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Fundamentals of Neural Network Modeling: Neuropsychology and Cognitive Neuroscience / Edition 1

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Overview

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.

Contributors: J. Wesson Ashford, Rajendra D. Badgaiyan,Jean P. Banquet, Yves Burnod, Nelson Butters, John Cardoso, Agnes S. Chan,Jean-Pierre Changeux, Kerry L. Coburn, Jonathan D. Cohen, Laurent Cohen, Jose L.

Contreras-Vidal, Antonio R. Damasio, Hanna Damasio, Stanislas Dehaene, Martha J.

Farah, Joaquin M. Fuster, Philippe Gaussier, Angelika Gissler, Dylan G. Harwood,Michael E. Hasselmo, J, Allan Hobson, Sam Leven, Daniel S. Levine, Debra L. Long,Roderick K. Mahurin, Raymond L. Ownby, Randolph W. Parks, Michael I. Posner, David P. Salmon, David Servan-Schreiber, Chantal E. Stern, Jeffrey P. Sutton, Lynette J.

Tippett, Daniel Tranel, Bradley Wyble.

This book provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. 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, Parkinson┬╣s disease, and Alzheimer┬╣s disease.

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Product Details

  • ISBN-13: 9780262161756
  • Publisher: MIT Press
  • Publication date: 12/11/1998
  • Series: Computational Neuroscience
  • Edition description: First Edition
  • Edition number: 1
  • Pages: 442
  • Product dimensions: 7.10 (w) x 10.00 (h) x 1.20 (d)

Table of Contents

Series Foreword
Preface
Contributors
I Introduction to Neural Networks 1
1 An Introduction to Neural Network Modeling: Merits, Limitations, and Controversies 3
2 Functional Cognitive Networks in Primates 33
3 Attention and Neural Networks 61
4 A Neural Network Model of Memory, Amnesia, and Cortico-Hippocampal Interactions 77
II Behavioral States 121
5 A Computational Model of Alcohol Dependence: Simulation of Genetic Differences in Alcohol Preference and of Therapeutic Strategies 123
6 A Computational Perspective on Learned Helplessness and Depression 141
7 Waking and Sleeping States 165
III Neuropsychological Tests and Clinical Syndromes 189
8 Stroop Task, Language, and Neuromodulation: Models of Cognitive Deficits in Schizophrenia 191
9 Neural Network Modeling of Executive Functioning with the Tower of Hanoi Test in Frontal Lobe-Lesioned Patients 209
10 Neuronal Network Models of Acalculia and Prefrontal Deficits 233
11 Neurophychological Assessment of Attention and Its Disorders: Computational Models for Neglect, Extinction, and Sustained Attention 257
12 The Neural Basis of Lexical Retrieval 271
IV Applications in Dementia 297
13 A Model of Human Memory Based on the Cellular Physiology of the Hippocampal Formation 299
14 Neural Network Modeling of Basal Ganglia Function in Parkinson's Disease and Related Disorders 331
15 Neural Network Modeling of Wisconsin Card Sorting and Verbal Fluency Tests: Applications with Frontal Lobe-Damaged and Alzheimer's Disease Patients 357
16 Semantic Network Abnormalities in Patients with Alzheimer's Disease 381
17 Parallel Distributed Processing Models in Alzheimer's Disease 395
Index 417
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