Neural Computing Architectures The Design of Brain-Like Machines

Name: Neural Computing Architectures The Design of Brain-Like Machines
Price: 36.0 USD
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ISBN: 9780262511506

ISBN-10: 0262511509

ISBN-13: 9780262511506

Edition: 2003

Authors: Igor Aleksander

List price: $45.00

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Description:

McClelland and Rumelhart's Parallel Distributed Processingwas 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 Architecturesaddresses 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…

Book details

List price: $45.00
Copyright year: 2003
Publisher: MIT Press
Publication date: 3/17/2003
Binding: Paperback
Pages: 412
Size: 6.10" wide x 8.98" long x 1.03" tall
Weight: 1.364
Language: English

Igor Aleksander is professor of neural engineering systems at the Imperial College of Science, Technology, and Medicine in London. He has studied artificial intelligence for more than thirty years and has published over 200 papers and ten books on the subject, including Reinventing Man, Impossible Minds: My Neurons, My Consciousness and Neuronsand Symbols: The Stuff That Mind Is Made Of.




The Classical Perspective



Why Neural Computing? A personal view




A theory of neural networks




Speech recognition based on topology-preserving neural maps




Neural map applications




Backpropagation in non-feedforward networks




A PDP learning approach to natural language understanding




Learning capabilities of Boolean networks




The Logical Perspective



The logic of connectionist systems




A probabilistic logic neuron network for associative learning




Applications of N-tuple sampling and genetic algorithms to speech recognition




Dynamic Behavior of Boolean networks




Analysis and Interpretation



Statistical mechanics and neural networks




Digital neural networks, matched filters and optical implementations




Hetero-associative networks using link-enabling vs. link-disabling local modification rules




Generation of movement trajectories in primates and robots



The PDP Perspective



A review of parallel distributed processing



Bibliography


Index