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dc.contributor.authorCalin Ciufudean*
dc.date.accessioned2021-02-11T07:49:20Z
dc.date.available2021-02-11T07:49:20Z
dc.date.issued2018*
dc.date.submitted2019-10-03 07:51:52*
dc.identifier37549*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/40308
dc.description.abstractNowadays, 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.*
dc.languageEnglish*
dc.subjectQA1-939*
dc.subject.otherEngineering*
dc.subject.otherPhysical Sciences*
dc.subject.otherEngineering and Technology*
dc.subject.otherElectrical and Electronic Engineering*
dc.subject.otherMathematical Modeling*
dc.titleAdvances in Memristor Neural Networks - Modeling and Applications*
dc.typebook
oapen.identifier.doi10.5772/intechopen.75147*
oapen.relation.isPublishedBy78a36484-2c0c-47cb-ad67-2b9f5cd4a8f6*
virtual.oapen_relation_isPublishedBy.publisher_nameIntechOpen
virtual.oapen_relation_isPublishedBy.publisher_websitehttps://www.intechopen.com/
oapen.relation.isbn9781789841152*
oapen.relation.isbn9781789841169*
oapen.pages124*
oapen.edition1st Edition*


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