The Handbook of Brain Theory and Neural Networks: Second Edition

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. The.Handbook.of.Brain.Theory.and.Neural.Networks......Page 1 Contents......Page 6 Preface to the Second Edition......Page 10 Preface to the First Edition......Page 12 How to Use This Book......Page 16 Part I: Background The Elements of Brain Theory and Neural Networks......Page 20 Part II: Road Maps A Guided Tour of Brain Theory and Neural Networks......Page 44 Part III: Articles......Page 100 Action Monitoring and Forward Control of Movements - Marc Jeannerod......Page 102 Activity-Dependent Regulation of NeuronalConductances - Larry F. Abbott and Eve Marder......Page 104 Adaptive Resonance Theory - Gail A. Carpenter and Stephen Grossberg......Page 106 Adaptive Spike Coding - Adrienne Fairhall and William Bialek......Page 109 Amplification, Attenuation, and Integration - H. Sebastian Seung......Page 113 Analog Neural Networks, Computational Power - Bhaskar DasGupta and Georg Schnitger......Page 116 Analog VLSI Implementations of Neural Networks - Paul Hasler and Jeff Dugger......Page 120 Analogy-Based Reasoning and Metaphor - Dedre Gentner and Arthur B. Markman......Page 125 Arm and Hand Movement Control - Stefan Schaal......Page 129 Artificial Intelligence and Neural Networks - John A. Barnden and Marcin Chady......Page 132 Auditory Cortex - Shihab A. Shamma......Page 141 Auditory Periphery and Cochlear Nucleus - David C. Mountain......Page 146 Auditory Scene Analysis - Guy J. Brown......Page 150 Axonal Modeling - Christof Koch and O ̈ jvind Bernander......Page 154 Axonal Path Finding - Geoffrey J. Goodhill......Page 159 Backpropagation: General Principles - Michael A. Arbib......Page 163 Basal Ganglia - Tony J. Prescott, Kevin Gurney, and Peter Redgrave......Page 166 Bayesian Methods and Neural Networks - David Barber......Page 170 Bayesian Networks - Judea Pearl and Stuart Russell......Page 176 Biologically Inspired Robotics - Noel E. Sharkey......Page 179 Biophysical Mechanisms in Neuronal Modeling - Lyle J. Graham......Page 183 Biophysical Mosaic of the Neuron - Lyle J. Graham and Raymond T. Kado......Page 189 Brain Signal Analysis - Jeng-Ren Duann, Tzyy-Ping Jung, and Scott Makeig......Page 194 Brain-Computer Interfaces - Jose ́ del R. Milla ́n......Page 197 Canonical Neural Models - Frank Hoppensteadt and Eugene M. Izhikevich......Page 200 Cerebellum and Conditioning - Jeffrey S. Grethe and Richard F. Thompson......Page 206 Cerebellum and Motor Control - Mitsuo Kawato......Page 209 Cerebellum: Neural Plasticity - Hervz Daniel and Francis Crepel......Page 214 Chains of Oscillators in Motor and Sensory Systems - Nancy Kopell and G. Bard Ermentrout......Page 220 Chaos in Biological Systems - Leon Glass......Page 224 Chaos in Neural Systems - Kazuyuki Aihara......Page 227 Editorial Advisory Board......Page 1258 Contributors......Page 1259 Index......Page 1274 The.Handbook.of.Brain.Theory.and.Neural.Networks 1 Contents 6 Preface to the Second Edition 10 Preface to the First Edition 12 How to Use This Book 16 Part I: Background The Elements of Brain Theory and Neural Networks 20 Part II: Road Maps A Guided Tour of Brain Theory and Neural Networks 44 Part III: Articles 100 Action Monitoring and Forward Control of Movements - Marc Jeannerod 102 Activity-Dependent Regulation of NeuronalConductances - Larry F. Abbott and Eve Marder 104 Adaptive Resonance Theory - Gail A. Carpenter and Stephen Grossberg 106 Adaptive Spike Coding - Adrienne Fairhall and William Bialek 109 Amplification, Attenuation, and Integration - H. Sebastian Seung 113 Analog Neural Networks, Computational Power - Bhaskar DasGupta and Georg Schnitger 116 Analog VLSI Implementations of Neural Networks - Paul Hasler and Jeff Dugger 120 Analogy-Based Reasoning and Metaphor - Dedre Gentner and Arthur B. Markman 125 Arm and Hand Movement Control - Stefan Schaal 129 Artificial Intelligence and Neural Networks - John A. Barnden and Marcin Chady 132 Auditory Cortex - Shihab A. Shamma 141 Auditory Periphery and Cochlear Nucleus - David C. Mountain 146 Auditory Scene Analysis - Guy J. Brown 150 Axonal Modeling - Christof Koch and O篓 jvind Bernander 154 Axonal Path Finding - Geoffrey J. Goodhill 159 Backpropagation: General Principles - Michael A. Arbib 163 Basal Ganglia - Tony J. Prescott, Kevin Gurney, and Peter Redgrave 166 Bayesian Methods and Neural Networks - David Barber 170 Bayesian Networks - Judea Pearl and Stuart Russell 176 Biologically Inspired Robotics - Noel E. Sharkey 179 Biophysical Mechanisms in Neuronal Modeling - Lyle J. Graham 183 Biophysical Mosaic of the Neuron - Lyle J. Graham and Raymond T. Kado 189 Brain Signal Analysis - Jeng-Ren Duann, Tzyy-Ping Jung, and Scott Makeig 194 Brain-Computer Interfaces - Jose麓 del R. Milla麓n 197 Canonical Neural Models - Frank Hoppensteadt and Eugene M. Izhikevich 200 Cerebellum and Conditioning - Jeffrey S. Grethe and Richard F. Thompson 206 Cerebellum and Motor Control - Mitsuo Kawato 209 Cerebellum: Neural Plasticity - Hervz Daniel and Francis Crepel 214 Chains of Oscillators in Motor and Sensory Systems - Nancy Kopell and G. Bard Ermentrout 220 Chaos in Biological Systems - Leon Glass 224 Chaos in Neural Systems - Kazuyuki Aihara 227 Editorial Advisory Board 1258 Contributors 1259 Index 1274 A new, dramatically updated edition of the classic resource on the constantly evolving fields of brain theory and neural networks.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. "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"--MIT CogNet

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 300articles 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.

For those fascinated with Neural Network Theory, this book is a comprehensive compendium of some of the best papers published in the subject. So far it is one of the best volumes in Neural Networks that I have seen, and a well thought paper compilation.