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Synopsis
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 U.S. research. Each part is introduced by the editor.
Igor Aleksander is Professor of Computer Science at Imperial College in London
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About the Author
Igor Aleksander is Professor of Computer Science at Imperial College in London.
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- PublisherMit Pr
- Publication date1989
- ISBN 10 0262011107
- ISBN 13 9780262011105
- BindingHardcover
- Edition number1
- EditorAleksander Igor
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Neural Computing Architectures: The Design of Brain-Like Machines
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MIT Press, Cambridge, MA, 1989
ISBN 10: 0262011107
ISBN 13: 9780262011105
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