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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...
Autores principales: | Shim, Yoonsik, Philippides, Andrew, Staras, Kevin, Husbands, Phil |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
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 |
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