Circuit complexity and neural networks

by Ian Parberry

• 22 Want to read
• 18 Currently reading

Published 1994 by MIT Press in Cambridge, Mass .
Written in

Subjects:

• Neural networks (Computer science),
• Computational complexity.,
• Logic circuits.

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Edition Notes

Includes bibliographical references (p. [251]-257) and index.

Statement	Ian Parberry.
Series	Foundations of computing
Classifications
LC Classifications	QA76.87 .P38 1994
The Physical Object
Pagination	xxix, 270 p. :
Number of Pages	270
ID Numbers
Open Library	OL1084239M
ISBN 10	0262161486
LC Control Number	94007955

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One of the aims of this book is to compare the complexity of neural networks and the complexity of conventional computers, looking at the computational ability and resources (neurons and time) that are a necessary part of the foundations of neural network learning. Circuit Complexity and Neural Networks contains a significant amount of. The authors show that neural networks are at least as powerful as cellular automata and that the converse is true for finite networks. Evidence indicates that the full classes are probably identical.

Warren McCulloch and Walter Pitts () opened the subject by creating a computational model for neural networks. In the late s, D. O. Hebb created a learning hypothesis based on the mechanism of neural plasticity that became known as Hebbian and Wesley A. Clark () first used computational machines, then called "calculators", to simulate a Hebbian network.   I’ll answer a more general but IMO slightly more interesting question, “How can neural networks be used for controlling systems?” tl;dr: Neural networks can be used for control in both reinforcement learning and supervised learning settings. The l. If you want a systematic and thorough overview of neural networks, need a good reference book on this subject, or are giving or taking a course on neural networks, this book is for you. More generally, the book is of value for anyone interested in understanding artificial neural networks or in .

The field of cellular neural networks (CNNs) is of growing importance in non­ linear circuits and systems and it is maturing to the point of becoming a new area of study in general nonlinear theory. CNNs emerged through two semi­ nal papers co-authored by Professor Leon O. Chua back in Since. Neural Networks and Deep Learning is a free online book. The book will teach you about: Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data Deep learning, a powerful set of techniques for learning in neural networks Neural networks and deep learning currently provide.   This book was published 24 years before the date of this review. With a focus on complexity in nature (to include the cell, neural networks, natural selection and evolution, gene expression, and other topics), it is interesting to see how much we have progressed since then, and to see which "new" names in the book from are sage old guard /5.