The covariance perceptron: A new paradigm for classification and processing of time series in recurrent neuronal networks

Matthieu Gilson, David Dahmen, Rubén Moreno-Bote, Andrea Insabato, Moritz Helias

Open source

DOI: 10.1371/journal.pcbi.1008127
Published: 2020-10-12
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.1008127,
  title = {The covariance perceptron: A new paradigm for classification and processing of time series in recurrent neuronal networks},
  author = {Matthieu Gilson and David Dahmen and Rubén Moreno-Bote and Andrea Insabato and Moritz Helias},
  year = {2020},
  journal = {PLOS Computational Biology},
  doi = {10.1371/journal.pcbi.1008127},
  url = {https://doi.org/10.1371/journal.pcbi.1008127}
}

RIS

TY  - JOUR
TI  - The covariance perceptron: A new paradigm for classification and processing of time series in recurrent neuronal networks
AU  - Matthieu Gilson
AU  - David Dahmen
AU  - Rubén Moreno-Bote
AU  - Andrea Insabato
AU  - Moritz Helias
PY  - 2020
JO  - PLOS Computational Biology
DO  - 10.1371/journal.pcbi.1008127
UR  - https://doi.org/10.1371/journal.pcbi.1008127
ER  -

APA

Gilson, M., Dahmen, D., Moreno-Bote, R., Insabato, A., & Helias, M. (2020). The covariance perceptron: A new paradigm for classification and processing of time series in recurrent neuronal networks. PLOS Computational Biology. https://doi.org/10.1371/journal.pcbi.1008127

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crossref · retrieved 2026-09-25T06:19:23.438Z