Neuroscience and Connectionist Theory

Inbunden, Engelska, 1990

Del i serien Developments in Connectionist Theory Series

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Beskrivning

Written for cognitive scientists, psychologists, computer scientists, engineers, and neuroscientists, this book provides an accessible overview of how computational network models are being used to model neurobiological phenomena. Each chapter presents a representative example of how biological data and network models interact with the authors' research. The biological phenomena cover network- or circuit-level phenomena in humans and other higher-order vertebrates.

Produktinformation

Utgivningsdatum:1990-02-01
Mått:152 x 229 x undefined mm
Vikt:816 g
Format:Inbunden
Språk:Engelska
Serie:Developments in Connectionist Theory Series
Antal sidor:420
Förlag:Taylor & Francis Inc
ISBN:9780805805048

Utforska kategorier

Systemvetenskap och AI inom Data och IT

Mer om författaren

Mark A. Gluck, David E. Rumelhart

Recensioner i media

"...provides good evidence that neuroscience continues to provide new inspiration for computational modelers."—Artificial Intelligence"There are important messages that can be gleaned by contemplating the collection as a whole....The future of computational neuroscience over the long-term must involve psychologists if it is to maintatin relevance and intellectual rigor. Neuroscience and Connectionist Theory points the way."—Contemporary Psychology

Innehållsförteckning

Contents: B.L. McNaughton, L. Nadel, Hebb-Marr Networks and the Neurobiological Representation of Action in Space. M.F. Bear, L.N. Cooper, Molecular Mechanisms for Synaptic Modification in the Visual Cortex: Interaction Between Theory and Experiment. R. Granger, J. Ambros-Ingerson, U. Staubli, G. Lynch, Memorial Operation of Multiple, Interacting Simulated Brain Structures. M.A. Gluck, E.S. Reifsnider, R.F. Thompson, Adaptive Signal Processing and the Cerebellum: Models of Classical Conditioning and VOR Adaptation. W.B. Levy, C.M. Colbert, N.L. Desmond, Elemental Adaptive Processes of Neurons and Synapses: A Statistical/Computational Perspective. H.T. Wang, B. Mathur, C. Koch, I Thought I Saw It Move: Computing Optical Flow in the Primate Visual System. K.D. Miller, Correlation-Based Models of Neural Development. D. Zipser, Modeling Cortical Computation With Backpropagation.