Memristor and Memristive Neural Networks
by Alex Pappachen James (ed.)
Publisher: InTech 2018
Number of pages: 324
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. 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.
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by David Kriesel - dkriesel.com
Text and illustrations should be memorable and easy to understand to offer as many people as possible access to the field of neural networks. The chapters are individually accessible to readers with little previous knowledge.
by Ben Goertzel - Plenum Press
This text applies the concepts of complexity science to provide an explanation of all aspects of human creativity. The book describes the model that integrates ideas from computer science, mathematics, neurobiology, philosophy, and psychology.
by Todd Troyer - University of Texas at San Antonio
These notes have three main objectives: to present the major concepts of computational neuroscience, to present the basic mathematics that underlies these concepts, and to give the reader some idea of common approaches taken by neuroscientists.
by Mark Watson - Lulu.com
The book uses the author's libraries and the best of open source software to introduce AI (Artificial Intelligence) technologies like neural networks, genetic algorithms, expert systems, machine learning, and NLP (natural language processing).