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Fundamentals of Neural Network Modeling: Neuropsychology and Cognitive Neuroscience (Computational Neuroscience)

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Description:Over the past few years, computer modeling has become more prevalent inthe clinical sciences as an alternative to traditional symbol-processing models.This book provides an introduction to the neural network modeling of complexcognitive and neuropsychological processes. It is intended to make the neuralnetwork approach accessible to practicing neuropsychologists, psychologists, neurologists, and psychiatrists. It will also be a useful resource for computerscientists, mathematicians, and interdisciplinary cognitive neuroscientists. Theeditors (in their introduction) and contributors explain the basic concepts behindmodeling and avoid the use of high-level mathematics.The book is divided into fourparts. Part I provides an extensive but basic overview of neural network modeling, including its history, present, and future trends. It also includes chapters onattention, memory, and primate studies. Part II discusses neural network models ofbehavioral states such as alcohol dependence, learned helplessness, depression, andwaking and sleeping. Part III presents neural network models of neuropsychologicaltests such as the Wisconsin Card Sorting Task, the Tower of Hanoi, and the StroopTest. Finally, part IV describes the application of neural network models todementia: models of acetycholine and memory, verbal fluency, Parkinsons disease, andAlzheimer's disease.Contributors: J. Wesson Ashford, Rajendra D. Badgaiyan, Jean P.Banquet, Yves Burnod, Nelson Butters, John Cardoso, Agnes S. Chan, Jean-PierreChangeux, 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, DavidP. Salmon, David Servan-Schreiber, Chantal E. Stern, Jeffrey P. Sutton, Lynette J.Tippett, Daniel Tranel, Bradley Wyble.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Fundamentals of Neural Network Modeling: Neuropsychology and Cognitive Neuroscience (Computational Neuroscience). To get started finding Fundamentals of Neural Network Modeling: Neuropsychology and Cognitive Neuroscience (Computational Neuroscience), you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

Pages: —

Format: PDF, EPUB & Kindle Edition

Publisher: MIT Press

Release: 1998

ISBN: 0262161753

Description: Over the past few years, computer modeling has become more prevalent inthe clinical sciences as an alternative to traditional symbol-processing models.This book provides an introduction to the neural network modeling of complexcognitive and neuropsychological processes. It is intended to make the neuralnetwork approach accessible to practicing neuropsychologists, psychologists, neurologists, and psychiatrists. It will also be a useful resource for computerscientists, mathematicians, and interdisciplinary cognitive neuroscientists. Theeditors (in their introduction) and contributors explain the basic concepts behindmodeling and avoid the use of high-level mathematics.The book is divided into fourparts. Part I provides an extensive but basic overview of neural network modeling, including its history, present, and future trends. It also includes chapters onattention, memory, and primate studies. Part II discusses neural network models ofbehavioral states such as alcohol dependence, learned helplessness, depression, andwaking and sleeping. Part III presents neural network models of neuropsychologicaltests such as the Wisconsin Card Sorting Task, the Tower of Hanoi, and the StroopTest. Finally, part IV describes the application of neural network models todementia: models of acetycholine and memory, verbal fluency, Parkinsons disease, andAlzheimer's disease.Contributors: J. Wesson Ashford, Rajendra D. Badgaiyan, Jean P.Banquet, Yves Burnod, Nelson Butters, John Cardoso, Agnes S. Chan, Jean-PierreChangeux, 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, DavidP. Salmon, David Servan-Schreiber, Chantal E. Stern, Jeffrey P. Sutton, Lynette J.Tippett, Daniel Tranel, Bradley Wyble.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Fundamentals of Neural Network Modeling: Neuropsychology and Cognitive Neuroscience (Computational Neuroscience). To get started finding Fundamentals of Neural Network Modeling: Neuropsychology and Cognitive Neuroscience (Computational Neuroscience), you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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Fundamentals of Neural Network Modeling: Neuropsychology and Cognitive Neuroscience (Computational Neuroscience)

Fundamentals of Neural Network Modeling: Neuropsychology and Cognitive Neuroscience (Computational Neuroscience)

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