LEADER 02470nam 2200445 450 001 9910317826903321 005 20231214145423.0 010 $a953-51-4009-4 010 $a953-51-3948-7 035 $a(CKB)4970000000099815 035 $a(iGPub)INOP0003791 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/53143 035 $a(EXLCZ)994970000000099815 100 $a20200219018 xx c0 0 101 0 $aeng 135 $aurcn||||m|||a 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMemristor and memristive neural networks 210 $cIntechOpen$d2018 210 1$a[Place of publication not identified] :$cIntechOpen,$d2018. 210 4$dİ2018 215 $a1 online resource (328 pages) 311 $a953-51-3947-9 330 $aThis 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. 606 $aCOMPUTERS / Data Science / Neural Networks$2bisacsh 610 $aPhysical Sciences 610 $aEngineering and Technology 610 $aNeural Network 610 $aComputer and Information Science 610 $aNumerical Analysis and Scientific Computing 615 7$aCOMPUTERS / Data Science / Neural Networks. 676 $a006 700 $aAlex Pappachen James$4auth$01318876 702 $aJames$b Alex Pappachen 906 $aBOOK 912 $a9910317826903321 996 $aMemristor and memristive neural networks$93033568 997 $aUNINA