Debra L. Long, Randolph W. Parks, Daniel S. Levine, "Fundamentals of Neural Network Modeling"
Publisher: The MIT Press | 1998 | ISBN: 0262161753 | English | PDF | 426 pages | 32.29 Mb
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. Table of contents Series Foreword
I. Introduction to Neural Networks
1. An Introduction to Neural Network Modeling: Merits, Limitations, and Controversies
2. Functional Cognitive Networks in Primates
3. Attention and Neural Networks
4. A Neural Network Model of Memory, Amnesia, and Cortico-Hippocampal Interactions
II. Behavioral States
5. A Computational Model of Alcohol Dependence: Simulation of Genetic Differences in Alcohol Preference and of Therepeutic Strategies
6. A Computational Perspective on Learned Helplessness and Depression
7. Waking and Sleeping States
III. Neuropsychological Tests and Clinical Syndromes
8. Stroop Task, Language, and Neuromodulation: Models of Cognitive Deficits in Schizophrenia
9. Neural Network Modeling of Executive Functioning with the Tower of Hanoi Test in Frontal Lobe-Lesioned Patientcs
10. Neuronal Network Models of Acalculia and Prefrontal Deficits
11. Neuropsychological Assessment of Attention and Its Disorders: Computational Models for Neglect, Extinction, and Sustained Attention
12. The Neural Basis of Lexical Retrieval
IV. Applications in Dementia
13. A Model of Human Memory Based on the Cellular Physiology of the Hippocampal Formation
14. Neural Network Modeling of Basal Ganglia Function in Parkinson's Disease and Related Disorders
15. Neural Network Modeling of Wisconsin Card Sorting and Verbal Fluency Tests: Applications with Frontal Lobe-Damaged and Alzheimer's Disease Patients
16. Semantic Network Abnormalities in Patients with Alzheimer's Disease
17. Parallel Distributed Processing Models in Alzheimer's Disease
About the Authors Randolph W. Parks, Ph.D., Psy.D., is Associate Professor of Psychiatry in the University of Mississippi School of Medicine at Jackson where he is Director of Neuropsychology.
Daniel S. Levine, Ph.D., is Professor in the Department of Psychology at the University of Texas at Arlington and was the former President of the International Neural Network Society.
Debra L. Long, Ph.D. is Associate Professor of Psychology at the University of California, Davis and teaches in the area of cognitive neuroscience.
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