Memristor and Memristive Neural Networks
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 ar...
שמור ב:
| פורמט: | Online |
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| שפה: | אנגלית |
| יצא לאור: |
IntechOpen
2023
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| נושאים: | |
| גישה מקוונת: | ONIX_20231201_9789535139485_1432 |
| תגים: |
אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!
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| _version_ | 1869529938355290112 |
|---|---|
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-130323 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | IntechOpen |
| publisherStr | IntechOpen |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1303232024-04-14T10:28:27Z Memristor and Memristive Neural Networks Pappachen James, Alex neuromorphic computing, deep learning, graphene oxide, phase transition, spice, optical flow thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQN Neural networks and fuzzy systems 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. 2023-12-01T17:19:22Z 2023-12-01T17:19:22Z 2018 book ONIX_20231201_9789535139485_1432 9789535139485 9789535139478 9789535140092 https://directory.doabooks.org/handle/20.500.12854/130323 eng image/jpeg n/a https://www.intechopen.com/books/5973 https://mts.intechopen.com/storage/books/5973/authors_book/authors_book.pdf IntechOpen IntechOpen 10.5772/66539 10.5772/66539 78a36484-2c0c-47cb-ad67-2b9f5cd4a8f6 9789535139485 9789535139478 9789535140092 IntechOpen 324 open access |
| spellingShingle | neuromorphic computing, deep learning, graphene oxide, phase transition, spice, optical flow thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQN Neural networks and fuzzy systems Memristor and Memristive Neural Networks |
| title | Memristor and Memristive Neural Networks |
| title_full | Memristor and Memristive Neural Networks |
| title_fullStr | Memristor and Memristive Neural Networks |
| title_full_unstemmed | Memristor and Memristive Neural Networks |
| title_short | Memristor and Memristive Neural Networks |
| title_sort | memristor and memristive neural networks |
| topic | neuromorphic computing, deep learning, graphene oxide, phase transition, spice, optical flow thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQN Neural networks and fuzzy systems |
| topic_facet | neuromorphic computing, deep learning, graphene oxide, phase transition, spice, optical flow thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQN Neural networks and fuzzy systems |
| url | ONIX_20231201_9789535139485_1432 |