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Neural manifolds in spinal networks that orchestrate walking and stopping

Code ↔ Paper

2 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 2 matches · all tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Theory of walk-to-stop transition ↔ Model Code Manifold of walking/DeNovoPerturbation.m, the whole file · a weak match · score 0.63 · low dimensional, firing rate, candidates, perturbation, PCA, activated
  2. [2] § Theory of walk-to-stop transition ↔ Model Code Manifold of walking/DeNovoBase.m, the whole file · a weak match · score 0.60 · low dimensional, firing rate, candidates, PCA, activated, instantiated

Paper

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

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The authors' code

MATLAB · 78 lines · 4.3 KB · GPL-3.0 · 1 match

  1. clear;
  2. % Instantiate network and baseline simulation parameters
  3. run BaseNet.m
  4. run BaseParameterSetup.m
  5. dur = 100; % Perturbation duration
  6. subset = 400; % Perturbation population subset
  8. % Freeze-inducing input for population candidate
  9. I_e = ones(Length,t_steps)*GlobalIe + repmat(normrnd(0,2,[size(N.ConnMat,1) 1]),[1 t_steps]); % Input noise across network
  10. POI = Pop2;
  11. deltainput = 18;
  12. for ii = 1:length(Segs)
  13. I_e(POI,(Segs(ii)-FreezeP):Segs(ii)) =I_e(POI,(Segs(ii)-FreezeP):Segs(ii)) - repmat(normrnd(deltainput,2,[nnz(POI) 1]),[1 FreezeP+1]); % Freeze
  14. I_e(randperm(Length,subset)',(Segs(ii)-FreezeP/2):(Segs(ii)-((FreezeP/2)-dur))) = repmat(normrnd(80,5,[1 subset]),[dur+1,1])'; % Random input perturbation to subset
  15. FreezeT = [FreezeT (Segs(ii)-FreezeP):Segs(ii)];
  16. StimT = [StimT (Segs(ii)-FreezeP/2):(Segs(ii)-((FreezeP/2)-dur))];
  17. end
  19. N.SimulateLine(t_steps,'gain',Gain,'I_e',I_e,'gain_noise',false,...
  20. 'threshold',threshold,'fmax',fmax,'noise_ampl',0.5); % Simulate with parameter setup
  21. N.ComputeSpikes; N.Voltage = []; N.Rates = []; % Generate spikes from firing rates
  22. N.FilterSpikes(25,1000); % Filter into continuous traces
  23. run GetStructure.m % Build PCA space from "mean cycle" and package parameters and low-dimensional traces
  25. % Plotting
  26. figure;
  27. subplot(1,3,1);
  28. run Plot.m
  29. title("Silencing sp2/V1",'FontWeight','normal','FontSize',15);
  30. Data_sp2_perturb = Data; clearvars Data
  32. % Freeze-inducing input for population candidate
  33. I_e(:,:) = repmat(I_e(:,1), [1 t_steps]); % Reuse global input structure
  34. POI = Pop3;
  35. deltainput = 12;
  36. for ii = 1:length(Segs)
  37. I_e(POI,(Segs(ii)-FreezeP):Segs(ii)) =I_e(POI,(Segs(ii)-FreezeP):Segs(ii)) - repmat(normrnd(deltainput,2,[nnz(POI) 1]),[1 FreezeP+1]); % Freeze
  38. I_e(randperm(Length,subset)',(Segs(ii)-FreezeP/2):(Segs(ii)-((FreezeP/2)-dur))) = repmat(normrnd(80,5,[1 subset]),[dur+1,1])'; % Random input perturbation to subset
  39. FreezeT = [FreezeT (Segs(ii)-FreezeP):Segs(ii)];
  40. StimT = [StimT (Segs(ii)-FreezeP/2):(Segs(ii)-((FreezeP/2)-dur))];
  41. end
  43. N.SimulateLine(t_steps,'gain',Gain,'I_e',I_e,'gain_noise',false,...
  44. 'threshold',threshold,'fmax',fmax,'noise_ampl',0.5); % Simulate with parameter setup
  45. N.ComputeSpikes; N.Voltage = []; % Generate spikes from firing rates
  46. N.FilterSpikes(25,1000); % Filter into continuous traces
  47. run GetStructure.m % Build PCA space from "mean cycle" and package parameters and low-dimensional traces
  49. % Plotting
  50. subplot(1,3,2);
  51. run Plot.m
  52. title("Silencing sp3/V2a",'FontSize',15,'FontWeight','normal');
  53. Data_sp3_perturb = Data; clearvars Data
  55. % Freeze-inducing input for population candidate
  56. I_e(:,:) = repmat(I_e(:,1), [1 t_steps]); % Reuse global input structure
  57. POI = Pop4;
  58. deltainput = 21;
  59. for ii = 1:length(Segs)
  60. I_e(POI,(Segs(ii)-FreezeP):Segs(ii)) =I_e(POI,(Segs(ii)-FreezeP):Segs(ii)) + repmat(normrnd(deltainput,2,[nnz(POI) 1]),[1 FreezeP+1]); % Freeze
  61. I_e(randperm(Length,subset)',(Segs(ii)-FreezeP/2):(Segs(ii)-((FreezeP/2)-dur))) = repmat(normrnd(80,5,[1 subset]),[dur+1,1])'; % Random input perturbation to subset
  62. FreezeT = [FreezeT (Segs(ii)-FreezeP):Segs(ii)];
  63. StimT = [StimT (Segs(ii)-FreezeP/2):(Segs(ii)-((FreezeP/2)-dur))];
  64. end
  66. N.SimulateLine(t_steps,'gain',Gain,'I_e',I_e,'gain_noise',false,...
  67. 'threshold',threshold,'fmax',fmax,'noise_ampl',0.5); % Simulate with parameter setup
  68. N.ComputeSpikes; N.Voltage = []; % Generate spikes from firing rates
  69. N.FilterSpikes(25,1000); % Filter into continuous traces
  70. run GetStructure.m % Build PCA space from "mean cycle" and package parameters and low-dimensional traces
  72. % Plotting
  73. subplot(1,3,3);
  74. run Plot.m
  75. title("Activating sp4/V2b",'FontSize',15,'FontWeight','normal');
  76. Data_sp4_perturb = Data;
  78. clearvars -except Data_sp2_perturb Data_sp3_perturb Data_sp4_perturb

DeNovoPerturbation.m at commit 4c54e58, under GPL-3.0 · at the source

Overview

Authors: Salif Komi1, Jaspreet Kaur1, August Winther1, Madelaine C. Adamsson Bonfils1, Grace A. Houser1, R.J.F. Sørensen1, Guanghui Li1, Karen Sobriel1, Rune W. Berg1
ORCID iDs: Salif Komi, Grace A. Houser, Rune W. Berg
  1. Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3, 2200, Copenhagen, Denmark
Institutions: University of Copenhagen (Denmark)
Journal: Research Square (preprint)
Dates: published online 23 June 2026
Type: Preprint
License: CC BY
Identifiers: DOI 10.21203/rs.3.rs-9914946/v1 · OpenAlex W7165621238
Open access: green, a free copy (OpenAlex)
Status: code verified
Categories: systems (subfield)
Topic: Zebrafish Biomedical Research Applications (Cell Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Swiss National Science Foundation (206824)
Citations: not cited yet (Europe PMC); 53 references in the paper

Abstract

Walking, stopping, and postural control are fundamental motor behaviors whose dynamical underpinnings remain poorly understood despite extensive characterization of the underlying circuitry. Here, we investigate neural activity behind locomotion and stopping by recording neuronal population activity with Neuropixels probes in the lumbar spinal cord of freely moving rats where walk-to-stop transitions are controlled by brainstem optogenetic stimulation. During walking, the population activity occupies a ring-shaped manifold with limit cycle attractor properties on which a single phase coordinate captures the kinematic variables, hence a “locomotor” manifold. Prior to stopping, the neural state undergoes a bifurcation onto a distinct region composed of non-trivial fixed point attractors whose structure encodes body posture, i.e. a “postural” manifold. Mechanical perturbations confirm the stability of both regimes. Finally, we show that a previously proposed linear network model with Mexican-hat recurrent connectivity accurately predicts both attractor regimes and their transitions. These findings demonstrate both how the spinal cord can operate as an autonomous dynamical system, and how descending controls and change between states of this system.

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 2 matches between paragraphs and lines of code.

BergLab/ManifoldsofWalking

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 4c54e580435a346bec50254c7d4d4231fb888904, 14 February 2026
Languages: MATLAB (21)
Size: 33 files, 21 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), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
23 files
At the source: github.com/BergLab/ManifoldsofWalking

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;
  • 21 scripts, each with its path and the digest of its content;
  • 2 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

No dataset and no data link were found in the paper.

Data availability statement

All data presented in this study are available from the corresponding author, RWB, on reasonable request. Please find the relevant code associated with modeling and data analysis in the following github repository (https://github.com/BergLab/ManifoldsofWalking).

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, journal, dates, 9 authors, 1 funder, 51 references.

Cite

This paper

Komi, S., Kaur, J., Winther, A., Bonfils, M. C. A., Houser, G. A., Sørensen, R., Li, G., Sobriel, K., & Berg, R. W. (2026). Neural manifolds in spinal networks that orchestrate walking and stopping. Research Square (preprint). https://doi.org/10.21203/rs.3.rs-9914946/v1

BibTeX

@article{komi2026neural,
author = {Komi, Salif and Kaur, Jaspreet and Winther, August and Bonfils, Madelaine C. Adamsson and Houser, Grace A. and Sørensen, R.J.F. and Li, Guanghui and Sobriel, Karen and Berg, Rune W.},
title = {{Neural manifolds in spinal networks that orchestrate walking and stopping}},
journal = {Research Square (preprint)},
year = {2026},
month = jun,
publisher = {Research Square},
issn = {2693-5015},
doi = {10.21203/rs.3.rs-9914946/v1},
url = {https://doi.org/10.21203/rs.3.rs-9914946/v1}
}

RIS

TY - JOUR
AU - Komi, Salif
AU - Kaur, Jaspreet
AU - Winther, August
AU - Bonfils, Madelaine C. Adamsson
AU - Houser, Grace A.
AU - Sørensen, R.J.F.
AU - Li, Guanghui
AU - Sobriel, Karen
AU - Berg, Rune W.
TI - Neural manifolds in spinal networks that orchestrate walking and stopping
T2 - Research Square (preprint)
J2 - Res Sq
PY - 2026
DA - 2026/06/23
SN - 2693-5015
PB - Research Square
DO - 10.21203/rs.3.rs-9914946/v1
UR - https://doi.org/10.21203/rs.3.rs-9914946/v1
ER -

CSL-JSON

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"title": "Neural manifolds in spinal networks that orchestrate walking and stopping",
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"author": [
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"issued": {
"date-parts": [
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23
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