Neural Networks: A Systematic Introduction
by Raul Rojas
Publisher: Springer 1996
Number of pages: 509
Theoretical laws and models scattered in the literature are brought together in this book into a general theory of artificial neural nets. Starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced. The book for readers who seek an overview of the field and wish to deepen their knowledge. Suitable as a basis for university courses in neurocomputing.
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by Kenji Suzuki (ed.) - InTech
Artificial neural networks may be the single most successful technology in the last two decades. The purpose of this book is to provide recent advances in architectures, methodologies, and applications of artificial neural networks.
Neural networks excel in a number of problem areas where conventional von Neumann computer systems have traditionally been slow and inefficient. This book is going to discuss the creation and use of artificial neural networks.
by Alex Pappachen James (ed.) - InTech
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. Various memristor models are discussed.
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.