OSCR

Spatial and network principles behind neural generation of locomotion.

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 · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Results › Emergent locomotor activity ↔ 02_HelperFunctions/PlotAnimatedNetworkEmgandPC.m, lines 1–92 · score 0.57 · biceps femoris, tibialis anterior, PC, vastus
  2. [2] § Methods › Estimation of 2D spatial cell type distributions ↔ 00_SpinalNetworkModel/@Network/GetGenesDiffExpSingleCell.m, the whole file · a weak match · score 0.56 · log fold change, genetic, cell, populations, model
  3. [3] § Methods › Estimation of 2D spatial cell type distributions ↔ 00_SpinalNetworkModel/@Network/GetNeuronsInDiffGeneExpSingleCell.m, the whole file · a weak match · score 0.56 · log fold change, genetic, cell, populations, model

Paper

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

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The authors' code

MATLAB · 289 lines · 9.7 KB · CC0-1.0 · 1 match

  1. function PlotAnimatedNetworkEmgandPC(N,varargin)
  2. screensize = get(groot,'ScreenSize');
  3. Save = 0;
  4. W = N.ConnMat;
  5. Save = 0;
  6. RS = 1;
  7. VA =[-130,50];
  8. EPos = nan;
  9. CC = nan;
  10. trail = 300;
  11. noiselevel= 0.2;
  12. pad = 750;
  13. srt = 1;
  14. M = 10 ;
  15. P = 0:size(N.Rates,2)-1;
  16. Proj = 0;
  17. Source = true(1,size(N.Rates,2));
  18. UoI = true(1,size(N.ConnMat,1));
  19. Moi = categorical({'Iliopsoas','Quadriceps','Vastus Lateralis','Biceps Femoris','Tibialis Anterior','Gastrocnemius'});
  22. for ii = 1:2:length(varargin)
  23. switch varargin{ii}
  24. case 'SavePath'
  25. SavePath = varargin{ii+1};
  26. Save = 1;
  27. case 'SaveName'
  28. Name = varargin{ii+1};
  29. case 'MN'
  30. Moi = categorical(varargin{ii+1})';
  31. case 'sort'
  32. srt = varargin{ii+1};
  33. case 'UoI'
  34. UoI = varargin{ii+1};
  35. case 'ElectrodePos'
  36. EPos =varargin{ii+1};
  37. case 'Cluster_chan'
  38. CC = varargin{ii+1};
  39. case 'Source'
  40. Source = varargin{ii+1};
  41. case 'Project'
  42. Proj = varargin{ii+1};
  43. end
  44. end
  46. if (Save)
  47. if ~exist(SavePath, 'dir')
  48. mkdir(SavePath)
  49. end
  50. v = VideoWriter([SavePath '/ ' Name],"MPEG-4");
  51. v.FrameRate = 30;
  52. v.Quality = 60;
  53. open(v);
  54. end
  56. if(isempty(N.EstimatedRates))
  57. N.ComputeEstimatedRates;
  58. end
  60. if(isempty(N.PC))
  61. N.ComputePC('Estimated',1,'Source',Source);
  62. end
  65. DorPop = unique(N.Types(N.Layers == 'DRG'|N.Layers == '1Sp'| N.Layers == '2Sp0' | N.Layers == '2SpI' | N.Layers == '3Sp' | N.Layers == '4Sp'),'sorted');
  66. VentrPop = unique(N.Types(N.Layers == '5Spm'|N.Layers == '5SpL'| N.Layers == '6SpM' | N.Layers == '6SL' | N.Layers == '7Sp' | N.Layers == '8Sp' | N.Layers == 'D' | N.Layers == '10Sp' | N.Layers == 'Ps9'),'sorted');
  67. MN = unique(N.MnID(~isundefined(N.MnID)));
  69. cmapdorsal = fliplr(autumn(length(DorPop)));
  70. cmapventralex = flipud(cool(length(VentrPop)));
  71. cmapventralin = cool(length(VentrPop));
  72. cmapmn = sky(length(MN));
  74. C = zeros(size(N.ConnMat,1),3);
  75. for T = 1:length(DorPop)
  76. whr = N.Types == DorPop(T);
  77. C(whr,:) = repmat(cmapdorsal(T,:),[nnz(whr),1]);
  78. end
  80. for T = 1:length(VentrPop)
  81. whr = N.Types == VentrPop(T);
  82. if(mean(N.Transmit(whr)) < 0)
  83. C(whr,:) = repmat(cmapventralin(T,:),[nnz(whr),1]);
  84. else
  85. C(whr,:) = repmat(cmapventralex(T,:),[nnz(whr),1]);
  86. end
  87. end
  89. for T = 1:length(MN)
  90. whr = N.MnID == MN(T);
  91. C(whr,:) = repmat(cmapmn(T,:),[nnz(whr),1]);
  92. end
  94. %% Compute EMG signal
  95. jj = 1;
  96. EMG = [];
  97. MoI = categorical();
  98. Col = [];
  99. for ii = fliplr(Moi)
  100. ix = N.MnID==ii & N.Latera > 0;
  101. if(~nnz(ix))
  102. continue
  103. end
  104. MoI(jj) = ii;
  105. Col(jj,:) = mean(C(ix,:),1);
  106. prob = sum(N.Rates(:,ix),2);
  107. sig = randn(size(prob))*noiselevel.*binornd(1,normcdf(normalize(prob,'zscore')));
  108. sig = sig.*prob/5 + (randn(size(prob))*noiselevel) ;
  109. EMG(:,jj) = [(randn(1,pad)*noiselevel) (sig*noiselevel)'] + 10*jj;
  110. jj = jj+1;
  111. end
  112. %% Compute Raster
  113. if(srt)
  114. ix = ComputeFiringPhaseSorting(N.Rates(Source,:));
  115. else
  116. [~,ix] = sort(N.Position(:,3));
  117. end
  119. UoI = UoI(ix);
  120. RI = N.Rates(1:end,ix);
  121. RIsp = ((RI-min(RI,[],1)));
  122. RIsp = RIsp./(max(RIsp,[],1));
  123. Poiss = poissrnd(RIsp/40,size(RIsp));
  124. SpikeTrain = [zeros(pad,size(Poiss,2)); logical(Poiss)];
  126. Rates = GetGaussianFiring(SpikeTrain,50,1000);
  127. RatesS = Rates(pad+1:end,:);
  128. RatesS(RatesS==0) = nan;
  130. SpikeTrain = SpikeTrain(:,UoI);
  131. location = N.Position(ix,:);
  132. if(any(~isnan(CC),"all")&&any(~isnan(EPos),"all")&&Proj)
  133. CC = CC(ix);
  134. location = [EPos(CC,1) EPos(CC,2) EPos(CC,3)];
  135. end
  137. if(Proj)
  138. UoIp = UoI;
  139. else
  140. UoIp = true(1,size(N.ConnMat,1));
  141. end
  143. %% Compute online gain
  144. L = round(range(N.Position(:,2)),-2);
  145. edges = [-L:250:L];
  146. tau_V = ones(size(N.ConnMat,1),1)*50;% Slower Excitation
  147. tau_V(sum(N.ConnMat,1) < 0 & ~(N.Types=='MN')',:) = 25; % Faster Inhibition
  148. g = abs(diff(movmean(RatesS,10,1),1,1));
  149. g = movmean(g,500,1);
  150. g = flipud(movmean(flipud(g),500,1));
  151. g = (100./tau_V').*g;
  152. ConnMat= N.ConnMat;
  153. ConnMatIpsiOr(N.Latera < 0,N.Latera > 0) = 0;
  154. ConnMatIpsiOr(N.Latera > 0,N.Latera < 0) = 0;
  155. ConnMatContraOr = N.ConnMat;
  156. ConnMatContraOr(N.Latera > 0,N.Latera > 0) = 0;
  157. ConnMatContraOr(N.Latera < 0,N.Latera < 0) = 0;
  158. %%
  159. fig = figure(Color=Colors().BergBlack,Position=screensize);
  160. %% Define Ax1
  161. ax1 = subplot(3,3,[1 2 4 5 7 8]); % For the 3D
  162. axis off equal tight
  163. box on
  164. view(VA(1),VA(2))
  165. hold on
  166. %% Define Ax2
  167. ax2 = subplot(3,3,3); % For the PC
  168. axis off equal
  169. box off
  170. set(ax2,'Color','none')
  171. xlim([min(N.PC(:,1)) max(N.PC(:,1))]);
  172. ylim([min(N.PC(:,2)) max(N.PC(:,2))]);
  173. zlim([min(N.PC(:,3)) max(N.PC(:,3))]);
  174. view(-20,-30)
  175. hold on
  176. plot3(ax2,[ax2.XLim(1) ax2.XLim(1)],[ax2.YLim(1) ax2.YLim(1)],[ax2.ZLim]-(ax2.ZLim(1)),'Color',Colors().BergWhite,'LineWidth',2);
  177. plot3(ax2,[ax2.XLim(1) ax2.XLim(1)],[ax2.YLim],[0 0],'Color',Colors().BergWhite,'LineWidth',2);
  178. plot3(ax2,[ax2.XLim],[ax2.YLim(1) ax2.YLim(1)],[0 0],'Color',Colors().BergWhite,'LineWidth',2);
  179. title('PCA','FontSize',20,'FontWeight','bold','Color',Colors().BergWhite)
  181. %% Definee Ax4
  182. ax4 = subplot(3,3,6); % For the EMG
  183. axis on square
  184. set(ax4,'Color','none')
  185. set(ax4,'YColor',Colors().BergWhite)
  186. box on
  187. hold on
  188. xlim([0 2*pad]);
  189. ylim([-15 nnz(UoI)+15]);
  190. plot(ax4,[pad pad],ax4.YLim,'Color',Colors().BergWhite,'LineWidth',2);
  191. jj = 1;
  192. yticks([]);
  193. title('Raster','FontSize',20,'FontWeight','bold','Color',Colors().BergWhite)
  195. %% Definee Ax3
  196. ax3 = subplot(3,3,9); % For the EMG
  197. axis on square
  198. set(ax3,'Color','none')
  199. set(ax3,'YColor',Colors().BergWhite)
  200. box on
  201. hold on
  202. xlim([0 1500]);
  203. ylim([-15 10*size(EMG,2)+15]);
  204. plot(ax3,[750 750],ax3.YLim,'Color',Colors().BergWhite,'LineWidth',2);
  205. jj = 1;
  206. yticks([1:length(MoI)]*10);
  207. yticklabels(MoI);
  208. title('EMG','FontSize',20,'FontWeight','bold','Color',Colors().BergWhite)
  210. % %% Define Ax5
  211. % ax5 = subplot(3,3,7);
  212. % xlim([-L L])
  213. % ylim([-300 300])
  214. % axis off
  215. % hold on
  216. % %% Define Ax6
  217. % ax6 = subplot(3,3,8);
  218. % axis off
  219. % hold on
  220. % linkaxes([ax5 ax6]);
  221. %%
  222. if(~isnan(EPos))
  223. scatter3(ax1,EPos(:,1),EPos(:,2),EPos(:,3),20,'Marker','square','MarkerEdgeColor',Colors().BergWhite,'MarkerFaceColor',Colors().BergGray05,'MarkerFaceAlpha',0.4,'LineWidth',0.01,'MarkerEdgeAlpha',0.4);
  224. hold on
  225. end
  226. scatter3(ax1,N.Geometry.Position(N.Geometry.Type=="WM",1),N.Geometry.Position(N.Geometry.Type=="WM",2),N.Geometry.Position(N.Geometry.Type=="WM",3),'filled','MarkerFaceAlpha',0.05,'MarkerEdgeColor','none','MarkerFaceColor',Colors().BergGray02);
  227. for nn = 1:5
  228. for ii = 1:33:size(RatesS,1)
  229. f1 = scatter3(ax1,location(UoIp,1),location(UoIp,2),location(UoIp,3),2*RatesS(ii,UoIp),C(UoIp,:),'filled','MarkerFaceAlpha',0.75);
  230. f2 = plot3(ax2,N.PC(max(1,ii-trail):ii,1),N.PC(max(1,ii-trail):ii,2),N.PC(max(1,ii-trail):ii,3),'LineWidth',4,'Color',sky(1));
  231. f3 = plot(ax3,circshift(EMG,-ii));
  232. ConnMatIspi = g(ii,:).*ConnMatIpsiOr;
  233. % ConnMatContra = g(ii,:).*ConnMatContraOr;
  234. % diffipsi = GeneratePlot(N,ConnMatIspi,edges);
  235. % diffcontra= GeneratePlot(N,ConnMatContra,edges);
  236. % p1 = plot(ax5,edges(1:end-1),diffipsi,'Color',[Colors().BergWhite 0.2],'LineWidth',2);
  237. % p2 = plot(ax6,edges(1:end-1),diffcontra,'Color',[Colors().BergWhite 0.2],'LineWidth',2);
  238. if(~isempty(Col))
  239. colororder(ax3,Col);
  240. end
  241. ST = circshift(SpikeTrain,-ii)';
  242. [ri,ci] = find(ST(:,1:2*pad));
  243. f4 = scatter(ax4,ci,ri,M,'MarkerFaceColor',Colors().BergGray02,'MarkerFaceAlpha',0.9,'MarkerEdgeColor','none');
  244. drawnow;
  245. if(Save)
  246. f = getframe(fig);
  247. writeVideo(v,f);
  248. else
  249. pause(0.033/RS);
  250. end
  251. delete(f1);
  252. delete(f2);
  253. delete(f3);
  254. delete(f4);
  255. % delete(p1);
  256. % delete(p2);
  257. end
  258. end
  259. if(Save)
  260. f = getframe(fig);
  261. writeVideo(v,f);
  262. close(v)
  263. end
  264. end
  266. function difference = GeneratePlot(N,ConnMat,edges)
  267. Ex = N.Transmit > 0;
  268. In = N.Transmit < 0;
  269. Dist = bsxfun(@minus,N.Position(:,2),N.Position(:,2)');
  270. ProjEx = ConnMat(:,Ex);
  271. ProjIn = ConnMat(:,In);
  272. DistEx = Dist(:,Ex);
  273. DistIn = Dist(:,In);
  275. yin = discretize(DistIn(ProjIn~= 0), edges);
  276. yex = discretize(DistEx(ProjEx~= 0), edges);
  277. [GnIn,Min,Cin] = grpstats(ProjIn(ProjIn~=0), yin,["gname","mean","numel"]);
  278. [GnEx,Mex,Cex] = grpstats(ProjEx(ProjEx~=0), yex,["gname","mean","numel"]);
  279. MInZ = zeros(length(edges)-1,1);
  280. MExZ = zeros(length(edges)-1,1);
  281. CInZ = zeros(length(edges)-1,1);
  282. CExZ = zeros(length(edges)-1,1);
  283. MInZ(str2double(GnIn)) = Min;
  284. MExZ(str2double(GnEx)) = Mex;
  285. CInZ(str2double(GnIn)) = Cin;
  286. CExZ(str2double(GnEx)) = Cex;
  287. difference = abs(MExZ.*CExZ)-abs(MInZ.*CInZ);
  288. end

PlotAnimatedNetworkEmgandPC.m at commit 0f04204, under CC0-1.0 · at the source

Overview

Authors: Salif Komi1, August Winther1, Grace A Houser1, Thomas Topilko1, RJF Sørensen1, Silas Dalum Larsen1, Madelaine C Adamsson Bonfils1, Guanghui Li1, Rune W Berg1
ORCID iDs: Salif Komi, August Winther, Grace A Houser, Rune W Berg
  1. Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
Institutions: University of Copenhagen (Denmark)
Journal: Nature communications, volume 17, issue 1, article 7525
Dates: received 10 July 2025; accepted 28 May 2026; published online 15 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-74228-0 · PMID 42297824 · PMCID PMC13408478 · OpenAlex W4403118810
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.10.03.616472
Status: code verified
Categories: genetics / omics (modality), mouse (organism), cellular / molecular (subfield)
Methods: Smoothing, state filtering, decompositions, Connectivity, Evoked potentials, Single-unit activity, calcium imaging, Machine learning
Keywords: Spinal cord, Network models
MeSH: Locomotion*, Nerve Net*, Spinal Cord*, Animals, Mice, Models, Neurological, Motor Neurons, Neurons, Synapses (* major topic)
Topic: Neural Networks and Applications (Artificial Intelligence, Computer Science), according to OpenAlex
Funding: Novo Nordisk Fonden (NNF23OC0082192); Lundbeckfonden (Lundbeck Foundation) (R366-2021-233); Swiss National Science Foundation (SNF Postdoc.Mobility P500PB_206824, 206824)
Citations: cited by 2 papers (Europe PMC); 94 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.

Repositories

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

albert597/TRAILMAP

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: f1349df9e929be6d7d90f6fc2a2ecfbaff4f7347, 18 June 2026
Languages: Python (14)
Size: 371 files, 14 scripts
Software Heritage: not archived
Found in: the text, “Whole-spinal-cord segmentation of neuronal proje”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (4 files), Keras (3 files), OpenCV (3 files), TensorFlow (3 files), Pillow (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
16 files
At the source: github.com/albert597/TRAILMAP

BergLab/SpinalProjectome

License: CC0-1.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0f042045ee2f6d93bbe482127ce2263ada07443f, 24 July 2025
Languages: MATLAB (60)
Size: 103 files, 60 scripts
Software Heritage: not 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 (10 files), Signal Processing Toolbox (5 files), Image Processing Toolbox (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
62 files
At the source: github.com/BergLab/SpinalProjectome

codeocean:9317742

License: none: the authors keep all their rights
State: cannot be verified, verified on 27 September 2026
Evidence: found in the paper
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 27 September 2026: cannot be verified
  • 27 September 2026: cannot be verified
At the source:

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:

  • it points to the authors' code: codeocean:9317742, BergLab/SpinalProjectome
  • it says that the code is available on request

Read it in the paper: doi.org/10.1038/s41467-026-74228-0.

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:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 74 scripts, each with its path and the digest of its content;
  • 3 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 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:

  • it says that the data are available on request

Read it in the paper: doi.org/10.1038/s41467-026-74228-0.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 2 keywords, 9 MeSH terms, 3 funders, 85 references.

Cite

This paper

Komi, S., Winther, A., Houser, G. A., Topilko, T., Sørensen, R., Larsen, S. D., Bonfils, M. C. A., Li, G., & Berg, R. W. (2026). Spatial and network principles behind neural generation of locomotion. Nature communications, 17(1), 7525. https://doi.org/10.1038/s41467-026-74228-0

BibTeX

@article{komi2026spatial,
author = {Komi, Salif and Winther, August and Houser, Grace A and Topilko, Thomas and Sørensen, RJF and Larsen, Silas Dalum and Bonfils, Madelaine C Adamsson and Li, Guanghui and Berg, Rune W},
title = {{Spatial and network principles behind neural generation of locomotion}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7525},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74228-0},
url = {https://doi.org/10.1038/s41467-026-74228-0},
pmid = {42297824},
pmcid = {PMC13408478}
}

RIS

TY - JOUR
AU - Komi, Salif
AU - Winther, August
AU - Houser, Grace A
AU - Topilko, Thomas
AU - Sørensen, RJF
AU - Larsen, Silas Dalum
AU - Bonfils, Madelaine C Adamsson
AU - Li, Guanghui
AU - Berg, Rune W
TI - Spatial and network principles behind neural generation of locomotion
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/15
VL - 17
IS - 1
SP - 7525
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74228-0
UR - https://doi.org/10.1038/s41467-026-74228-0
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-74228-0",
"type": "article-journal",
"title": "Spatial and network principles behind neural generation of locomotion",
"container-title": "Nature communications",
"author": [
{
"family": "Komi",
"given": "Salif"
},
{
"family": "Winther",
"given": "August"
},
{
"family": "Houser",
"given": "Grace A"
},
{
"family": "Topilko",
"given": "Thomas"
},
{
"family": "Sørensen",
"given": "RJF"
},
{
"family": "Larsen",
"given": "Silas Dalum"
},
{
"family": "Bonfils",
"given": "Madelaine C Adamsson"
},
{
"family": "Li",
"given": "Guanghui"
},
{
"family": "Berg",
"given": "Rune W"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "7525",
"DOI": "10.1038/s41467-026-74228-0",
"PMID": "42297824",
"PMCID": "PMC13408478",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-74228-0",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
15
]
]
}
}

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

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In common: NumPy, cellular / molecular, 5 references
[5] doi:10.3389/fnetp.2026.1853254 [code]
Recovery in gait and posture: a network-based approach to the assessment of rehabilitation effectiveness after spinal cord injury.
Journal: Frontiers in network physiology
In common: 5 references
[6] doi:10.1002/epi.70296 [code]
Fully automated three-dimensional deep learning-based magnetic resonance imaging segmentation of brain cavities in epilepsy surgery.
Journal: Epilepsia
In common: Keras, TensorFlow, OpenCV, 3 other tools
[7] doi:10.1038/s41597-025-05174-7 [code]
A large-scale MEG and EEG dataset for object recognition in naturalistic scenes
Journal: n/a
In common: Keras, TensorFlow, OpenCV, 3 other tools
[8] doi:10.3390/biomedicines14081670 [code]
Limited Detectability of Network Functional Alterations in a Tauopathy Model Using Mouse Primary Cortical Cultures.
Journal: Biomedicines
In common: Keras, TensorFlow, Image Processing Toolbox, 3 other tools, mouse
[9] doi:10.1038/s41467-026-73106-z [code]
Respiratory pauses highlight sleep architecture in mice.
Journal: Nature communications
In common: Keras, TensorFlow, Image Processing Toolbox, 3 other tools, mouse
[10] doi:10.1038/s41593-026-02376-z [code]
A framework for comparative analysis of human and mouse cortical neuron dendrites in corresponding brain regions.
Journal: Nature neuroscience
In common: Keras, OpenCV, Image Processing Toolbox, 3 other tools, mouse

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