Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP

We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven b...

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Detalles Bibliográficos
Autores principales: Shim, Yoonsik, Philippides, Andrew, Staras, Kevin, Husbands, Phil
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5070787/
https://www.ncbi.nlm.nih.gov/pubmed/27760125
http://dx.doi.org/10.1371/journal.pcbi.1005137

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5070787/
https://www.ncbi.nlm.nih.gov/pubmed/27760125
http://dx.doi.org/10.1371/journal.pcbi.1005137

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