Intelligence Science:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore
World Scientific
2012
|
| Schriftenreihe: | Series on intelligence science
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | 5.7.4 Energy distribution model 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 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 intelligence at the molecular, cell and behavior level. Cognitive science studies human mental activity, such as perception, learning, memory, thinking, consciousness etc. In order to implement machine intelligence, artificial intelligence attempts simulation, extension and expansion of human intelligence u |
| Beschreibung: | 1 Online-Ressource (682 pages) |
| ISBN: | 9789814360784 9814360783 |
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| 500 | |a 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 | ||
| 500 | |a 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 | ||
| 500 | |a 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 | ||
| 500 | |a 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 | ||
| 500 | |a 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 | ||
| 500 | |a 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 intelligence at the molecular, cell and behavior level. Cognitive science studies human mental activity, such as perception, learning, memory, thinking, consciousness etc. In order to implement machine intelligence, artificial intelligence attempts simulation, extension and expansion of human intelligence u | ||
| 650 | 4 | |a Computer algorithms | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Soft computing | |
| 650 | 4 | |a Computer science | |
| 650 | 7 | |a COMPUTERS / Enterprise Applications / Business Intelligence Tools |2 bisacsh | |
| 650 | 7 | |a COMPUTERS / Intelligence (AI) & Semantics |2 bisacsh | |
| 650 | 7 | |a Artificial intelligence |2 fast | |
| 650 | 4 | |a Informatik | |
| 650 | 4 | |a Künstliche Intelligenz | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 0 | 7 | |a Künstliche Intelligenz |0 (DE-588)4033447-8 |2 gnd |9 rswk-swf |
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Datensatz im Suchindex
| _version_ | 1804175282495553536 |
|---|---|
| any_adam_object | |
| author | Shi, Zhongzhi |
| author_facet | Shi, Zhongzhi |
| author_role | aut |
| author_sort | Shi, Zhongzhi |
| author_variant | z s zs |
| building | Verbundindex |
| bvnumber | BV042961106 |
| collection | ZDB-4-EBA ZDB-4-EBU |
| ctrlnum | (OCoLC)794328422 (DE-599)BVBBV042961106 |
| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| id | DE-604.BV042961106 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:13:49Z |
| institution | BVB |
| isbn | 9789814360784 9814360783 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028386974 |
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| spelling | Shi, Zhongzhi Verfasser aut Intelligence Science Singapore World Scientific 2012 1 Online-Ressource (682 pages) txt rdacontent c rdamedia cr rdacarrier Series on intelligence science 5.7.4 Energy distribution model 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 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 intelligence at the molecular, cell and behavior level. Cognitive science studies human mental activity, such as perception, learning, memory, thinking, consciousness etc. In order to implement machine intelligence, artificial intelligence attempts simulation, extension and expansion of human intelligence u Computer algorithms Machine learning Soft computing Computer science COMPUTERS / Enterprise Applications / Business Intelligence Tools bisacsh COMPUTERS / Intelligence (AI) & Semantics bisacsh Artificial intelligence fast Informatik Künstliche Intelligenz Artificial intelligence Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 s 1\p DE-604 http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=457235 Aggregator Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Shi, Zhongzhi Intelligence Science Computer algorithms Machine learning Soft computing Computer science COMPUTERS / Enterprise Applications / Business Intelligence Tools bisacsh COMPUTERS / Intelligence (AI) & Semantics bisacsh Artificial intelligence fast Informatik Künstliche Intelligenz Artificial intelligence Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)4033447-8 |
| title | Intelligence Science |
| title_auth | Intelligence Science |
| title_exact_search | Intelligence Science |
| title_full | Intelligence Science |
| title_fullStr | Intelligence Science |
| title_full_unstemmed | Intelligence Science |
| title_short | Intelligence Science |
| title_sort | intelligence science |
| topic | Computer algorithms Machine learning Soft computing Computer science COMPUTERS / Enterprise Applications / Business Intelligence Tools bisacsh COMPUTERS / Intelligence (AI) & Semantics bisacsh Artificial intelligence fast Informatik Künstliche Intelligenz Artificial intelligence Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Computer algorithms Machine learning Soft computing Computer science COMPUTERS / Enterprise Applications / Business Intelligence Tools COMPUTERS / Intelligence (AI) & Semantics Artificial intelligence Informatik Künstliche Intelligenz |
| url | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=457235 |
| work_keys_str_mv | AT shizhongzhi intelligencescience |