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

This text provides an introduction to the neural network modelling of complex cognitive and neuropsychological processes. It is intended to make the neural network approach accessible to practicing neuropsychologists, psychologists, neurologists, and psychiatry. It should also be a useful resource for computer scientists, mathematicians, and interdisciplinary cognitive neuroscientists. The editors (in their introduction) and contibutors explain the basic concepts behind modelling and avoid the use of high-level mathematics. The book is divided into four parts. Part One provides an extensive but basic overview of neural networks modelling, including its history, and present and future trends. It also includes chapters on attention, memory, and primate studies. Part Two discusses neural network models of behavioural states such as alcohol dependence, learned helplessness, depression, and waking and sleeping. Part Three presents neural network models of neuropsychological tests such as the Wisconsin Card Sorting Task, the Tower of Hanoi, and the Stroop Test. Finally, Part Four describes the application of neural network models to dementia: models of acetycholine and memory, verbal fluency, Parkinson's disease, and Alzheimer's disease.

Table of Contents:
Part 1 Introduction to neural networks: an introduction to neural network modelling - merits, limitations and controversies, Debra L. Long et al; functional cognitive networks in primates, J. Wesson Ashford et al; attention and neural networks, Michaael I. Posner and Rajendra D. Badgaiyan; a neural network model of memory, amnesia and cortico-hippocampal interactions, Jean P. Banquet et al. Part 2 Behavioural states: a computational model of alcohol dependence - simulation of genetic differences in alcohol preference and of therapeutic strategies, Raymond L. Ownby; a computational perspective on learned helplessness and depression, Sam Leven; waking and sleeping states, Jeffrey P. Sutton and J. Allan Hobson. Part 3 Neurospychological tests and clinical syndromes: stroop task, language and neuromodulation - models of cognitive deficits in schizophrenia, David Servan-Schreiber and Jonathan D. Cohen; neural network modelling of executive functioning with the Tower of Hanoi test in frontal lobe-lesioned patients, John Cardoso and Randolph W. Parks; neuronal network models of acalculia and prefrontal deficits, Stanislas Dehaene et al; neuropsychological assessment of attention and its disorders -computational models for neglect, extinction and sustained attention, Raymond L. Ownby and Dylan G. Harwood; the neural basis of lexical retrieval, Daniel Tranel et al. Part 4 Applications in dementia: a mode of human memory based on the cellular physiology of the hippocampal formation, Michael E. Hasselmo et al; neural network modelling of basal ganglia function in Parkinson's disease and related disorders, Roderick K. Mahurin; neural network modelling of Wisconsin card sorting and verbal fluency tests -applications with frontal lobe-damaged and Alzheimer's disease patients, Randolph W. Parks and Daniel S. Levine; semantic network abnormalities in patients with Alzheimer's disease, Agnes S. Chan et al; parallel distributed processing models in Alzheimer's disease, Lynette J. Tippett and Martha J. Farah.

About the Author :
Terrence J. Sejnowski holds the Francis Crick Chair at the Salk Institute for Biological Studies and is a Distinguished Professor at the University of California, San Diego. He was a member of the advisory committee for the Obama administration's BRAIN initiative and is President of the Neural Information Processing (NIPS) Foundation. He is the author of The Deep Learning Revolution (MIT Press) and other books. Tomaso A. Poggio is Eugene McDermott Professor in the Department of Brain and Cognitive Sciences at MIT, where he is also Director of the Center for Brains, Minds, and Machines and Codirector of the Center for Biological and Computational Learning. He is coeditor of Perceptual Learning (MIT Press).