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Representation Transfer via Invariant Input-driven Continuous Attractors for Fast Domain Adaptation.

Overview

Authors: Tie Xu1, Shengdun Wu1, Junwen Luo2, Yao Yang1, Feng Lin3, Junjie Jiang4, Guozhang Chen5, Dongping Yang1,6
ORCID iDs: Feng Lin, Guozhang Chen, Dongping Yang
  1. Zhejiang Lab,Hangzhou, 311100 China
  2. Institutes of Brain Science, Fudan University,Shanghai, 200433 China
  3. MoE Key Lab of Human Factors and Intelligent Reduction of Vibration & Noise, Fuyao University of Science and Technology, Fuzhou, 350000 China
  4. The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University,Xi’an, 710049 China
  5. National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University,Beijing, 100871 China
  6. Shenzhen Loop Area Institute (SLAI), Shenzhen, 518045 China
Institutions: Zhejiang Lab (China); Fudan University (China); Fuyao University of Science and Technology (China); Xi'an Jiaotong University (China); Peking University (China)
Journal: Communications biology, volume 9, issue 1, article 747
Dates: received 9 September 2025; accepted 16 March 2026; published online 4 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-09938-8 · PMID 41935160 · PMCID PMC13230790 · OpenAlex W7149661022
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), human (organism), computational (subfield)
Methods: Smoothing, state filtering, decompositions, Machine learning, Statistics, fMRI & imaging
Keywords: Learning algorithms, Dynamical systems
MeSH: Recurrent Neural Networks*, Adaptive Algorithms, Humans, Representation Machine Learning, Soft Computing (* major topic)
Topic: Domain Adaptation and Few-Shot Learning (Artificial Intelligence, Computer Science), according to OpenAlex
Funding: Natural Science Foundation of Zhejiang Province (LZ24A050007); National Natural Science Foundation of China (National Science Foundation of China) (12175242)
Citations: not cited yet (Europe PMC); 76 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.

Code

No file of the authors' code could be read here: it is described below, and read at its source.

Zenodo 16441066

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
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 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
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: Zenodo 16441066

Read it in the paper: doi.org/10.1038/s42003-026-09938-8.

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;
  • 0 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

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:

Read it in the paper: doi.org/10.1038/s42003-026-09938-8.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 2 keywords, 5 MeSH terms, 2 funders, 60 references.

Cite

This paper

Xu, T., Wu, S., Luo, J., Yang, Y., Lin, F., Jiang, J., Chen, G., & Yang, D. (2026). Representation Transfer via Invariant Input-driven Continuous Attractors for Fast Domain Adaptation. Communications biology, 9(1), 747. https://doi.org/10.1038/s42003-026-09938-8

BibTeX

@article{xu2026representation,
author = {Xu, Tie and Wu, Shengdun and Luo, Junwen and Yang, Yao and Lin, Feng and Jiang, Junjie and Chen, Guozhang and Yang, Dongping},
title = {{Representation Transfer via Invariant Input-driven Continuous Attractors for Fast Domain Adaptation}},
journal = {Communications biology},
year = {2026},
month = apr,
volume = {9},
number = {1},
pages = {747},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-09938-8},
url = {https://doi.org/10.1038/s42003-026-09938-8},
pmid = {41935160},
pmcid = {PMC13230790}
}

RIS

TY - JOUR
AU - Xu, Tie
AU - Wu, Shengdun
AU - Luo, Junwen
AU - Yang, Yao
AU - Lin, Feng
AU - Jiang, Junjie
AU - Chen, Guozhang
AU - Yang, Dongping
TI - Representation Transfer via Invariant Input-driven Continuous Attractors for Fast Domain Adaptation
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/04/04
VL - 9
IS - 1
SP - 747
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-09938-8
UR - https://doi.org/10.1038/s42003-026-09938-8
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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