The Handbook of Brain Theory and Neural Networks

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MIT Press, 2003 - Computers - 1290 pages
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Dramatically updating and extending the first edition, published in 1995, the second edition of The Handbook of Brain Theory and Neural Networks 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?Once again, the heart of the book is a set of almost 300 articles covering the whole spectrum of topics in brain theory and neural networks. The first two parts of the book, prepared by Michael Arbib, are designed to help readers orient themselves in this wealth of material. Part I provides general background on brain modeling and on both biological and artificial neural networks. Part II consists of "Road Maps" to help readers steer through articles in part III on specific topics of interest. The articles in part III are written so as to be accessible to readers of diverse backgrounds. They are cross-referenced and provide lists of pointers to Road Maps, background material, and related reading.The second edition greatly increases the coverage of models of fundamental neurobiology, cognitive neuroscience, and neural network approaches to language. It contains 287 articles, compared to the 266 in the first edition. Articles on topics from the first edition have been updated by the original authors or written anew by new authors, and there are 106 articles on new topics.

 

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This book, though outdated, treats with some great, interesting concepts, like Dynamic link architecture. This is the formation of a set of dynamic linked nodes to create objects in human memory, like a dynamic link library, but for the human brain.
At 1290 pages, the name "handbook" hardly applies, but the book would help anyone studying psychology, memory, learning, or even specialized fields like heart medicine.
 

Contents

How to Use Part l
3
Road Maps A GuIded Tour
25
Action Monitoring and Forward Control
83
Adaptive Spike Coding
90
Computational Power
97
AnalogyBased Reasoning and Metaphor
106
Artifical lntelligence and Neural Networks
113
Associative Networks
117
Motivation
680
Motor Control Biological and Theoretical
687
Motor Paneru Generation
697
Motor Theories ot Perception
705
Muscle Models
711
Basic Neuron Types
719
Chemical and Electrical Synapses
725
Neuroanatomy in a Computational Perspective
733

Auditory Periphery and Cochiear Nacleus
127
Axonal Modeling
135
General Principles
144
Bayesian Methods and Neural Networks
151
Bayesian Networks
157
Biophysical Mechanisms in Neuronal Modeling
164
Biophysical Mosaic of the Neuron
170
BrainComputer lnterfaces
178
Cerebellam and Conditioning
187
Neural Plasticity
196
Chaos in Biological Systems
205
CogniIive Development
212
Psychology and Connectionism
219
Collicular Visuomotor Transformations for Gaze
226
Command Neurons and Command Systems
233
Competitive Queuing for Planning and Serial
241
Computing with Anractors
248
Conditioning
256
Consciousness Neural Models of
263
Contour and Surface Perception
271
Cooperative Phenomena
279
Cortical Hebbian Modules
285
Cortical Population Dynamics and Psychophysics
295
Crustacean SIomaIogasIric System
304
Databases for Neuroscience
312
Systems
470
Hcbbian Synuptie Plastieity
522
Hidden Markov Models
528
Spatial Models
539
1dentification and Control
547
lmaging the Motor Brain
556
lmaging the Visual Bratn
562
lndependent Component Analysis
569
1nformation Theory and Visual Plasticity
575
Aplysiu
583
Neural lmplications
593
Language Acquisition
600
The Mirror System Hypothesis
606
Language Processing
612
Theoretical Bounds
619
Learuing Network Topology
629
Lesioned Networks as Models of Neuropsychological
635
Localized Versus Distributed Representations
643
Locomotion Vertebrate
649
Markov Random Field Models in image Processing
657
Model Validation
666
Elementary Mechanisms
672
Neuroinformancs
741
Neurological and Psychiatric Disorders
751
Neuromodulation in lnvertebrate Nervous Systems
757
Neuromorphic VLSl Circuits and Systems
765
Neuropsychological lmpairments
773
Tools and Resources
779
NSL Neural Simulation Language
785
Object Recognition Neurophysiology
793
Ocular Dominance and Orientation Colamns
801
Olfactory Cortex
810
Optimally Theory in Linguistics
819
Optimization Principles in Motor Control
827
Oscillatory and Bursting Properties of Neurons
835
Pain Networks
843
Paneru Formation Biological
851
Paneru Formation Neural
859
Perception of TbreeDimensional Structure
868
Perspective on Neuron Model Complexity
877
Philosophical lssues in Brain Theory
886
Population Codes
893
Potential Fields and Neural Networks
901
Principal Component Analysis
910
Programmable Neurocomputing Systems
916
Prosthetics Neural
923
Parsuit Eye Movements
929
Radial Basis Function Networks
937
1mplications for Computational
945
Reading
951
Neurophysiological Modeling
960
Reinforcement Learuing in Motor Control
968
Retina
975
Robot Learuing
983
Rodent Head Direction System
990
Scratch Reflex
999
SelfOrganizing Feature Maps
1005
Sensor Fusion
1014
Sensorimotor Learuing
1020
Sequence Learuing
1027
Silicon Neurons
1034
SingleCell Models
1044
Somatosensory System
1053
Sound Localization and Binaural Processing
1061
Psycholinguistics
1068
Editorial Advisory Board
1239
lndex
1255
Copyright

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About the author (2003)

Michael A. Arbib is University Professor, Fletcher Jones Professor of Computer Science, and Professor of Biological Sciences, Biomedical Engineering, Electrical Engineering, Neuroscience, and Psychology at the University of Southern California. He is the author or editor of many books, including The Handbook of Brain Theory and Neural Networks (MIT Press, second edition 2002).

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