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...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore :
World Scientific,
2012.
|
| Schriftenreihe: | Series on intelligence science.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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: | 5.7.4 Energy distribution model. |
| Beschreibung: | 1 online resource (682 pages) |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 9789814360784 9814360783 |
Internformat
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| 490 | 1 | |a Series on Intelligence Science | |
| 505 | 0 | |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. | |
| 505 | 8 | |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. | |
| 505 | 8 | |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. | |
| 505 | 8 | |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. | |
| 505 | 8 | |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 5.7.4 Energy distribution model. | ||
| 520 | |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. | ||
| 588 | 0 | |a Print version record. | |
| 504 | |a Includes bibliographical references. | ||
| 650 | 0 | |a Artificial intelligence. |0 http://id.loc.gov/authorities/subjects/sh85008180 | |
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| 776 | 0 | 8 | |i Print version: |a Shi, Zhongzhi. |t Intelligence Science. |d Singapore : World Scientific, ©2012 |z 9789814360777 |
| 830 | 0 | |a Series on intelligence science. |0 http://id.loc.gov/authorities/names/no2011110846 | |
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Datensatz im Suchindex
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| author | Shi, Zhongzhi |
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| contents | 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. |
| ctrlnum | (OCoLC)794328422 |
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| 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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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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">1.5 Research Roadmap of Intelligence Science1. 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| id | ZDB-4-EBA-ocn794328422 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:18:24Z |
| institution | BVB |
| isbn | 9789814360784 9814360783 |
| language | English |
| oclc_num | 794328422 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (682 pages) |
| psigel | ZDB-4-EBA |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | World Scientific, |
| record_format | marc |
| series | Series on intelligence science. |
| series2 | Series on Intelligence Science |
| spelling | Shi, Zhongzhi. Intelligence Science. Singapore : World Scientific, 2012. 1 online resource (682 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Series on Intelligence Science 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. 5.7.4 Energy distribution 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. Print version record. Includes bibliographical references. Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Intelligence artificielle. artificial intelligence. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Artificial intelligence fast Print version: Shi, Zhongzhi. Intelligence Science. Singapore : World Scientific, ©2012 9789814360777 Series on intelligence science. http://id.loc.gov/authorities/names/no2011110846 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=457235 Volltext |
| spellingShingle | Shi, Zhongzhi Intelligence Science. Series on intelligence science. 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. Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Intelligence artificielle. artificial intelligence. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Artificial intelligence fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85008180 https://id.nlm.nih.gov/mesh/D001185 |
| 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 | Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Intelligence artificielle. artificial intelligence. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Artificial intelligence fast |
| topic_facet | Artificial intelligence. Artificial Intelligence Intelligence artificielle. artificial intelligence. COMPUTERS Enterprise Applications Business Intelligence Tools. COMPUTERS Intelligence (AI) & Semantics. Artificial intelligence |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=457235 |
| work_keys_str_mv | AT shizhongzhi intelligencescience |