In the 19th century, Spanish anatomists founded the theory of neurons. With the development of brain science, the biological characteristics of neurons and related electrical properties have been discovered. The advent of mathematical methods to simulate the actual human neural network in 1943 can be recognized as one of the notable landmarks. 63 years since then, deep neural networks were proposed and developed to simulate the structure of the human cerebral cortex. The emergence of deep learni
關於作者：
Wenfeng Wang is currently the leader of a CAS Light of West China Program XBBS-2014-16 and has been invited as the director of the Institute of Artificial Intelligence, the College of Brain-inspired Intelligence, Chinese Academy of Sciences to be set up in Nov. 2017. He also serves as a Distinguished Professor and the academic director of the R&D and Promotion center of artificial intelligence in the Robot Group of Harbin Institute of Technology, Hefei, China. His major research interests include functional analysis and intelligent algorithms with applications to video surveillance, ecologic modelling, geographic data mining and etc. He is the editor in chief of the book COMPUTER VISION AND MACHINE COGNITION in Chinese, which has been published by Beihang University in China. Wenfeng Wang is enthusiastic in academic communications in any way and he served as PC members and Session chairs of a series of international conferences associated with the brain-inspired intelligence and visual cognition, including the 2017 IEEE International Conference on Advanced Robotics and Mechatronics, the 2017 International Conference on Information Science, Control Engineering and the 3rd International Conference on Cognitive Systems and Information Processing and etc.Xiangyang Deng is currently a full assistant professor with the Institute of Information Fusion, Naval Aeronautical University, Yantai, China. His current research interests include video big data, deep learning and computational intelligence. Xiangyang Deng has rich experience in R & D management. He won 3 First Class Prizes and 2 Third Class Prizes of Military Scientific and Technological Progress Award. He published 9 papers about the topics in the past 3 years while 5 of them were indexed by SCI, EI. He contributed to a monograph SWARM INTELLIGENCE AND APPLICATIONS in Chinese, which was published by National Defense Industry Press. He has 2 patents and obtained 3 items of software copyright. Liang Ding is currently a full Professor with the State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China. His current research interests include intelligent control and robotics, including planetary rovers and legged robots. Dr. Ding was a recipient of the 2017 ISTVS Shne-Hata-Jurecka Award, the 2011 National Award for Technological Invention of China and the 200920132015 Award for Technological Invention of Heilongjiang Province. He received the Hiwin Excellent Doctoral Dissertation Award, the Best Conference Paper Award of IEEE ARM, and the Best Paper in Information Award of the 2012 IEEE ICIA Conference. Liang Ding is an influential scientist in intelligent control of robots and has published more than 120 authored or co-authored papers in journals and conference proceedings. Limin Zhang is currently a Full Professor and Tutor for Doctor with the Institute of Information Fusion, Naval Aeronautical University, Yantai, Shangdong, China. He was a senior visiting scholar at university college london UCL Modern Space Analysis and Research Center CASA from 2006 to 2007. His current research interests include signal processing, Complex system simulation and computational intelligence. More than 180 papers are published and 80 papers are indexed by SCI, EI. 2 monographs are published and 20 patents are applied and 6 were authorized. Limin Zhang has won two Second Class Prizes of National Scientific and Technological Progress Award and five First Class Prizes of Military Scientific and Technological Progress Award. He has been selected as outstanding scientists in national science and technology and millions of talents in engineering research field and he is enjoying special allowance from the State Council.
目錄：
1 Introduction of Brain Cognition 1
1.1Background1
1.2TheoryandMechanisms 2
1.2.1 Brain Mechanisms to Determine AttentionValue of Information in the Video 3
1.2.2 Swarm Intelligence to Implement the Above Biological
Mechanisms4
1.2.3 Models Framework for Social Computing in Object
Detection 5
1.2.4 Swarm Optimization and Classification of the Target
Impulse Responses 5
1.2.5 Performance of Integration Models on a Series of Challenging Real Data 6
1.3FromDetectiontoTracking 7
1.3.1 Brain Mechanisms for Select Important Objects to Track8
1.3.2 Mechanisms for Motion Tracking by Brain-Inspired
Robots 9
1.3.3 Sketch of Algorithms to Implement Biological
Mechanisms in the Model 10
1.3.4 Model Framework of the Brain-Inspired Compressive
Tracking and Future Applications 11
1.4Objectivesand Contributions 12
1.5 Outline of the Book 13
1.6 References 15
2 The VisionBrain Hypothesis17
2.1 Background 17
2.2 Attention Mechanisms19
2.2.1 Attention Mechanisms in Manned Driving 19
2.2.2 Attention Mechanisms in Unmanned Driving 20
2.2.3 Implications to the Accuracy of Cognition 21
2.2.4 Implications to the Speed of Response21
2.2.5 Future Treatment of Regulated Attention 22
2.3 Locally Compressive Cognition 23
2.3.1 Construction of a Compressive Attention 24
2.3.2 Locating Centroid of a Region of Interest 25
2.3.3 Parameters and Classifiers of the Cognitive System25
2.3.4 Treating Noise Data in the Cognition Process26
2.4 An Example of the VisionBrain 27
2.4.1 Illustration of the Cognitive System 29
2.4.2 Definition of a VisionBrain 31
2.4.3 Implementation of the VisionBrain32
References 34
3 Pheromone Accumulation and Iteration 41
3.1 Background 41
3.2 Improving the Classical Ant Colony Optimization 43
3.2.1 Model of Ants Moving Environment 44
3.2.2 Ant Colony System： A Classical Model44
3.2.3 The Pheromone Modification Strategy 46
3.2.4 Adaptive Adjustment of Involved Sub-paths 47
3.3 Experiment Tests of the SPB-ACO 48
3.3.1 Test of SPB Rule 48
3.3.2 Test of Comparing the SPB-ACO with ACS 51
3.4 ACO Algorithm with Pheromone Marks52
3.4.1 The Discussed Background Problem52
3.4.2 The Basic Model of PM-ACO 53
3.4.3 The Improvement of PM-ACO54
3.5 Two Coefficients of Ant Colonys Evolutionary Phases 55
3.5.1 Colony Diversity Coefficient 55
3.5.2 Elitist Individual Persistence Coefficient 56
3.6 Experimental Tests of PM-ACO 56
3.6.1 Tests in Problems Which Have Different Nodes 57
3.6.2 Relationship Between CDC and EIPC 57
3.6.3 Tests About the Best-Ranked Nodes58
3.7 Further Applications of the VisionBrain Hypothesis 59
3.7.1 Scene Understanding and Partition59
3.7.2 Efficiency of the VisionBrain in Face Recognition 63
References 67
4 Neural Cognitive Computing Mechanisms 69
4.1 Background 69
4.2 The Full State Constrained Wheeled Mobile Robotic System 71
4.2.1 System Description 71
4.2.2 Useful Technical Lemmas and Assumptions 72
4.2.3 NN Approximation 73
4.3 The Controller Design and Theoretical Analyses 74
4.3.1 Controller Design 74
4.3.2 Theoretic Analyses of the System Stability 78
4.4 Validation of the Nonlinear WMR System 81
4.4.1 Modeling Description of the Nonlinear WMR System81
4.4.2 Evaluating Performance of the Nonlinear
WMR System 81
4.5 System Improvement by Reinforced Learning85
4.5.1 Scheme to Enhance the Wheeled Mobile Robotic
System 85
4.5.2 Strategic Utility Function and Critic NN Design 89
4.6 Stability Analysis of the Enhanced WMR System91
4.6.1 Action NN Design Under the Adaptive Law 91
4.6.2 Boundedness Approach and the Tracking Errors
Convergence92
4.6.3 Simulation and Discussion of the WMR System 96
References 99
5 Integration and Scheduling of Core Modules105
5.1 Background 105
5.2 Theoretical Analyses 106
5.2.1 Preliminary Formulation 106
5.2.2 Three-Layer Architecture 109
5.3 Simulation and Discussion114
5.3.1 Brain-Inspired Cognition 114
5.3.2 Integrated Intelligence 119
5.3.3 Geospatial Visualization 126
5.4 The Future Research Priorities 131
5.4.1 WheelTerrain Interaction Mechanics of Rovers131
5.4.2 The Future Research Priorities 135
References 136
6 Brain-Inspired Perception, Motion and Control143
6.1 Background 143
6.2 Formulation of the Perceptive Information 145
6.2.1 Visual Signals in Cortical Information Processing
Pathways 145
6.2.2 Formulation of Cognition in the VisionBrain146
6.3 A Conceptual Model to Evaluate Cognition Efficiency 147
6.3.1 Computation of Attention Value and Warning Levels 147
6.3.2 Detailed Analysis on the Time Sequence Complexity 151
6.4 From Perception to Cognition and Decision 155
6.4.1 Brain-Inspired Motion and Control of Robotic
Systems 155
6.4.2 Layer Fusion of Sensors, Feature and Knowledge 155
6.5 The Major Principles to Implement a Real Brain Cognition158
6.5.1 Intelligence Extremes of the Robotic VisionBrain 158
6.5.2 Necessity to Set an up Limit for the Robotic
Intelligence 159
References 161
Index 165
內容試閱：
Brain-inspired intelligence has been proposed as a vision of the future for machine intelligence when Turing define intelligence and present experimental methods for judging whether a machine is intelligent. He wished that machines could work as well as the human brain. As an emerging branch in artificial intelligence, brain-inspired intelligence has attracted much attention. However, until now, there is not a widely-accepted theoretical framework of brain-inspired intelligence. It is still in debate whether brain-inspired intelligence should be recognized as a relatively independent branch of intelligence. One key problem is how to differentiate brain-inspired algorithms from other normal intelligent algorithms. The exact answer remains undetermined.In the 19th century, Spanish anatomists founded the theory of neurons. With the development of brain science, the biological characteristics of neurons and related electrical properties have been discovered. The advent of mathematical methods to simulate the actual human neural network in 1943 can be recognized as one of the notable landmarks. 63 years since then, deep neural networks were proposed and developed to simulate the structure of the human cerebral cortex. The emergence of deep learning has a great influence on the traditional artificial intelligence and enhanced the importance of brain-inspired intelligence in the whole field of artificial intelligence. This is a great dream into reality!Now researchers of machine intelligence are trying to review, summarize and further develop the past research achievements in speech, image and natural language processing from the perspective of deep Learning. This book reports our latest attempts in visual perception and also presents a better understanding of brain-inspired intelligence by establishing the vision brain hypothesis. We claim that Shu Li is a co-author of Chapter 4. Thanks to Professor Zongquan Deng, Professor Haibo Gao and some other colleagues for their significant contributions in originally published journal articles associated with Chapters 4 and 5 and much appreciation to Ruyi Zhou and Huaiguang Yang for their great efforts in explicit re-organization of these published articles.
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