of bin sizes to be tested;
  11. % MaxLags:= vector of maximal lags to be tested. For a binning dimension of BinSizes(i) the program will test all pairs configurations with a time shift between -MaxLags(i) and MaxLags(i);
  12. % (optional) ref_lag := reference lag. Default value 2
  13. % (optional) alph := alpha level. Default value 0.05
  14. % (optional) No_th := minimal number of occurrences required for an assembly (all assemblies, even if significant, with fewer occurrences than No_th are discarded). Default value 0.
  15. % (optional) O_th := maximal assembly order (the algorithm will return assemblies of composed by maximum O_th elements).
  16. % (optional) bytelimit := maximal size (in bytes) allocated for all assembly structures detected with a bin dimension. When the size limit is reached the algorithm stops adding new units.
  17. %
  18. % RETURNS:
  19. % assembly - structure containing assembly information:
  20. % assembly.parameters - parameters used to run Main_assemblies_detection
  21. % assembly.bin{i} contains information about assemblies detected with
  22. % 'BinSizes(i)' bin size tested for all lags between
  23. % '-MaxLags(i)' and 'MaxLags(i)'
  24. %
  25. % assembly.bin{i}.bin_edges - bin edges (common to all assemblies in assembly.bin{i})
  26. % assembly.bin{i}.n{j} information about the j-th assembly detected with BinSizes(i) bin size
  27. % elements: vector of units taking part to the assembly (unit order correspond to the agglomeration order)
  28. % lag: vector of time lags. '.lag(z)' is the activation delay between .elements(1) and .elements(z+1)
  29. % pr: vector of pvalues. '.pr(z)' is the pvalue of the statistical test between performed adding .elements(z+1) to the structure .elements(1:z)
  30. % Time: assembly activation time. It reports how many times the complete assembly activates in that bin. .Time always refers to the activation of the first listed assembly element (.elements(1)), that doesn't necessarily corresponds to the first unit firing.
  31. % Noccurrences: number of assembly occurrence. '.Noccurrences(z)' is the occurrence number of the structure composed by the units .elements(1:z+1)
  32. %
  33. % As_across_bins - structure containing assembly information (exactly same information contained in "assembly" but collected across different temporal resolutions)
  34. % As_across_bins_index - information to link assemblies in "As_across_bins"
  35. % back to the structure "assembly":
  36. % assembly As_across_bins{i} is contained in assembly.bin{As_across_bins_index{i}(1)}.n{As_across_bins_index{i}(2)}.
  37. %
  38. % © 2020 Russo
  39. % for information please contact [email hidden]
  42. if nargin<4 || isempty(ref_lag), ref_lag=2; end
  43. if nargin<5 || isempty(alph), alph=0.05; end
  44. if nargin<6 || isempty(No_th), No_th=0; end % no limitation on the number of assembly occurrences
  45. if nargin<7 || isempty(O_th), O_th=Inf; end % no limitation on the assembly order (=number of elements in the assembly)
  46. if nargin<8 || isempty(bytelimit), bytelimit=Inf; end % no limitation on assembly dimension
  48. %%
  50. nneu=size(spM,1); % number of units
  51. testit=ones(1,length(BinSizes));
  52. binM=cell(1,length(BinSizes));
  53. number_tests=0;
  54. % matrix binning at all bins
  55. for gg=1:length(BinSizes)
  56. int=BinSizes(gg);
  57. tb=min(spM(:)):int:max(spM(:));
  59. binM{gg}=zeros(nneu,length(tb)-1,'uint8');
  60. number_tests=number_tests+nneu*(nneu-1)*(2*MaxLags(gg)+1)/2;
  61. for n=1:nneu
  62. [ binM{gg}(n,:),~] = histcounts(spM(n,:),tb);
  63. end
  64. assembly.bin{gg}.n=[];
  65. assembly.bin{gg}.bin_edges=tb;
  66. if size(binM{gg},2)-MaxLags(gg)<100
  67. fprintf('Warning: testing bin size=%f. The time series is too short, consider taking a longer portion of spike train or diminish the bin size to be tested \n', int);
  68. testit(gg)=0;
  69. end
  70. end
  72. fprintf('order 1\n')
  73. clear Assemblies_all_orders
  74. O=1;
  75. Dc=100; %length (in # bins) of the segments in which the spike train is divided to compute #abba variance (parameter k).
  78. assembly_selected_xy=[];
  79. % assembly_selected_xy=nan(nneu,nneu);
  80. p_values=[];
  81. % first order assembly
  82. parfor w1=1:nneu
  83. for w2=w1+1:nneu
  84. assemblybin=cell(1,length(BinSizes));
  85. p_by_bin=[];
  86. for gg=1:length(BinSizes)
  87. [assemblybin{gg}]=FindAssemblies_recursive_prepruned([binM{gg}(w1,:);binM{gg}(w2,:)],w1,w2,MaxLags(gg),Dc,ref_lag);
  88. p_values=[p_values; assemblybin{gg}.pr(end)];
  89. assemblybin{gg}.bin=BinSizes(gg);
  90. p_by_bin(gg)=assemblybin{gg}.pr;
  91. end
  92. [~, b]=min(p_by_bin);
  93. assembly_selected_xy=[assembly_selected_xy,assemblybin(b)];
  94. end
  95. end
  96. assembly_selected=assembly_selected_xy;
  98. if ~isempty(assembly_selected)
  100. %% Holm-Bonferroni
  101. x=1:length(p_values);
  102. p_values=sort(p_values);
  103. p_values_alpha=alph./(number_tests+1-x);
  104. aus=find((p_values'-p_values_alpha)<0);
  105. if isempty(aus)
  106. HBcorrected_p=0;
  107. else
  108. HBcorrected_p=p_values(aus(end));
  109. end
  110. ANfo=zeros(nneu,nneu);
  112. for oo=length(assembly_selected):-1:1
  113. if assembly_selected{oo}.pr(end)>HBcorrected_p
  114. assembly_selected(oo)=[];
  115. else
  116. ANfo(assembly_selected{oo}.elements(1),assembly_selected{oo}.elements(2))=1;
  117. end
  118. end
  119. Assemblies_all_orders{O}=assembly_selected;
  121. %%
  122. % higher orders
  123. Oincrement=1;
  124. while Oincrement && O<(O_th-1)
  125. O=O+1;
  126. fprintf('order %d\n',O)
  127. Oincrement=0;
  128. assembly_selected_aus=[];
  129. xx=1;
  130. for w1=1:size(assembly_selected,2)
  132. % bin at which to test w1
  133. ggg=find(BinSizes==assembly_selected{w1}.bin);
  135. % element to test with w1
  136. w1_elements=assembly_selected{w1}.elements;
  137. [~, w2_to_test]=find(ANfo(w1_elements,:)==1); % I try to add only neurons that have significant first order cooccurrences with members of the assembly
  138. w2_to_test(ismember(w2_to_test,w1_elements))=[]; % I erase the one that are already in the assembly
  139. w2_to_test=unique(w2_to_test);
  141. for ww2=1:length(w2_to_test)
  142. w2=w2_to_test(ww2);
  143. spikeTrain2=binM{ggg}(w2,:)';
  144. [assemblybin_aus]=TestPair_ref(assembly_selected{w1},spikeTrain2,w2,MaxLags(ggg),Dc,ref_lag);
  145. p_values=[p_values; assemblybin_aus.pr(end)];
  146. number_tests=number_tests+2*MaxLags(ggg)+1;
  147. if assemblybin_aus.pr(end)<HBcorrected_p
  148. assembly_selected_aus{xx}=assemblybin_aus;
  149. assembly_selected_aus{xx}.bin=BinSizes(ggg);
  150. xx=xx+1;
  151. Oincrement=1;
  152. end
  153. end
  154. end
  156. if Oincrement
  157. %%% pruning within the same size %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  158. % between two assemblies with the same unit set arranged into different configurations I choose the most significant one
  160. na=length(assembly_selected_aus); % number assemblies
  161. nelement=size(assembly_selected_aus{1}.elements,2); % number elements for assembly
  162. selection=nan(na,nelement+1+1);
  163. assembly_final=cell(1,na); %max possible dimension
  164. nns=1;
  165. for i=1:na
  166. elem=sort(assembly_selected_aus{i}.elements);
  167. [ism,indx]=ismember(elem,selection(:,1:nelement),'rows');
  168. if ~ism
  169. assembly_final{nns}=assembly_selected_aus{i};
  170. selection(nns,1:nelement)=elem;
  171. selection(nns,nelement+1)=assembly_selected_aus{i}.pr(end);
  172. selection(nns,nelement+2)=i;
  173. nns=nns+1;
  174. else
  175. if selection(indx,nelement+1)>assembly_selected_aus{i}.pr(end)
  176. assembly_final{indx}=assembly_selected_aus{i};
  177. selection(indx,nelement+1)=assembly_selected_aus{i}.pr(end);
  178. selection(indx,nelement+2)=i;
  179. end
  180. end
  181. end
  182. assembly_final(nns:end)=[];
  183. assembly_selected=assembly_final;
  184. Assemblies_all_orders{O}=assembly_final;
  185. clear assembly_final
  186. end
  188. %% Holm-Bonferroni
  189. x=1:length(p_values);
  190. p_values=sort(p_values);
  191. p_values_alpha=alph./(number_tests+1-x);
  192. aus=find((p_values'-p_values_alpha)<0);
  193. % HBcorrected_p=p_values(aus(end));
  194. if isempty(aus)
  195. HBcorrected_p=0;
  196. else
  197. HBcorrected_p=p_values(aus(end));
  198. end
  201. end
  206. %% Holm–Bonferroni
  207. x=1:length(p_values);
  208. p_values=sort(p_values)';
  209. p_values_alpha=alph./(number_tests+1-x);
  210. aus=find((p_values-p_values_alpha)<0);
  211. % HBcorrected_p=p_values(aus(end));
  212. if isempty(aus)
  213. HBcorrected_p=0;
  214. else
  215. HBcorrected_p=p_values(aus(end));
  216. end
  218. for o=1:length(Assemblies_all_orders)
  219. for oo=length(Assemblies_all_orders{o}):-1:1
  220. if Assemblies_all_orders{o}{oo}.pr(end)>HBcorrected_p
  221. Assemblies_all_orders{o}(oo)=[];
  222. end
  223. end
  224. end
  226. %% pruning between differen assembly size
  227. o=length(Assemblies_all_orders);
  228. x=1;
  229. for oo=length(Assemblies_all_orders{o}):-1:1
  230. Element_template{x}=Assemblies_all_orders{o}{oo}.elements;
  231. x=x+1;
  232. end
  235. for o=length(Assemblies_all_orders)-1:-1:1
  236. for oo=length(Assemblies_all_orders{o}):-1:1
  237. found=0;
  238. ooo=1;
  239. while ~found && ooo<x
  240. if ismember(Assemblies_all_orders{o}{oo}.elements,Element_template{ooo})
  241. Assemblies_all_orders{o}(oo)=[];
  242. found=1;
  243. else
  244. ooo=ooo+1;
  245. end
  246. end
  247. if found==0
  248. Element_template{x}=Assemblies_all_orders{o}{oo}.elements;
  249. x=x+1;
  250. end
  251. end
  252. end
  254. %% reformat dividing by bins
  255. for o=length(Assemblies_all_orders):-1:1
  256. for oo=length(Assemblies_all_orders{o}):-1:1
  257. bx=find(BinSizes==Assemblies_all_orders{o}{oo}.bin);
  258. assembly.bin{bx}.n=[assembly.bin{bx}.n,Assemblies_all_orders{o}(oo)];
  259. end
  260. end
  261. for gg=length(BinSizes):-1:1
  262. if isempty(assembly.bin{gg}.n)
  263. assembly.bin{gg}=[];
  264. end
  265. end
  267. fprintf('\n');
  268. else
  269. for gg=length(BinSizes):-1:1
  271. assembly.bin{gg}=[];
  272. Assemblies_all_orders=[];
  273. end
  275. end
  277. assembly.parameters.alph=alph;
  278. assembly.parameters.Dc=Dc;
  279. assembly.parameters.No_th=No_th;
  280. assembly.parameters.O_th=O_th;
  281. assembly.parameters.bytelimit=bytelimit;
  282. assembly.parameters.ref_lag=ref_lag;
  285. [As_across_bins,As_across_bins_index]=assemblies_across_bins(assembly,BinSizes);
  288. end

CADopti.m at commit a996de8, under GPL-3.0 · at the source

Overview

Authors: Fabrizio Londei1,2, Giulia Arena2,3,4, Lorenzo Ferrucci3,5, Francesco Siano2,3, Encarni Marcos1, Francesco Ceccarelli3, Genovesio Aldo3,4,6
ORCID iDs: Fabrizio Londei, Lorenzo Ferrucci, Francesco Siano, Encarni Marcos, Francesco Ceccarelli, Genovesio Aldo
  1. Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas and Universidad Miguel Hernández, Sant Joan d'Alacant, Alicante, Spain
  2. Behavioral Neuroscience PhD Program, Sapienza University, Rome, Italy
  3. Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy
  4. Institute of Biochemistry and Cell Biology (IBBC), National Research Council of Italy (CNR), Monterotondo Scalo, Roma, Italy
  5. Department of Wellbeing, Health and Environmental Sustainability, Sapienza University of Rome, Rieti, Italy
  6. Department of Pharmaceutical Sciences, University of Piemonte Orientale, Novara, Italy
Institutions: Consejo Superior de Investigaciones Científicas (Spain); Instituto de Neurociencias (Spain); Sapienza University of Rome (Italy); National Research Council (Italy); Università degli Studi del Piemonte Orientale “Amedeo Avogadro” (Italy)
Journal: The Journal of physiology, volume 604, issue 7, pages 2958-2984
Dates: received 30 July 2025; accepted 27 January 2026; published online 5 March 2026; in print 1 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1113/jp289827 · PMID 41784464 · PMCID PMC13039282 · OpenAlex W7133805990
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), systems (subfield)
Methods: Statistics, Machine learning
Keywords: assembly, connectomics, functional connectivity, hub, motifs
MeSH: Brain*, Nerve Net*, Neurons*, Animals, Mice, Mice, Inbred C57BL, Models, Neurological (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Sapienza Università di Roma (PH1181642DB714F6); European Union NextGeneration EU/PRTR and ERDF/EU (PID2022-141173NA-I00, MICIU/AEI/10.13039/501100011033, RYC2021-035061-I)
Citations: not cited yet (Europe PMC); 107 references in the paper

Abstract

Abstract: Efficient cooperation between brain areas requires a dynamic balance between the segregation of region‐specific functional roles and information broadcasting. Imaging studies of brain‐wide co‐ordination cannot reach the single‐cell assembly level analysis. In this study, we explored co‐ordinative relationships between single neurons across 71 mouse brain regions using the concept of cell assembly as an investigative tool. Cell assemblies can be considered the fundamental brain processing units, and identifying their aggregate structure can provide a high‐resolution view of inter‐regional co‐ordinative relationships. We first examined pairwise co‐ordination between areas, and then we investigated higher‐order forms of inter‐regional connectivity, searching for triplets of neurons. We mainly focused on one functionally relevant motif: the loop‐like triplet, modelling a reentrant flow of information, which we hypothesised could represent a core mechanism for the integration of information. We found that this reentrant mode of communication was often asymmetrical between areas and largely unrelated to neuron pair co‐ordination. We found that hub neurons, which have a higher‐than‐average number of co‐ordinative relationships with external regions, are consistently and significantly embedded in loop‐like assemblies. These findings suggest that this peculiar motif represents a core architectural feature supporting brain‐wide integration.

Key points: Cell assembly detection allows the identification of motifs of inter‐regional co‐ordination.

Loop‐like motifs of co‐ordination are heterogeneously distributed across the brain.

External hub neurons are consistently and brain‐wide embedded in loop‐like motifs.

Loop‐like motifs appear as an integration‐oriented structure of co‐ordination.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

DurstewitzLab/CADopti

License: GPL-3.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: a996de82f0f6816f7991ef794c17bea17b12bef4, 11 April 2020
Languages: MATLAB (8)
Size: 11 files, 8 scripts
Software Heritage: not archived
Found in: “Data availability statement”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
10 files
At the source: github.com/DurstewitzLab/CADopti

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

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;
  • 8 scripts, each with its path and the digest of its content;
  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • 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

Datasets cited

Data availability statement

The dataset recorded by Steinmetz et al. (2019) and analysed in this study is available at: https://figshare.com/articles/steinmetz/9598406. The algorithm for cell assembly detection (CADopti) proposed by Russo and Durstewitz (2017) and used in this study is available at: https://github.com/DurstewitzLab/CADopti.

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, 7 authors, 5 keywords, 7 MeSH terms, 2 funders, 101 references.

Cite

This paper

Londei, F., Arena, G., Ferrucci, L., Siano, F., Marcos, E., Ceccarelli, F., & Aldo, G. (2026). Neurons embedded in loop-like motifs act as central hubs for brain-wide integration. The Journal of physiology, 604(7), 2958-2984. https://doi.org/10.1113/jp289827

BibTeX

@article{londei2026neurons,
author = {Londei, Fabrizio and Arena, Giulia and Ferrucci, Lorenzo and Siano, Francesco and Marcos, Encarni and Ceccarelli, Francesco and Aldo, Genovesio},
title = {{Neurons embedded in loop-like motifs act as central hubs for brain-wide integration}},
journal = {The Journal of physiology},
year = {2026},
month = mar,
volume = {604},
number = {7},
pages = {2958--2984},
publisher = {Wiley},
issn = {0022-3751},
doi = {10.1113/jp289827},
url = {https://doi.org/10.1113/jp289827},
pmid = {41784464},
pmcid = {PMC13039282}
}

RIS

TY - JOUR
AU - Londei, Fabrizio
AU - Arena, Giulia
AU - Ferrucci, Lorenzo
AU - Siano, Francesco
AU - Marcos, Encarni
AU - Ceccarelli, Francesco
AU - Aldo, Genovesio
TI - Neurons embedded in loop-like motifs act as central hubs for brain-wide integration
T2 - The Journal of physiology
J2 - J Physiol
PY - 2026
DA - 2026/03/05
VL - 604
IS - 7
SP - 2958
EP - 2984
SN - 0022-3751
PB - Wiley
DO - 10.1113/jp289827
UR - https://doi.org/10.1113/jp289827
LA - en
ER -

CSL-JSON

{
"id": "10.1113/jp289827",
"type": "article-journal",
"title": "Neurons embedded in loop-like motifs act as central hubs for brain-wide integration",
"container-title": "The Journal of physiology",
"author": [
{
"family": "Londei",
"given": "Fabrizio"
},
{
"family": "Arena",
"given": "Giulia"
},
{
"family": "Ferrucci",
"given": "Lorenzo"
},
{
"family": "Siano",
"given": "Francesco"
},
{
"family": "Marcos",
"given": "Encarni"
},
{
"family": "Ceccarelli",
"given": "Francesco"
},
{
"family": "Aldo",
"given": "Genovesio"
}
],
"container-title-short": "J Physiol",
"volume": "604",
"issue": "7",
"page": "2958-2984",
"DOI": "10.1113/jp289827",
"PMID": "41784464",
"PMCID": "PMC13039282",
"ISSN": "0022-3751",
"publisher": "Wiley",
"URL": "https://doi.org/10.1113/jp289827",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
5
]
]
}
}

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:10.1113/jp288070 [code]
History bias and its perturbation of the stimulus representation in the macaque prefrontal cortex.
Journal: The Journal of physiology
In common: 2 references, 3 authors
[2] doi:10.1016/j.neuron.2026.07.016 [code]
Inferring brain-wide interactions using data-constrained recurrent neural network models.
Journal: Neuron
In common: Statistics and Machine Learning Toolbox, systems, mouse, 5 references
[3] doi:10.1007/s12311-026-02042-x
The Cerebellar Connectome.
Journal: Cerebellum (London, England)
In common: 5 references
[4] doi:10.1038/s41467-026-74466-2 [code]
Neuromorphic hierarchical modular reservoirs.
Journal: Nature communications
In common: 5 references
[5] doi:10.1038/s41467-026-71664-w [code]
Dorsal prefrontal cortex drives perseverative behavior in mice.
Journal: Nature communications
In common: Statistics and Machine Learning Toolbox, systems, mouse, 4 references
[6] doi:10.7554/elife.109717 [code]
Retrosplenial cortex enables context-dependent goal-directed sensorimotor transformation.
Journal: eLife
In common: Statistics and Machine Learning Toolbox, systems, mouse, 3 references
[7] doi:10.1038/s41467-026-71725-0 [code]
Interactions across hemispheres in prefrontal cortex reflect global cognitive processing.
Journal: Nature communications
In common: Statistics and Machine Learning Toolbox, 4 references
[8] doi:10.1038/s41467-026-71724-1 [code]
Dynamics of oscillatory power support the integration of perceptual and mnemonic information within the premotor cortex.
Journal: Nature communications
In common: Statistics and Machine Learning Toolbox, 3 references
[9] doi:10.1111/ejn.70582 [code]
Multifiber Array-Based Photometry System for Multiregional Functional Mapping in the Mouse Brain.
Journal: The European journal of neuroscience
In common: systems, mouse, 4 references
[10] doi:10.1038/s41467-026-72444-2 [code]
Broadband synergy versus oscillatory redundancy in the visual cortex.
Journal: Nature communications
In common: Statistics and Machine Learning Toolbox, systems, 3 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.