Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses

Artificial neural networks exhibit learning abilities and can perform tasks which are tricky for conventional computing systems, such as pattern recognition. Here, Serb et al. show experimentally that memristor arrays can learn reversibly from noisy data thanks to sophisticated learning rules.

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Autores principales:	Alexander Serb, Johannes Bill, Ali Khiat, Radu Berdan, Robert Legenstein, Themis Prodromakis
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2016
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/cdeb3fafed714e98959c6868368644df
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spelling	oai:doaj.org-article:cdeb3fafed714e98959c6868368644df2021-12-02T15:35:00ZUnsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses10.1038/ncomms126112041-1723https://doaj.org/article/cdeb3fafed714e98959c6868368644df2016-09-01T00:00:00Zhttps://doi.org/10.1038/ncomms12611https://doaj.org/toc/2041-1723Artificial neural networks exhibit learning abilities and can perform tasks which are tricky for conventional computing systems, such as pattern recognition. Here, Serb et al. show experimentally that memristor arrays can learn reversibly from noisy data thanks to sophisticated learning rules.Alexander SerbJohannes BillAli KhiatRadu BerdanRobert LegensteinThemis ProdromakisNature PortfolioarticleScienceQENNature Communications, Vol 7, Iss 1, Pp 1-9 (2016)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Alexander Serb Johannes Bill Ali Khiat Radu Berdan Robert Legenstein Themis Prodromakis Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses
description	Artificial neural networks exhibit learning abilities and can perform tasks which are tricky for conventional computing systems, such as pattern recognition. Here, Serb et al. show experimentally that memristor arrays can learn reversibly from noisy data thanks to sophisticated learning rules.
format	article
author	Alexander Serb Johannes Bill Ali Khiat Radu Berdan Robert Legenstein Themis Prodromakis
author_facet	Alexander Serb Johannes Bill Ali Khiat Radu Berdan Robert Legenstein Themis Prodromakis
author_sort	Alexander Serb
title	Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses
title_short	Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses
title_full	Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses
title_fullStr	Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses
title_full_unstemmed	Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses
title_sort	unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses
publisher	Nature Portfolio
publishDate	2016
url	https://doaj.org/article/cdeb3fafed714e98959c6868368644df
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Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses

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