Neural Computing Architectures

Neural Computing Architectures

by Igor Aleksander
     
 
McClelland and Rumelhart's Parallel Distributed Processing was the first book to present a definitive account of the newly revived connectionist/neural net paradigm for artificial intelligence and cognitive science. While Neural Computing Architectures addresses the same issues, there is little overlap in the research it reports. These 18 contributions

Overview

McClelland and Rumelhart's Parallel Distributed Processing was the first book to present a definitive account of the newly revived connectionist/neural net paradigm for artificial intelligence and cognitive science. While Neural Computing Architectures addresses the same issues, there is little overlap in the research it reports. These 18 contributions provide a timely and informative overview and synopsis of both pioneering and recent European connectionist research. Several chapters focus on cognitive modeling; however, most of the work covered revolves around abstract neural network theory or engineering applications, bringing important complementary perspectives to currently published work in PDP.

In four parts, chapters take up neural computing from the classical perspective, including both foundational and current work; the mathematical perspective (of logic, automata theory, and probability theory), presenting less well-known work in which the neuron is modeled as a logic truth function that can be implemented in a direct way as a silicon read only memory. They present new material both in the form of analytical tools and models and as suggestions for implementation in optical form, and summarize the PDP perspective in a single extended chapter covering PDP theory, application, and speculation in US research. Each part is introduced by the editor.

Editorial Reviews

Booknews
An overview and synopsis of both pioneering and recent European connectionist research. The book's sixteen contributions treat classical and logical perspectives and analysis and implementation. A concluding chapter considers the appeal of parallel distributed processing. Acidic paper. Annotation c. Book News, Inc., Portland, OR (booknews.com)

Product Details

ISBN-13:
9780262011105
Publisher:
MIT Press
Publication date:
02/29/2000
Edition description:
1st MIT Press ed
Pages:
401
Product dimensions:
6.41(w) x 9.56(h) x 1.03(d)

Meet the Author

Igor Aleksander is Professor of Computer Science at Imperial College in London.

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