OSCR

Assistive algorithms influence neural representations in motor brain-computer interfaces.

Code ↔ Paper

11 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 11 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Data analysis › Rank-ordered neuron adding curve ↔ neural_data_analysis/compactness.ipynb, lines 142–234 · score 0.85 · normalized prediction accuracy, early days, ranked units, Adding Curve, late days, scores
  2. [2] § Methods › Model › Arm model ↔ model/src/TorqueBasedArm.cpp, lines 88–124 · score 0.83 · end effector acceleration, friction matrix, end effector velocity, end effector position, inertia, torques
  3. [3] § Results › Credit assignment to readout units during long-term learning with adaptive decoders ↔ neural_data_analysis/credit_assignment.ipynb, lines 141–207 · score 0.67 · cross validation, logistic regression, credit assignment, predict, accuracies, neural
  4. [4] § Methods › Data analysis › Logistic regression model ↔ neural_data_analysis/compactness.ipynb, lines 142–234 · score 0.65 · max iters, late days, L2, Logistic, regression, accuracy
  5. [5] § Methods › Experiment › BCI control with adaptive decoders ↔ model/params/bci-model-clda0.5/copy_sim_bci_model.cpp, lines 1–14 · score 0.62 · closed loop decoder, velocity Kalman filter, KF, BCI, adaptation, trained
  6. [6] § Methods › Experiment › BCI control with adaptive decoders ↔ model/params/bci_model1/copy_sim_bci_model.cpp, lines 1–14 · score 0.62 · closed loop decoder, velocity Kalman filter, KF, BCI, adaptation, trained
  7. [7] § Methods › Model › Analysis of the model ↔ model/analysis/plot_weight_change.py, lines 102–175 · score 0.60 · weight change, recurrent weight, linregress, scatter, fixed decoder, seed
  8. [8] § Methods › Data analysis › Logistic regression model ↔ neural_data_analysis/credit_assignment.ipynb, lines 141–207 · score 0.59 · logistic regression, flattened, scored, splits, predict, accuracy
  9. [9] § Methods › Experiment › BCI control with adaptive decoders ↔ model/params/bci-model-clda0.5/copy_sim_bci_model.cpp, lines 1–14 · score 0.54 · unit swaps, closed loop, KF, BCI, loss, decoder
  10. [10] § Methods › Experiment › BCI control with adaptive decoders ↔ model/params/bci_model1/copy_sim_bci_model.cpp, lines 1–14 · score 0.54 · unit swaps, closed loop, KF, BCI, loss, decoder
  11. [11] § Methods › Model › Network model ↔ model/params/arm_model/copy_sim_arm_model.cpp, the whole file · a weak match · score 0.54 · activation function, weight matrix, membrane, ReLU, recurrent, RNN

Paper

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

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The authors' code

Jupyter notebook · 289 lines · 66 KB · no license · 2 matches

The registry keeps no copy of this file: its repository has no license, so its authors keep all their rights to it. Your browser shows it from its source, with JavaScript.

It can be read at the source: neural_data_analysis/compactness.ipynb.

Overview

Authors: Pavithra Rajeswaran1, Alexandre Payeur2,3, Guillaume Lajoie2,3, Amy L Orsborn1,4,5
ORCID iDs: Pavithra Rajeswaran, Alexandre Payeur, Guillaume Lajoie, Amy L Orsborn
  1. Department of Bioengineering, University of Washington, Seattle, WA USA
  2. Department of Mathematics and Statistics, Université de Montréal, Montréal, QC Canada
  3. Mila - Québec Artificial Intelligence Institute, Montréal, QC Canada
  4. Department of Electrical and Computer Engineering, University of Washington, Seattle, WA USA
  5. Washington National Biomedical Research Center, Seattle, WA USA
Institutions: University of Washington (United States); Mila - Quebec Artificial Intelligence Institute (Canada); Université de Montréal (Canada)
Journal: Nature communications, volume 17, issue 1, article 9832
Dates: received 25 April 2025; accepted 20 July 2026; published online 15 September 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-76109-y · PMID 42744792 · PMCID PMC13578456 · OpenAlex W7213337904
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: non-human primate (organism), systems (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Single-unit activity, calcium imaging
Keywords: Brain-machine interface, Motor cortex, Neural decoding, Learning algorithms
MeSH: Algorithms*, Brain-Computer Interfaces*, Motor Cortex*, Animals, Learning, Macaca mulatta, Male, Models, Neurological, Movement, Neural Networks, Computer, Neurons (* major topic)
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NICHD NIH HHS (K12 HD073945); Canada First Research Excellence Fund (Fonds d'excellence en recherche Apog&ée Canada) (IVADO postdoctoral fellowship); NINDS NIH HHS (R01 NS134634); Simons Foundation (898220); U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) (NS134634); National Science Foundation (NSF) (Accelnet INBIC fellowship); U.S. Department of Health & Human Services | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) (K12HD073945); Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada (NSERC Canadian Network for Research and Innovation in Machining Technology) (RGPIN-2018-04821)
Citations: not cited yet (Europe PMC); 82 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.

Repository

Its files are read in the Code ↔ Paper reader above, with 11 matches between paragraphs and lines of code.

pavi-rajes/assistive-sensory-motor-perturbations-influence-learned-neural-representations

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 571dfc0479b74a21f4b95bfda5671cf27d2ba36c, 1 May 2024
Languages: C++ (40), Python (7), Jupyter (3), Shell (2)
Size: 109 files, 52 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (neural_data_analysis/requirements.txt, model/analysis/environment.yml), 3 notebooks
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: Matplotlib (10 files), NumPy (10 files), seaborn (10 files), SciPy (5 files), scikit-learn (4 files), pandas (3 files), Numba (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
53 files, not copied: shown from their source

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At the source: github.com/pavi-rajes/Assistive-sensory-motor-perturbations-influence-learned-neural-representations/tree/main

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:

Read it in the paper: doi.org/10.1038/s41467-026-76109-y.

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;
  • 52 scripts, each with its path and the digest of its content;
  • 11 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.

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-76109-y.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 4 keywords, 11 MeSH terms, 8 funders, 73 references.

Cite

This paper

Rajeswaran, P., Payeur, A., Lajoie, G., & Orsborn, A. L. (2026). Assistive algorithms influence neural representations in motor brain-computer interfaces. Nature communications, 17(1), 9832. https://doi.org/10.1038/s41467-026-76109-y

BibTeX

@article{rajeswaran2026assistive,
author = {Rajeswaran, Pavithra and Payeur, Alexandre and Lajoie, Guillaume and Orsborn, Amy L},
title = {{Assistive algorithms influence neural representations in motor brain-computer interfaces}},
journal = {Nature communications},
year = {2026},
month = sep,
volume = {17},
number = {1},
pages = {9832},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-76109-y},
url = {https://doi.org/10.1038/s41467-026-76109-y},
pmid = {42744792},
pmcid = {PMC13578456}
}

RIS

TY - JOUR
AU - Rajeswaran, Pavithra
AU - Payeur, Alexandre
AU - Lajoie, Guillaume
AU - Orsborn, Amy L
TI - Assistive algorithms influence neural representations in motor brain-computer interfaces
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/09/15
VL - 17
IS - 1
SP - 9832
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-76109-y
UR - https://doi.org/10.1038/s41467-026-76109-y
LA - en
ER -

CSL-JSON

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

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