Learning through ferroelectric domain dynamics in solid-state synapses

Accurate modelling of memristor dynamics is essential for the development of autonomous learning in artificial neural networks. Through a combined theoretical and experimental study of the polarization switching process in ferroelectric memristors, Boynet al. establish a model that enables learning...

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Autores principales:	Sören Boyn, Julie Grollier, Gwendal Lecerf, Bin Xu, Nicolas Locatelli, Stéphane Fusil, Stéphanie Girod, Cécile Carrétéro, Karin Garcia, Stéphane Xavier, Jean Tomas, Laurent Bellaiche, Manuel Bibes, Agnès Barthélémy, Sylvain Saïghi, Vincent Garcia
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/4eca89cea8fb41d595066d4cec248c7a
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spelling	oai:doaj.org-article:4eca89cea8fb41d595066d4cec248c7a2021-12-02T14:42:00ZLearning through ferroelectric domain dynamics in solid-state synapses10.1038/ncomms147362041-1723https://doaj.org/article/4eca89cea8fb41d595066d4cec248c7a2017-04-01T00:00:00Zhttps://doi.org/10.1038/ncomms14736https://doaj.org/toc/2041-1723Accurate modelling of memristor dynamics is essential for the development of autonomous learning in artificial neural networks. Through a combined theoretical and experimental study of the polarization switching process in ferroelectric memristors, Boynet al. establish a model that enables learning and retrieving patterns in a neural system.Sören BoynJulie GrollierGwendal LecerfBin XuNicolas LocatelliStéphane FusilStéphanie GirodCécile CarrétéroKarin GarciaStéphane XavierJean TomasLaurent BellaicheManuel BibesAgnès BarthélémySylvain SaïghiVincent GarciaNature PortfolioarticleScienceQENNature Communications, Vol 8, Iss 1, Pp 1-7 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Sören Boyn Julie Grollier Gwendal Lecerf Bin Xu Nicolas Locatelli Stéphane Fusil Stéphanie Girod Cécile Carrétéro Karin Garcia Stéphane Xavier Jean Tomas Laurent Bellaiche Manuel Bibes Agnès Barthélémy Sylvain Saïghi Vincent Garcia Learning through ferroelectric domain dynamics in solid-state synapses
description	Accurate modelling of memristor dynamics is essential for the development of autonomous learning in artificial neural networks. Through a combined theoretical and experimental study of the polarization switching process in ferroelectric memristors, Boynet al. establish a model that enables learning and retrieving patterns in a neural system.
format	article
author	Sören Boyn Julie Grollier Gwendal Lecerf Bin Xu Nicolas Locatelli Stéphane Fusil Stéphanie Girod Cécile Carrétéro Karin Garcia Stéphane Xavier Jean Tomas Laurent Bellaiche Manuel Bibes Agnès Barthélémy Sylvain Saïghi Vincent Garcia
author_facet	Sören Boyn Julie Grollier Gwendal Lecerf Bin Xu Nicolas Locatelli Stéphane Fusil Stéphanie Girod Cécile Carrétéro Karin Garcia Stéphane Xavier Jean Tomas Laurent Bellaiche Manuel Bibes Agnès Barthélémy Sylvain Saïghi Vincent Garcia
author_sort	Sören Boyn
title	Learning through ferroelectric domain dynamics in solid-state synapses
title_short	Learning through ferroelectric domain dynamics in solid-state synapses
title_full	Learning through ferroelectric domain dynamics in solid-state synapses
title_fullStr	Learning through ferroelectric domain dynamics in solid-state synapses
title_full_unstemmed	Learning through ferroelectric domain dynamics in solid-state synapses
title_sort	learning through ferroelectric domain dynamics in solid-state synapses
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/4eca89cea8fb41d595066d4cec248c7a
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Learning through ferroelectric domain dynamics in solid-state synapses

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