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Learning and Categorization in Modular Neural Networks

Language EnglishEnglish
Book Paperback
Book Learning and Categorization in Modular Neural Networks Jacob M. J. Murre
Libristo code: 06592189
Publishers Taylor & Francis Inc, October 1992
This book introduces a new neural network model called CALM, for categorization and learning in neur... Full description
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This book introduces a new neural network model called CALM, for categorization and learning in neural networks. The author demonstrates how this model can learn the word superiority effect for letter recognition, and discusses a series of studies that simulate experiments in implicit and explicit memory, involving normal and amnesic patients. Pathological, but psychologically accurate, behavior is produced by "lesioning" the arousal system of these models. A concise introduction to genetic algorithms, a new computing method based on the biological metaphor of evolution, and a demonstration on how these algorithms can design network architectures with superior performance are included in this volume.

The role of modularity in parallel hardware and software implementations is considered, including transputer networks and a dedicated 400-processor neurocomputer built by the developers of CALM in cooperation with Delft Technical University. Concluding with an evaluation of the psychological and biological plausibility of CALM models, the book offers a general discussion of catastrophic interference, generalization, and representational capacity of modular neural networks. Researchers in cognitive science, neuroscience, computer simulation sciences, parallel computer architectures, and pattern recognition will be interested in this volume, as well as anyone engaged in the study of neural networks, neurocomputers, and neurosimulators.

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About the book

Full name Learning and Categorization in Modular Neural Networks
Language English
Binding Book - Paperback
Date of issue 1992
Number of pages 250
EAN 9780805813388
ISBN 9780805813388
Libristo code 06592189
Weight 340
Dimensions 152 x 229 x 13
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