Toward reliable machine learning models for neural circuit inference: A diagnostic study of CNNs on spike trains

Xiaoqian Sun, Hui Lu, Chen Zeng, Rahul Simha

Open source

DOI: 10.1371/journal.pcbi.1014615
Published: 2026-08-10
Container: PLOS Computational Biology
Publisher: Public Library of Science (PLoS)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1371/journal.pcbi.1014615,
  title = {Toward reliable machine learning models for neural circuit inference: A diagnostic study of CNNs on spike trains},
  author = {Xiaoqian Sun and Hui Lu and Chen Zeng and Rahul Simha},
  year = {2026},
  journal = {PLOS Computational Biology},
  doi = {10.1371/journal.pcbi.1014615},
  url = {https://doi.org/10.1371/journal.pcbi.1014615}
}

RIS

TY  - JOUR
TI  - Toward reliable machine learning models for neural circuit inference: A diagnostic study of CNNs on spike trains
AU  - Xiaoqian Sun
AU  - Hui Lu
AU  - Chen Zeng
AU  - Rahul Simha
PY  - 2026
JO  - PLOS Computational Biology
DO  - 10.1371/journal.pcbi.1014615
UR  - https://doi.org/10.1371/journal.pcbi.1014615
ER  -

APA

Sun, X., Lu, H., Zeng, C., & Simha, R. (2026). Toward reliable machine learning models for neural circuit inference: A diagnostic study of CNNs on spike trains. PLOS Computational Biology. https://doi.org/10.1371/journal.pcbi.1014615

Source records

crossref · retrieved 2026-09-26T17:31:18.784Z