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Intelligence Science.
Intelligence Science is an interdisciplinary subject dedicated to joint research on basic theory and technology of intelligence by brain science, cognitive science, artificial intelligence and others. Brain science explores the essence of brain research on the principle and model of natural intellig...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Singapore :
World Scientific,
2012.
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| Colección: | Series on intelligence science.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Preface; Acknowledgement; Contents; Chapter 1 Introduction; 1.1 The Dream of Mankind; 1.2 The Rise of Intelligence Science; 1.3 Research Contents; 1.3.1 Basic process of neural activity; 1.3.2 Synaptic plasticity; 1.3.3 Perceptual representation and feature binding; 1.3.4 Coding and retrieval of memory; 1.3.5 Linguistic cognition; 1.3.6 Learning; 1.3.7 Thought; 1.3.8 Emotion; 1.3.9 Nature of consciousness; 1.3.10 Mind modeling; 1.4 Research Methods; 1.4.1 Behavioral experiments; 1.4.2 Brain imaging; 1.4.3 Computational modeling; 1.4.4 Neurobiological methods; 1.4.5 Simulation.
- 1.5 Research Roadmap of Intelligence Science1. Short-term goal (2010-2020); 2. Medium-term goal (2020-2035); 3. Long-term goal (2035-2050); Chapter 2 Foundation of Neurophysiology; 2.1 Brain; 2.2 Nervous Tissues; 2.2.1 Basal composition of neuron; 2.2.2 Classification of neurons; 2.2.3 Neuroglial cells; 2.3 Synaptic Transmission; 2.3.1 Chemical synapse; 2.3.2 Electrical synapse; 2.3.3 Mechanism of synaptic transmission; 2.4 Neurotransmitter; 2.4.1 Acetylcholine; 2.4.2 Catecholamines; 2.4.3 5-hydroxytryptamine; 2.4.4 Amine acid and oligopeptide; 2.4.5 Nitric oxide; 2.4.6 Receptor.
- 2.5 Transmembrane Signal Transduction2.5.1 Transducin; 2.5.2 The second messenger; 2.6 Resting Membrane Potential; 2.7 Action Potential; 2.8 Ion Channels; 2.9 The Nervous System; 2.9.1 The second messenger; 2.9.2 Peripheral nervous system; 2.10 Cerebral Cortex; Chapter 3 Neural Computation; 3.1 Overview; 3.2 Neuron Model; 3.3 Back-Propagation Learning Algorithm; 3.2.1 Back propagation principle; 3.2.2 Back propagation algorithm; 3.2.4 Advantages and disadvantages of back-propagation network; 3.4 Neural Network Ensemble; 3.4.1 Generation of conclusion; 3.4.2 Generation of individual.
- 3.5 Bayesian Linking Field Model3.5.1 Related works; 3.5.2 Noisy neuron firing strategy; 3.5.3 Bayesian coupling of inputs; 3.5.4 Competition among neurons; 3.6 Neural Field Model; 3.7 Nrural Column Model; Chapter 4 Mind Model; 4.1 Introduction; 4.2 The Physical Symbol System; 4.3 ACT-R Model; 4.3.1 Brief history; 4.3.2 The ACT-R architecture; 4.3.3 ACT-R works; (1) Modules; (2) Buffers; (3) Pattern Matcher; 4.3.4 Applications of ACT-R; 4.4 SOAR; 4.5 Society of Mind; 4.6 CAM Model; 4.7 Synergetics; 4.8 Dynamical System Theory; Chapter 5 Perceptual Cognition.
- 5.1 Dialectic Process of Understanding5.2 Sensation; 5.3 Perception; 5.4 Combination of Perception; 1. Approaching combination; 2. Similar combination; 3. Combination of the good figure; 5.5 Perception Theories; 5.5.1 Constructing theory; 5.5.2 Gestalt theory; 5.5.3 Movement theory; 5.5.4 Gibson's ecology theory; 5.6 Representation; 1. Intuitivity; 2. Generality; 3. Representation happens on paths of many kinds of feelings; 4. Role of representation in thinking; 5.7 Attention in the Perceptual Cognition; 5.7.1 Filter model; 5.7.2 Decay model; 5.7.3 Response selection model.