The handbook of brain theory and neural networks /
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| Edition: | 2nd ed. |
|---|---|
| Imprint: | Cambridge, Mass. : MIT Press, c2003. |
| Description: | xvii, 1290 p. : ill. ; 29 cm. |
| Language: | English |
| Subject: | Neural networks (Neurobiology) -- Handbooks, manuals, etc. Neural networks (Computer science) -- Handbooks, manuals, etc. Neural networks (Computer science) Neural networks (Neurobiology) Handbooks and manuals. |
| Format: | Print Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/4785279 |
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| 050 | 0 | 0 | |a QP363.3 |b .H36 2003 |
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| 245 | 0 | 4 | |a The handbook of brain theory and neural networks / |c edited by Michael A. Arbib ; editoral advisory board, Shun-Ichi Amari ... [et al.] ; editoral assistant, Prudence H. Arbib. |
| 246 | 3 | 0 | |a Brain theory and neural networks |
| 250 | |a 2nd ed. | ||
| 260 | |a Cambridge, Mass. : |b MIT Press, |c c2003. | ||
| 300 | |a xvii, 1290 p. : |b ill. ; |c 29 cm. | ||
| 336 | |a text |b txt |2 rdacontent |0 http://id.loc.gov/vocabulary/contentTypes/txt | ||
| 337 | |a unmediated |b n |2 rdamedia |0 http://id.loc.gov/vocabulary/mediaTypes/n | ||
| 338 | |a volume |b nc |2 rdacarrier |0 http://id.loc.gov/vocabulary/carriers/nc | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | 0 | |g Pt. I. |t Background: The Elements of Brain Theory and Neural Networks -- |t Introducing the Neuron -- |t Levels and Styles of Analysis -- |t Dynamics and Adaptation in Neural Networks -- |g Pt. II. |t Road Maps: A Guided Tour of Brain Theory and Neural Networks -- |t The Meta-Map -- |t Grounding Models of Neurons and Networks -- |t Brain, Behavior, and Cognition -- |t Psychology, Linguistics, and Artificial Intelligence -- |t Biological Neurons and Networks -- |t Dynamics and Learning in Artificial Networks -- |t Sensory Systems -- |t Motor Systems -- |t Applications, Implementations, and Analysis -- |g Pt. III. |t Articles -- |t Action Monitoring and Forward Control of Movements -- |t Activity-Dependent Regulation of Neuronal Conductances -- |t Adaptive Resonance Theory -- |t Adaptive Spike Coding -- |t Amplification, Attenuation, and Integration -- |t Analog Neural Nets: Computational Power -- |t Analog VLSI Implementations of Neural Networks -- |t Analogy-Based Reasoning and Metaphor -- |t Arm and Hand Movement Control -- |t Artificial Intelligence and Neural Networks -- |t Associative Networks -- |t Auditory Cortex -- |t Auditory Periphery and Cochlear Nucleus -- |t Auditory Scene Analysis -- |t Axonal Modeling -- |t Axonal Path Finding -- |t Backpropagation: General Principles -- |t Basal Ganglia -- |t Bayesian Methods and Neural Networks -- |t Bayesian Networks -- |t Biologically Inspired Robotics -- |t Biophysical Mechanisms of Neuronal Modeling -- |t Biophysical Mosaic of the Neuron -- |t Brain Signal Analysis -- |t Brain-Computer Interfaces -- |t Canonical Neural Models -- |t Cerebellum and Conditioning -- |t Cerebellum and Motor Control -- |t Cerebellum: Neural Plasticity -- |t Chains of Oscillators in Motor and Sensory Systems -- |t Chaos in Biological Systems -- |t Chaos in Neural Systems -- |t Cognitive Development -- |t Cognitive Maps -- |t Cognitive Modeling: Psychology and Connectionism -- |t Collective Behavior of Coupled Oscillators -- |t Collicular Visuomotor Transformations for Gaze Control -- |t Color Perception -- |t Command Neurons and Command Systems -- |t Competitive Learning -- |t Competitive Queuing for Planning and Serial Performance -- |t Compositionality in Neural Systems -- |t Computing with Attractors -- |t Concept Learning -- |t Conditioning -- |t Connectionist and Symbolic Representations -- |t Consciousness, Neural Models of -- |t Constituency and Recursion in Language -- |t Contour and Surface Perception -- |t Convolutional Networks for Images, Speech, and Time Series -- |t Cooperative Phenomena -- |t Cortical Hebbian Modules -- |t Cortical Memory -- |t Cortical Population Dynamics and Psychophysics -- |t Covariance Structural Equation Modeling -- |t Crustacean Stomatogastric System -- |t Data Clustering and Learning -- |t Databases for Neuroscience -- |t Decision Support Systems and Expert Systems -- |t Dendritic Learning -- |t Dendritic Processing -- |t Dendritic Spines -- |t Development of Retinotectal Maps -- |t Developmental Disorders -- |t Diffusion Models of Neuron Activity -- |t Digital VLSI for Neural Networks -- |t Directional Selectivity -- |t Dissociations Between Visual Processing Modes -- |t Dopamine, Roles of -- |t Dynamic Link Architecture -- |t Dynamic Remapping -- |t Dynamics of Bifurcation in Neural Nets -- |t Dynamics of Association and Recall -- |t Echolocation: Cochleotopic and Computational Maps -- |t EEG and MEG Analysis -- |t Electrolocation -- |t Embodied Cognition -- |t Emotional Circuits -- |t Energy Functionals for Neural Networks -- |t Ensemble Learning -- |t Equilibrium Point Hypothesis -- |t Event-Related Potentials -- |t Evolution and Learning in Neural Networks -- |t Evolution of Artificial Neural Networks -- |t Evolution of Genetic Networks -- |t Evolution of the Ancestral Vertebrate Brain -- |t Eye-Hand Coordination in Reaching Movements -- |t Face Recognition: Neurophysiology and Neural Technology -- |t Face Recognition: Psychology and Connectionism -- |t Fast Visual Processing -- |t Feature Analysis -- |t Filtering, Adaptive -- |t Forecasting -- |t Gabor Wavelets and Statistical Pattern Recognition -- |t Gait Transitions -- |t Gaussian Processes -- |t Generalization and Regularization in Nonlinear Learning Systems -- |t GENESIS Simulation System -- |t Geometrical Principles in Motor Control -- |t Global Visual Pattern Extraction -- |t Graphical Models: Parameter Learning -- |t Graphical Models: Probabilistic Inference -- |t Graphical Models: Structure Learning -- |t Grasping Movements: Visuomotor Transformations -- |t Habituation -- |t Half-Center Oscillators Underlying Rhythmic Movements -- |t Hebbian Learning and Neuronal Regulation -- |t Hebbian Synaptic Plasticity -- |t Helmholtz Machines and Sleep-Wake Learning -- |t Hemispheric Interactions and Specialization -- |t Hidden Markov Models -- |t Hippocampal Rhythm Generation -- |t Hippocampus: Spatial Models -- |t Hybrid Connectionist/Symbolic Systems -- |t Identification and Control -- |t Imaging the Grammatical Brain -- |t Imaging the Motor Brain -- |t Imaging the Visual Brain -- |t Imitation -- |t Independent Component Analysis -- |t Information Theory and Visual Plasticity -- |t Integrate-and-Fire Neurons and Networks -- |t Invertebrate Models of Learning: Aplysia and Hermissenda -- |t Ion Channels: Keys to Neuronal Specialization -- |t Kalman Filtering: Neural Implications -- |t Laminar Cortical Architecture in Visual Perception -- |t Language Acquisition -- |t Language Evolution and Change -- |t Language Evolution: The Mirror System Hypothesis -- |t Language Processing -- |t Layered Computation in Neural Networks -- |t Learning and Generalization: Theoretical Bounds -- |t Learning and Statistical Inference -- |t Learning Network Topology -- |t Learning Vector Quantization -- |t Lesioned Networks as Models of Neuropsychological Deficits -- |t Limb Geometry, Neural Control -- |t Localized Versus Distributed Representations -- |t Locomotion, Invertebrate -- |t Locomotion, Vertebrate -- |t Locust Flight: Components and Mechanisms in the Motor -- |t Markov Random Field Models in Image Processing -- |t Memory-Based Reasoning -- |t Minimum Description Length Analysis -- |t Model Validation -- |t Modular and Hierarchical Learning Systems -- |t Motion Perception: Elementary Mechanisms -- |t Motion Perception: Navigation -- |t Motivation -- |t Motoneuron Recruitment -- |t Motor Control, Biological and Theoretical -- |t Motor Cortex: Coding and Decoding of Directional Operations -- |t Motor Pattern Generation -- |t Motor Primitives -- |t Motor Theories of Perception -- |t Multiagent Systems -- |t Muscle Models -- |t Neocognitron: A Model for Visual Pattern Recognition -- |t Neocortex: Basic Neuron Types -- |t Neocortex: Chemical and Electrical Synapses -- |t Neural Automata and Analog Computational Complexity -- |t Neuroanatomy in a Computational Perspective -- |t Neuroethology, Computational -- |t Neuroinformatics -- |t Neurolinguistics -- |t Neurological and Psychiatric Disorders -- |t Neuromanifolds and Information Geometry -- |t Neuromodulation in Invertebrate Nervous Systems -- |t Neuromodulation in Mammalian Nervous Systems -- |t Neuromorphic VLSI Circuits and Systems -- |t NEURON Simulation Environment -- |t Neuropsychological Impairments -- |t Neurosimulation: Tools and Resources -- |t NMDA Receptors: Synaptic, Cellular, and Network Models -- |t NSL Neural Simulation Language -- |t Object Recognition -- |t Object Recognition, Neurophysiology -- |t Object Structure, Visual Processing -- |t Ocular Dominance and Orientation Columns -- |t Olfactory Bulb -- |t Olfactory Cortex -- |t Optimal Sensory Encoding -- |t Optimality Theory in Linguistics -- |t Optimization, Neural -- |t Optimization Principles in Motor Control -- |t Orientation Selectivity -- |t Oscillatory and Bursting Properties of Neurons -- |t PAC Learning and Neural Networks -- |t Pain Networks -- |t Past Tense Learning -- |t Pattern Formation, Biological -- |t Pattern Formation, Neural -- |t Pattern Recognition -- |t Perception of Three-Dimensional Structure -- |t Perceptrons, Adalines, and Backpropagation -- |t Perspective on Neuron Model Complexity -- |t Phase-Plane Analysis of Neural Nets -- |t Philosophical Issues in Brain Theory and Connectionism -- |t Photonic Implementations of Neurobiologically Inspired Networks -- |t Population Codes -- |
| 505 | 8 | 0 | |t Post-Hebbian Learning Algorithms -- |t Potential Fields and Neural Networks -- |t Prefrontal Cortex in Temporal Organization of Action -- |t Principal Component Analysis -- |t Probabilistic Regularization Methods for Low-Level Vision -- |t Programmable Neurocomputing Systems -- |t Prosthetics, Motor Control -- |t Prosthetics, Neural -- |t Prosthetics, Sensory Systems -- |t Pursuit Eye Movements -- |t Q-Learning for Robots -- |t Radial Basis Function Networks -- |t Rate Coding and Signal Processing -- |t Reaching Movements: Implications for Computational Models -- |t Reactive Robotic Systems -- |t Reading -- |t Recurrent Networks: Learning Algorithms -- |t Recurrent Networks: Neurophysiological Modeling -- |t Reinforcement Learning -- |t Reinforcement Learning in Motor Control -- |t Respiratory Rhythm Generation -- |t Retina -- |t Robot Arm Control -- |t Robot Learning -- |t Robot Navigation -- |t Rodent Head Direction System -- |t Schema Theory -- |t Scratch Reflex -- |t Self-Organization and the Brain -- |t Self-Organizing Feature Maps -- |t Semantic Networks -- |t Sensor Fusion -- |t Sensorimotor Interactions and Central Pattern Generators -- |t Sensorimotor Learning -- |t Sensory Coding and Information Transmission -- |t Sequence Learning -- |t Short-Term Memory -- |t Silicon Neurons -- |t Simulated Annealing and Boltzmann Machines -- |t Single-Cell Models -- |t Sleep Oscillations -- |t Somatosensory System -- |t Somatotopy: Plasticity of Sensory Maps -- |t Sound Localization and Binaural Processing -- |t Sparse Coding in the Primate Cortex -- |t Speech Processing: Psycholinguistics -- |t Speech Production -- |t Speech Recognition Technology -- |t Spiking Neurons, Computation with -- |t Spinal Cord of Lamprey: Generation of Locomotor Patterns -- |t Statistical Mechanics of Generalization -- |t Statistical Mechanics of Neural Networks -- |t Statistical Mechanics of On-line Learning and Generalization -- |t Statistical Parametric Mapping of Cortical Activity Patterns -- |t Stereo Correspondence -- |t Stochastic Approximation and Efficient Learning -- |t Stochastic Resonance -- |t Structured Connectionist Models -- |t Support Vector Machines -- |t Synaptic Interactions -- |t Synaptic Noise and Chaos in Vertebrate Neurons -- |t Synaptic Transmission -- |t Synchronization, Binding and Expectancy -- |t Synfire Chains -- |t Synthetic Functional Brain Mapping -- |t Systematicity and Generalizations in Connectionist Networks -- |t Temporal Dynamics of Biological Synapses -- |t Temporal Integration in Recurrent Microcircuits -- |t Temporal Pattern Processing -- |t Temporal Sequences: Learning and Global Analysis -- |t Tensor Voting and Visual Segmentation -- |t Thalamus -- |t Universal Approximators -- |t Unsupervised Learning with Global Objective Functions -- |t Vapnik-Chervonenkis Dimension of Neural Networks -- |t Vestibulo-Ocular Reflex -- |t Visual Attention -- |t Visual Cortex: Anatomical Structure and Models of Function -- |t Visual Course Control in Flies -- |t Visual Scene Perception, Neurophysiology -- |t Visual Scene Segmentation -- |t Visuomotor Coordination in Frog and Toad -- |t Visuomotor Coordination in Salamander -- |t Winner-Take-All Networks -- |t Ying-Yang Learning. |
| 650 | 0 | |a Neural networks (Neurobiology) |v Handbooks, manuals, etc. | |
| 650 | 0 | |a Neural networks (Computer science) |v Handbooks, manuals, etc. | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast |0 http://id.worldcat.org/fast/fst01036260 | |
| 650 | 7 | |a Neural networks (Neurobiology) |2 fast |0 http://id.worldcat.org/fast/fst01036271 | |
| 655 | 7 | |a Handbooks and manuals. |2 fast |0 http://id.worldcat.org/fast/fst01423877 | |
| 700 | 1 | |a Arbib, Michael A. |0 http://id.loc.gov/authorities/names/n50023543 |1 http://viaf.org/viaf/4952830 | |
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