Connectionist Modeling and Brain Function
Connectionist Modeling and Brain Function

Author: Stephen José Hanson

Publisher: Bradford Book

Published: 1990

Total Pages: 448

ISBN-13:

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Bringing together contributions in biology, neuroscience, computer science, physics, and psychology, this book offers a solid tutorial on current research activity in connectionist-inspired biology-based modeling. It describes specific experimental approaches and also confronts general issues related to learning associative memory, and sensorimotor development. Introductory chapters by editors Hanson and Olson, along with Terrence Sejnowski, Christof Koch, and Patricia S. Churchland, provide an overview of computational neuroscience, establish the distinction between "realistic" brain models and "simplified" brain models, provide specific examples of each, and explain why each approach might be appropriate in a given context. The remaining chapters are organized so that material on the anatomy and physiology of a specific part of the brain precedes the presentation of modeling studies. The modeling itself ranges from simplified models to more realistic models and provides examples of constraints arising from known brain detail as well as choices modelers face when including or excluding such constraints. There are three sections, each focused on a key area where biology and models have converged. Stephen Jose Hanson is Member of Technical Staff, Bellcore, and Visiting Faculty, Cognitive Science Laboratory, Princeton University. Carl R. Olson is Assistant Professor, Department of Psychology at Princeton Connectionist Modeling and Brain Functionis included in the Network Modeling and Connectionism series, edited by Jeffrey Elman.

Modeling Brain Function
Modeling Brain Function

Author: D. J. Amit

Publisher: Cambridge University Press

Published: 1989

Total Pages: 528

ISBN-13: 9780521421249

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One of the most exciting and potentially rewarding areas of scientific research is the study of the principles and mechanisms underlying brain function. It is also of great promise to future generations of computers. A growing group of researchers, adapting knowledge and techniques from a wide range of scientific disciplines, have made substantial progress understanding memory, the learning process, and self organization by studying the properties of models of neural networks - idealized systems containing very large numbers of connected neurons, whose interactions give rise to the special qualities of the brain. This book introduces and explains the techniques brought from physics to the study of neural networks and the insights they have stimulated. It is written at a level accessible to the wide range of researchers working on these problems - statistical physicists, biologists, computer scientists, computer technologists and cognitive psychologists. The author presents a coherent and clear nonmechanical presentation of all the basic ideas and results. More technical aspects are restricted, wherever possible, to special sections and appendices in each chapter. The book is suitable as a text for graduate courses in physics, electrical engineering, computer science and biology.

Connectionist Modelling in Cognitive Neuropsychology
Connectionist Modelling in Cognitive Neuropsychology

Author: David C. Plaut

Publisher: Psychology Press

Published: 1994

Total Pages: 172

ISBN-13: 9780863773365

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This title presents the most comprehensive existing "case study" of how the effects of damage in connectionist models can replicate the patterns of cognitive impairments that can arise in humans as a result of brain damage.

Fundamentals of Neural Network Modeling
Fundamentals of Neural Network Modeling

Author: Randolph W. Parks

Publisher: MIT Press

Published: 1998

Total Pages: 450

ISBN-13: 9780262161756

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Provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. 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

Introduction to Connectionist Modelling of Cognitive Processes
Introduction to Connectionist Modelling of Cognitive Processes

Author: Peter McLeod

Publisher: Oxford University Press, USA

Published: 1998-01-01

Total Pages: 388

ISBN-13: 9780198524274

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Describes the principles of connectionist modelling, and its application in understanding how the brain produces speech, forms memories, recognizes faces, and how intellect develops and deteriorates after brain damage.

The Handbook of Brain Theory and Neural Networks
The Handbook of Brain Theory and Neural Networks

Author: Michael A. Arbib

Publisher: MIT Press

Published: 2003

Total Pages: 1328

ISBN-13: 0262011972

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This second edition presents the enormous progress made in recent years in the many subfields related to the two great questions : how does the brain work? and, How can we build intelligent machines? This second edition greatly increases the coverage of models of fundamental neurobiology, cognitive neuroscience, and neural network approaches to language. (Midwest).

Connectionism and the Mind
Connectionism and the Mind

Author: William Bechtel

Publisher: Wiley-Blackwell

Published: 2002-01-21

Total Pages: 424

ISBN-13: 9780631207139

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Connectionism and the Mind provides a clear and balanced introduction to connectionist networks and explores theoretical and philosophical implications. Much of this discussion from the first edition has been updated, and three new chapters have been added on the relation of connectionism to recent work on dynamical systems theory, artificial life, and cognitive neuroscience. Read two of the sample chapters on line: Connectionism and the Dynamical Approach to Cognition: http://www.blackwellpublishing.com/pdf/bechtel.pdf Networks, Robots, and Artificial Life: http://www.blackwellpublishing.com/pdf/bechtel2.pdf

Connectionist Models
Connectionist Models

Author: David S. Touretzky

Publisher: Morgan Kaufmann

Published: 2014-05-12

Total Pages: 417

ISBN-13: 1483214486

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Connectionist Models contains the proceedings of the 1990 Connectionist Models Summer School held at the University of California at San Diego. The summer school provided a forum for students and faculty to assess the state of the art with regards to connectionist modeling. Topics covered range from theoretical analysis of networks to empirical investigations of learning algorithms; speech and image processing; cognitive psychology; computational neuroscience; and VLSI design. Comprised of 40 chapters, this book begins with an introduction to mean field, Boltzmann, and Hopfield networks, focusing on deterministic Boltzmann learning in networks with asymmetric connectivity; contrastive Hebbian learning in the continuous Hopfield model; and energy minimization and the satisfiability of propositional logic. Mean field networks that learn to discriminate temporally distorted strings are described. The next sections are devoted to reinforcement learning and genetic learning, along with temporal processing and modularity. Cognitive modeling and symbol processing as well as VLSI implementation are also discussed. This monograph will be of interest to both students and academicians concerned with connectionist modeling.

Representation in the Brain
Representation in the Brain

Author: Asim Roy

Publisher: Frontiers Media SA

Published: 2018-09-28

Total Pages: 147

ISBN-13: 2889455963

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This eBook contains ten articles on the topic of representation of abstract concepts, both simple and complex, at the neural level in the brain. Seven of the articles directly address the main competing theories of mental representation – localist and distributed. Four of these articles argue – either on a theoretical basis or with neurophysiological evidence – that abstract concepts, simple or complex, exist (have to exist) at either the single cell level or in an exclusive neural cell assembly. There are three other papers that argue for sparse distributed representation (population coding) of abstract concepts. There are two other papers that discuss neural implementation of symbolic models. The remaining paper deals with learning of motor skills from imagery versus actual execution. A summary of these papers is provided in the Editorial.