02470nam 2200445 450 991031782690332120231214145423.0953-51-4009-4953-51-3948-7(CKB)4970000000099815(iGPub)INOP0003791(oapen)https://directory.doabooks.org/handle/20.500.12854/53143(EXLCZ)99497000000009981520200219018 xx c0 0engurcn||||m|||atxtrdacontentcrdamediacrrdacarrierMemristor and memristive neural networksIntechOpen2018[Place of publication not identified] :IntechOpen,2018.©20181 online resource (328 pages)953-51-3947-9 This book covers a range of models, circuits and systems built with memristor devices and networks in applications to neural networks. It is divided into three parts: (1) Devices, (2) Models and (3) Applications. The resistive switching property is an important aspect of the memristors, and there are several designs of this discussed in this book, such as in metal oxide/organic semiconductor nonvolatile memories, nanoscale switching and degradation of resistive random access memory and graphene oxide-based memristor. The modelling of the memristors is required to ensure that the devices can be put to use and improve emerging application. In this book, various memristor models are discussed, from a mathematical framework to implementations in SPICE and verilog, that will be useful for the practitioners and researchers to get a grounding on the topic. The applications of the memristor models in various neuromorphic networks are discussed covering various neural network models, implementations in A/D converter and hierarchical temporal memories.COMPUTERS / Data Science / Neural NetworksbisacshPhysical SciencesEngineering and TechnologyNeural NetworkComputer and Information ScienceNumerical Analysis and Scientific ComputingCOMPUTERS / Data Science / Neural Networks.006Alex Pappachen Jamesauth1318876James Alex PappachenBOOK9910317826903321Memristor and memristive neural networks3033568UNINA