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Fundamentals of neural network modeling : neuropsychology and cognitive neuroscience

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Over the past few years, computer modeling has become more prevalent in the clinical sciences as an alternative to traditional symbol-processing models. This book provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. It is intended to make the neural network approach accessible to practicing neuropsychologists, psychologists, neurologists, and psychiatrists. It will also be a useful resource for computer scientists, mathematicians, and interdisciplinary cognitive neuroscientists. The editors (in their introduction) and contributors explain the basic concepts behind modeling and avoid the use of high-level mathematics.

The book is divided into four parts. Part I provides an extensive but basic overview of neural network modeling, including its history, present, and future trends. It also includes chapters on attention, memory, and primate studies. Part II discusses neural network models of behavioral states such as alcohol dependence, learned helplessness, depression, and waking and sleeping. Part III presents neural network models of neuropsychological tests such as the Wisconsin Card Sorting Task, the Tower of Hanoi, and the Stroop Test. Finally, part IV describes the application of neural network models to dementia: models of acetycholine and memory, verbal fluency, Parkinsons disease, and Alzheimer's disease.

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Detail Information

Series Title

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Call Number

001 FUN

Publisher

Cambridge, Mass. : : MIT Press,., 1998.

Collation

1 online resource (xiii, 428 pages) : illustrations.

Language

English

ISBN/ISSN

0585108358

Classification

001

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

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Subject(s)

Neural networks (Neurobiology)
Neuropsychology.
Cognitive neuroscience.
Neuropsychiatry.

Specific Detail Info

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Statement of Responsibility

edited by Randolph W. Parks, Daniel S. Levine, and Debra L. Long.

Other Information

Cataloger

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Source

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Validator

maya

Digital Object Identifier (DOI)

https://doi.org/10.7551/mitpress/3163.001.0001

Journal Volume

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Journal Issue

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Subtitle

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