Details

Subject(s)

• Computational intelligence [Browse]
• Cognition [Browse]

Series

• Series on intelligence science ; vol. 31. [More in this series]
• Series on intelligence science ; Vol. 31 [More in this series]

Summary note

"Mind computation is a hot topic of intelligence science. It is explored by computing to explain the theoretical basis of human intelligence. Through long-term research, a mind model CAM (Consciousness and Memory) is proposed, which provides a general framework for brain-like intelligence and brain-like intelligent systems. This novel book centers on mind model CAM, systematically discusses the theoretical basis of mind computation in nine chapters. Because of its advanced progresses on brain-like intelligence, it is useful as a primary reference volume for professionals and graduate students in intelligence science, cognitive science and artificial intelligence."--Publisher's website.

Bibliographic references

Includes bibliographical references and index.

Contents

• Machine generated contents note: 1.1.Mind
• 1.2.Philosophy Issues of Mind
• 1.3.Biological Basis of Mind
• 1.4.Intelligence Science Issues of Mind
• 1.4.1.Working mechanism of brain neural network
• 1.4.2.Perception process and perception theory
• 1.4.3.Memory
• 1.4.4.Learning
• 1.4.5.Cognitive mechanisms of language processing
• 1.4.6.Cognitive mechanisms of thought
• 1.4.7.Intelligence development
• 1.4.8.Emotion system
• 1.4.9.Consciousness
• 1.4.10.Mind model
• 1.5.The Structure of Mind
• 1.6.The Modularity of Mind
• 1.7.The Society of Mind
• 1.8.Automata Theory
• 1.8.1.Overview
• 1.8.1.1.Abstract theory
• 1.8.1.2.Structure theory
• 1.8.1.3.Self-organization theory
• 1.8.2.Finite state automata (FSA)
• 1.8.3.Probabilistic automata
• 1.8.4.Cellular automata (CA)
• 1.9.Turing Machine
• 1.10.Computational Theory of Mind
• 2.1.Introduction
• 2.2.Criteria of Mind Modeling
• 2.2.1.Agile action
• 2.2.2.Real-time
• 2.2.3.Adaptive behavior
• Note continued: 2.2.4.Large-scale knowledge base
• 2.2.5.Dynamic action
• 2.2.6.Knowledge integration
• 2.2.7.Natural language
• 2.2.8.Consciousness
• 2.2.9.Learning
• 2.2.10.Development
• 2.2.11.Evolution
• 2.2.12.Brain
• 2.3.Cognitive Mind Modeling
• 2.3.1.Physical symbol system
• 2.3.2.ACT-R
• 2.3.3.Soar
• 2.4.Connectionism-based Mind Modeling
• 2.4.1.Connectionism
• 2.4.1.1.Large-scale parallel processing
• 2.4.1.2.Content-based addressing
• 2.4.1.3.Distributed storage
• 2.4.1.4.Adaptability
• 2.4.1.5.Fault tolerance
• 2.4.2.Adaptive Resonance Theory
• 2.5.Agent Mind Modeling
• 2.6.CAM Architecture
• 2.7.CAM Cognitive Cycle
• 2.7.1.Perception phase
• 2.7.2.Motivation phase
• 2.7.3.Action planning phase
• 3.1.Overview
• 3.2.Basis of the DDL
• 3.2.1.Notations
• 3.2.2.Semantics of the DDL
• 3.2.3.Inference in the DDL
• 3.3.Long-term Memory
• 3.3.1.Semantic memory
• 3.3.1.1.Hierarchical network model
• Note continued: 3.3.1.2.Spreading activation model
• 3.3.1.3.Set theoretic model
• 3.3.1.4.Feature comparison model
• 3.3.1.5.Human association memory
• 3.3.1.6.ELINOR model
• 3.3.1.7.Ontology memory model
• 3.3.2.Episodic memory
• 3.3.3.Procedural memory
• 3.4.Short-term Memory
• 3.4.1.Short-term memory encoding
• 3.4.2.Information extraction
• 3.4.2.1.The classical research by Sternberg
• 3.4.2.2.Direct access model
• 3.4.2.3.Dual model
• 3.4.3.Short-term memory in CAM
• 3.4.3.1.Belief
• 3.4.3.2.Target
• 3.4.3.3.Intention
• 3.5.Working Memory
• 3.5.1.Models of working memory
• 3.5.2.Working memory and reasoning
• 3.5.3.Neural mechanism of working memory
• 3.6.Theory of Forgetting
• 3.7.Physiological Mechanism of Memory
• 3.8.Theory of Memory-Prediction
• 3.8.1.Constant characterization
• 3.8.2.Structure of cerebral cortex
• 3.8.3.How does the cerebral cortex work
• 4.1.Overview
• 4.1.1.Base elements of consciousness
• Note continued: 4.1.2.The attribute of consciousness
• 4.2.Theory of Consciousness
• 4.2.1.The theater of consciousness
• 4.2.2.Reductionism
• 4.2.3.Theory of neuronal group selection
• 4.2.4.Quantum theories
• 4.2.5.Block model of consciousness
• 4.2.6.Information integration theory
• 4.3.Attention
• 4.3.1.Attention functions
• 4.3.2.Selective attention
• 4.3.3.Attention distribution
• 4.3.4.Attention system
• 4.4.Metacognition
• 4.4.1.Metacognitive knowledge
• 4.4.2.Metacognitive experience
• 4.4.3.Metacognitive monitoring
• 4.4.4.Metacognition training
• 4.5.Motivation
• 4.5.1.Overview
• 4.5.2.Theory of motivation
• 4.6.Consciousness Subsystem in CAM
• 4.6.1.Awareness module
• 4.6.2.Attention module
• 4.6.3.Global workspace module
• 4.6.4.Motivation module
• 4.6.5.Metacognitive module
• 4.6.6.Introspective learning module
• 5.1.Visual Cortex Area
• 5.2.Visual Computation Theory
• 5.2.1.Mares visual computation theory
• Note continued: 5.2.2.Gestalt vision theory
• 5.2.3.Dual visual pathway
• 5.2.4.Topological vision theory
• 5.3.Feature Binding
• 5.3.1.Temporal synchronization theory
• 5.3.2.Formal model of feature binding
• 5.3.3.Feature integration theory
• 5.3.4.Neural network model
• 5.4.Object Recognition
• 5.4.1.Visual representation
• 5.4.2.Object low-level feature extraction
• 5.4.3.Relation encoding
• 5.4.4.Learning recognition network
• 5.4.5.Link search
• 5.5.Visual Space Cognition
• 5.6.Visual Effective Coding
• 6.1.The Neural Structure of Motor Control
• 6.2.Motor Cortex
• 6.3.The Basal Ganglia
• 6.4.Motor Control Pathway
• 6.5.EEG Signal Analysis
• 6.5.1.EEG signal sorting
• 6.5.2.EEG signal analytical method
• 6.6.Encoding Motor
• 6.6.1.Overview
• 6.6.2.Entropy encoding theory
• 6.6.3.Bayesian neuronal population encoding
• 6.6.4.Bayesian neuronal population decoding
• 6.7.Brain-Computer Interface
• 6.7.1.Overview
• Note continued: 6.7.2.Brain-Computer interface technology
• 6.7.3.P300 Brain-computer interface system
• 6.8.Brain-Computer Integration
• 7.1.Mental Lexicon
• 7.2.Perceptual Analysis of Language Input
• 7.2.1.Spoken language input
• 7.2.2.Speech coding
• 7.2.3.Rhythm perception
• 7.2.4.Written input
• 7.2.5.Word recognition
• 7.2.6.Speech generation
• 7.3.Chomsky's Formal Grammar
• 7.3.1.Phrase structure grammar
• 7.3.2.Context-sensitive grammar
• 7.3.3.Context-free grammar (CFG)
• 7.3.4.Regular grammar
• 7.4.Augmented Transition Networks
• 7.5.Conceptual Dependency Theory
• 7.6.Language Understanding
• 7.6.1.Overview
• 7.6.2.Development stage
• 7.6.3.Rule-based analysis method
• 7.6.4.Statistical model based on Corpus
• 7.6.5.Machine learning method
• 7.7.Functional Area of Brain
• 7.7.1.Classical function area
• 7.7.2.Semantic-related functional area
• 7.7.3.Phonological-related functional area
• 7.7.4.Spelling-related functional area
• Note continued: 7.7.5.Bilingual brain functional areas
• 7.8.Neural Model of Language Understanding
• 7.8.1.Aphasia
• 7.8.2.Classical localization model
• 7.8.3.Memory-integration-control model
• 8.1.Introduction
• 8.2.Reinforcement Learning
• 8.2.1.RL model
• 8.2.2.Q learning
• 8.2.3.Partial observation reinforcement learning
• 8.2.4.Motivated reinforcement learning (MRL)
• 8.2.5.Reinforcement learning of Soar system
• 8.3.Deep Learning
• 8.3.1.Introduction
• 8.3.2.Human brain visual mechanism
• 8.3.3.Autoencoder
• 8.3.4.Restricted Boltzmann machine
• 8.3.5.Deep belief networks
• 8.3.6.Convolutional neural networks
• 8.4.Introspective Learning
• 8.4.1.Introduction
• 8.4.2.General model of introspection learning
• 8.4.3.Meta-reasoning of introspection learning
• 8.4.4.Failure classification
• 8.4.5.Case-based reasoning in the introspective process
• 8.5.Brain Cognitive Data Analysis
• 8.5.1.Brain function imaging
• Note continued: 8.5.2.Brain nerve semantics
• 8.5.3.Brain functional connectivity analysis
• 9.1.Overview
• 9.2.Blue-Brain Project
• 9.2.1.Brain neural network
• 9.2.2.Cerebral cortex model
• 9.2.3.Super computing simulation
• 9.3.Human Brain Project of the EU
• 9.3.1.Introduction
• 9.3.2.Spike-timing-dependent plasticity
• 9.3.3.Unified brain model
• 9.4.The US Brain Project
• 9.4.1.Human connectome project
• 9.4.2.MoNETA
• 9.4.3.Neurocore chip
• 9.4.4.HP memristor
• 9.5.Brain Simulation System Spaun
• 9.6.Neuromorphic Chip
• 9.6.1.The development history of neuromorphic chip
• 9.6.2.IBM's TrueNorth neuromorphic system
• 9.6.3.British SpiNNaker
• 9.7.Development Roadmap of Intelligence Science
• 9.7.1.Elementary brain-like computing
• 9.7.2.Advanced brain-like computing
• 9.7.3.Super-brain computing.

Show 236 more Contents items

ISBN

• 9789813145801 ((hc ; : alk. paper))
• 9813145803 ((hc ; : alk. paper))

LCCN

2016032985

OCLC

953792598

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