Advances in Memristor Neural Networks

Nowadays, scientific research deals with alternative solutions for creating non-traditional computing systems, such as neural network architectures where the stochastic nature and live dynamics of memristive models play a key role. The features of memristors make it possible to direct processing and...

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Baskı/Yayın Bilgisi: IntechOpen 2023
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Online Erişim:ONIX_20231201_9781789841169_1603
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collection Directory of Open Access Books
description Nowadays, scientific research deals with alternative solutions for creating non-traditional computing systems, such as neural network architectures where the stochastic nature and live dynamics of memristive models play a key role. The features of memristors make it possible to direct processing and analysis of both biosystems and systems driven by artificial intelligence, as well as develop plausible physical models of spiking neural networks with self-organization. This book deals with advanced applications illustrating these concepts, and delivers an important contribution for the achievement of the next generation of intelligent hybrid biostructures. Different modeling and simulation tools can deliver an alternative to funding the theoretical approach as well as practical implementation of memristive systems.
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id doab-20.500.12854ir-130494
institution Directory of Open Access Books
language eng
publishDate 2023
publishDateRange 2023
publishDateSort 2023
publisher IntechOpen
publisherStr IntechOpen
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spelling doab-20.500.12854ir-1304942024-04-04T19:18:22Z Advances in Memristor Neural Networks Ciufudean, Calin synapse, neuromorphic computing, graphene, graphene oxide, image processing, artificial neural networks thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics::PBWH Mathematical modelling Nowadays, scientific research deals with alternative solutions for creating non-traditional computing systems, such as neural network architectures where the stochastic nature and live dynamics of memristive models play a key role. The features of memristors make it possible to direct processing and analysis of both biosystems and systems driven by artificial intelligence, as well as develop plausible physical models of spiking neural networks with self-organization. This book deals with advanced applications illustrating these concepts, and delivers an important contribution for the achievement of the next generation of intelligent hybrid biostructures. Different modeling and simulation tools can deliver an alternative to funding the theoretical approach as well as practical implementation of memristive systems. 2023-12-01T17:42:21Z 2023-12-01T17:42:21Z 2018 book ONIX_20231201_9781789841169_1603 9781789841169 9781789841152 9781838818159 https://directory.doabooks.org/handle/20.500.12854/130494 eng image/jpeg n/a https://www.intechopen.com/books/7334 https://mts.intechopen.com/storage/books/7334/authors_book/authors_book.pdf IntechOpen IntechOpen 10.5772/intechopen.75147 10.5772/intechopen.75147 78a36484-2c0c-47cb-ad67-2b9f5cd4a8f6 9781789841169 9781789841152 9781838818159 IntechOpen 124 open access
spellingShingle synapse, neuromorphic computing, graphene, graphene oxide, image processing, artificial neural networks
thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics::PBWH Mathematical modelling
Advances in Memristor Neural Networks
title Advances in Memristor Neural Networks
title_full Advances in Memristor Neural Networks
title_fullStr Advances in Memristor Neural Networks
title_full_unstemmed Advances in Memristor Neural Networks
title_short Advances in Memristor Neural Networks
title_sort advances in memristor neural networks
topic synapse, neuromorphic computing, graphene, graphene oxide, image processing, artificial neural networks
thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics::PBWH Mathematical modelling
topic_facet synapse, neuromorphic computing, graphene, graphene oxide, image processing, artificial neural networks
thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics::PBWH Mathematical modelling
url ONIX_20231201_9781789841169_1603