The handbook of brain theory and neural networks /

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Bibliographic Details
Edition:2nd ed.
Imprint:Cambridge, Mass. : MIT Press, ©2003.
©2003
Description:1 online resource (xvii, 1290 pages) : illustrations.
Language:English
Series:A Bradford book
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11177830
Hidden Bibliographic Details
Varying Form of Title:Brain theory and neural networks
Other authors / contributors:Arbib, Michael A.
ISBN:0262011972
9780262011976
9780262267267
0262267268
0585457409
9780585457406
0262307111
9780262307116
Digital file characteristics:data file
Notes:"A Bradford Book."
Includes bibliographical references and index.
Print version record.
Summary:"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.
Other form:Print version: Handbook of brain theory and neural networks. 2nd ed. Cambridge, Mass. : MIT Press, ©2003 0262011972
Table of Contents:
  • Post-Hebbian Learning Algorithms
  • Potential Fields and Neural Networks
  • Prefrontal Cortex in Temporal Organization of Action
  • Principal Component Analysis
  • Probabilistic Regularization Methods for Low-Level Vision
  • Programmable Neurocomputing Systems
  • Prosthetics, Motor Control
  • Prosthetics, Neural
  • Prosthetics, Sensory Systems
  • Pursuit Eye Movements
  • Q-Learning for Robots
  • Radial Basis Function Networks
  • Rate Coding and Signal Processing
  • Reaching Movements: Implications for Computational Models
  • Reactive Robotic Systems
  • Reading
  • Recurrent Networks: Learning Algorithms
  • Recurrent Networks: Neurophysiological Modeling
  • Reinforcement Learning
  • Reinforcement Learning in Motor Control
  • Respiratory Rhythm Generation
  • Retina
  • Robot Arm Control
  • Robot Learning
  • Robot Navigation
  • Rodent Head Direction System
  • Schema Theory
  • Scratch Reflex
  • Self-Organization and the Brain
  • Self-Organizing Feature Maps
  • Semantic Networks
  • Sensor Fusion
  • Sensorimotor Interactions and Central Pattern Generators
  • Sensorimotor Learning
  • Sensory Coding and Information Transmission
  • Sequence Learning
  • Short-Term Memory
  • Silicon Neurons
  • Simulated Annealing and Boltzmann Machines
  • Single-Cell Models
  • Sleep Oscillations
  • Somatosensory System
  • Somatotopy: Plasticity of Sensory Maps
  • Sound Localization and Binaural Processing
  • Sparse Coding in the Primate Cortex
  • Speech Processing: Psycholinguistics
  • Speech Production
  • Speech Recognition Technology
  • Spiking Neurons, Computation with
  • Spinal Cord of Lamprey: Generation of Locomotor Patterns
  • Statistical Mechanics of Generalization
  • Statistical Mechanics of Neural Networks
  • Statistical Mechanics of On-line Learning and Generalization
  • Statistical Parametric Mapping of Cortical Activity Patterns
  • Stereo Correspondence
  • Stochastic Approximation and Efficient Learning
  • Stochastic Resonance
  • Structured Connectionist Models
  • Support Vector Machines
  • Synaptic Interactions
  • Synaptic Noise and Chaos in Vertebrate Neurons
  • Synaptic Transmission
  • Synchronization, Binding and Expectancy
  • Synfire Chains
  • Synthetic Functional Brain Mapping
  • Systematicity and Generalizations in Connectionist Networks
  • Temporal Dynamics of Biological Synapses
  • Temporal Integration in Recurrent Microcircuits
  • Temporal Pattern Processing
  • Temporal Sequences: Learning and Global Analysis
  • Tensor Voting and Visual Segmentation
  • Thalamus
  • Universal Approximators
  • Unsupervised Learning with Global Objective Functions
  • Vapnik-Chervonenkis Dimension of Neural Networks
  • Vestibulo-Ocular Reflex
  • Visual Attention
  • Visual Cortex: Anatomical Structure and Models of Function
  • Visual Course Control in Flies
  • Visual Scene Perception, Neurophysiology
  • Visual Scene Segmentation
  • Visuomotor Coordination in Frog and Toad
  • Visuomotor Coordination in Salamander
  • Winner-Take-All Networks
  • Ying-Yang Learning.
  • pt. I. Background: The Elements of Brain Theory and Neural Networks
  • Introducing the Neuron
  • Levels and Styles of Analysis
  • Dynamics and Adaptation in Neural Networks
  • pt. II. Road Maps: A Guided Tour of Brain Theory and Neural Networks
  • The Meta-Map
  • Grounding Models of Neurons and Networks
  • Brain, Behavior, and Cognition
  • Psychology, Linguistics, and Artificial Intelligence
  • Biological Neurons and Networks
  • Dynamics and Learning in Artificial Networks
  • Sensory Systems
  • Motor Systems
  • Applications, Implementations, and Analysis
  • pt. III. Articles
  • Action Monitoring and Forward Control of Movements
  • Activity-Dependent Regulation of Neuronal Conductances
  • Adaptive Resonance Theory
  • Adaptive Spike Coding
  • Amplification, Attenuation, and Integration
  • Analog Neural Nets: Computational Power
  • Analog VLSI Implementations of Neural Networks
  • Analogy-Based Reasoning and Metaphor
  • Arm and Hand Movement Control
  • Artifical Intelligence and Neural Networks
  • Associative Networks
  • Auditory Cortex
  • Auditory Periphery and Cochlear Nucleus
  • Auditory Scene Analysis
  • Axonal Modeling
  • Axonal Path Finding
  • Backpropagation: General Principles
  • Basal Ganglia
  • Bayesian Methods and Neural Networks
  • Bayesian Networks
  • Biologically Inspired Robotics
  • Biophysical Mechanisms of Neuronal Modeling
  • Biophysical Mosaic of the Neuron
  • Brain Signal Analysis
  • Brain-Computer Interfaces
  • Canonical Neural Models
  • Cerebellum and Conditioning
  • Cerebellum and Motor Control
  • Cerebellum: Neural Plasticity
  • Chains of Oscillators in Motor and Sensory Systems
  • Chaos in Biological Systems
  • Chaos in Neural Systems
  • Cognitive Development
  • Cognitive Maps
  • Cognitive Modeling: Psychology and Connectionism
  • Collective Behavior of Coupled Oscillators
  • Collicular Visuomotor Transformations for Gaze Control
  • Color Perception
  • Command Neurons and Command Systems
  • Competitive Learning
  • Competitive Queuing for Planning and Serial Performance
  • Compositionality in Neural Systems
  • Computing with Attractors
  • Concept Learning
  • Conditioning
  • Connectionist and Symbolic Representations
  • Consciousness, Neural Models of
  • Constituency and Recursion in Language
  • Contour and Surface Perception
  • Convolutional Networks for Images, Speech, and Time Series
  • Cooperative Phenomena
  • Cortical Hebbian Modules
  • Cortical Memory
  • Cortical Population Dynamics and Psychophysics
  • Covariance Structural Equation Modeling
  • Crustacean Stomatogastric System
  • Data Clustering and Learning
  • Databases for Neuroscience
  • Decision Support Systems and Expert Systems
  • Dendritic Learning
  • Dendritic Processing
  • Dendritic Spines
  • Development of Retinotectal Maps
  • Developmental Disorders
  • Diffusion Models of Neuron Activity
  • Digital VLSI for Neural Networks
  • Directional Selectivity
  • Dissociations Between Visual Processing Modes
  • Dopamine, Roles of
  • Dynamic Link Architecture
  • Dynamic Remapping
  • Dynamics of Bifurcation in Neural Nets
  • Dynamics of Association and Recall
  • Echolocation: Cochleotopic and Computational Maps
  • EEG and MEG Analysis
  • Electrolocation
  • Embodied Cognition
  • Emotional Circuits
  • Energy Functionals for Neural Networks
  • Ensemble Learning
  • Equilibrium Point Hypothesis
  • Event-Related Potentials
  • Evolution and Learning in Neural Networks
  • Evolution of Artificial Neural Networks
  • Evolution of Genetic Networks
  • Evolution of the Ancestral Vertebrate Brain
  • Eye-Hand Coordination in Reaching Movements
  • Face Recognition: Neurophysiology and Neural Technology
  • Face Recognition: Psychology and Connectionism
  • Fast Visual Processing
  • Feature Analysis
  • Filtering, Adaptive
  • Forecasting
  • Gabor Wavelets and Statistical Pattern Recognition
  • Gait Transitions
  • Gaussian Processes
  • Generalization and Regularization in Nonlinear Learning Systems
  • GENESIS Simulation System
  • Geometrical Principles in Motor Control
  • Global Visual Pattern Extraction
  • Graphical Models: Parameter Learning
  • Graphical Models: Probabilistic Inference
  • Graphical Models: Structure Learning
  • Grasping Movements: Visuomotor Transformations
  • Habituation
  • Half-Center Oscillators Underlying Rhythmic Movements
  • Hebbian Learning and Neuronal Regulation
  • Hebbian Synaptic Plasticity
  • Helmholtz Machines and Sleep-Wake Learning
  • Hemispheric Interactions and Specialization
  • Hidden Markov Models
  • Hippocampal Rhythm Generation
  • Hippocampus: Spatial Models
  • Hybrid Connectionist/Symbolic Systems
  • Identification and Control
  • Imaging the Grammatical Brain
  • Imaging the Motor Brain
  • Imaging the Visual Brain
  • Imitation
  • Independent Component Analysis
  • Information Theory and Visual Plasticity
  • Integrate-and-Fire Neurons and Networks
  • Invertebrate Models of Learning: Aplysia and Hermissenda
  • Ion Channels: Keys to Neuronal Specialization
  • Kalman Filtering: Neural Implications
  • Laminar Cortical Architecture in Visual Perception
  • Language Acquisition
  • Language Evolution and Change
  • Language Evolution: The Mirror System Hypothesis
  • Language Processing
  • Layered Computation in Neural Networks
  • Learning and Generalization: Theoretical Bounds
  • Learning and Statistical Inference
  • Learning Network Topology
  • Learning Vector Quantization
  • Lesioned Networks as Models of Neuropsychological Deficits
  • Limb Geometry, Neural Control
  • Localized Versus Distributed Representations
  • Locomotion, Invertebrate
  • Locomotion, Vertebrate
  • Locust Flight: Components and Mechanisms in the Motor
  • Markov Random Field Models in Image Processing
  • Memory-Based Reasoning
  • Minimum Description Length Analysis
  • Model Validation
  • Modular and Hierarchical Learning Systems
  • Motion Perception: Elementary Mechanisms
  • Motion Perception: Navigation
  • Motivation
  • Motoneuron Recruitment
  • Motor Control, Biological and Theoretical
  • Motor Cortex: Coding and Decoding of Directional Operations
  • Motor Pattern Generation
  • Motor Primitives
  • Motor Theories of Perception
  • Multiagent Systems
  • Muscle Models
  • Neocognitron: A Model for Visual Pattern Recognition
  • Neocortex: Basic Neuron Types
  • Neocortex: Chemical and Electrical Synapses
  • Neural Automata and Analog Computational Complexity
  • Neuroanatomy in a Computational Perspective
  • Neuroethology, Computational
  • Neuroinformatics
  • Neurolinguistics
  • Neurological and Psychiatric Disorders
  • Neuromanifolds and Information Geometry
  • Neuromodulation in Invertebrate Nervous Systems
  • Neuromodulation in Mammalian Nervous Systems
  • Neuromorphic VLSI Circuits and Systems
  • NEURON Simulation Environment
  • Neuropsychological Impairments
  • Neurosimulation: Tools and Resources
  • NMDA Receptors: Synaptic, Cellular, and Network Models
  • NSL Neural Simulation Language
  • Object Recognition
  • Object Recognition, Neurophysiology
  • Object Structure, Visual Processing
  • Ocular Dominance and Orientation Columns
  • Olfactory Bulb
  • Olfactory Cortex
  • Optimal Sensory Encoding
  • Optimality Theory in Linguistics
  • Optimization, Neural
  • Optimization Principles in Motor Control
  • Orientation Selectivity
  • Oscillatory and Bursting Properties of Neurons
  • PAC Learning and Neural Networks
  • Pain Networks
  • Past Tense Learning
  • Pattern Formation, Biological
  • Pattern Formation, Neural
  • Pattern Recognition
  • Perception of Three-Dimensional Structure
  • Perceptrons, Adalines, and Backpropagation
  • Perspective on Neuron Model Complexity
  • Phase-Plane Analysis of Neural Nets
  • Philosophical Issues in Brain Theory and Connectionism
  • Photonic Implementations of Neurobiologically Inspired Networks
  • Population Codes.