Advances in Neural Networks - ISNN 2006

1. Front Matter

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2. Neurobiological Analysis

   1. The Ideal Noisy Environment for Fast Neural Computation

      • Si Wu, Jianfeng Feng, Shun-ichi Amari

      Pages 1-6

   2. How Does a Neuron Perform Subtraction? – Arithmetic Rules of Synaptic Integration of Excitation and Inhibition

      • Xu-Dong Wang, Jiang Hao, Mu-Ming Poo, Xiao-Hui Zhang

      Pages 7-14

   3. Stochastic Resonance Enhancing Detectability of Weak Signal by Neuronal Networks Model for Receiver

      • Jun Liu, Jian Wu, Zhengguo Lou, Guang Li

      Pages 15-20

   4. A Gaussian Dynamic Convolution Models of the FMRI BOLD Response

      • Huafu Chen, Ling Zeng, Dezhong Yao, Qing Gao

      Pages 21-26

   5. Cooperative Motor Learning Model for Cerebellar Control of Balance and Locomotion

      • Mingxiao Ding, Naigong Yu, Xiaogang Ruan

      Pages 27-33

   6. A Model of Category Learning with Attention Augmented Simplistic Prototype Representation

      • Toshihiko Matsuka

      Pages 34-40

   7. On the Learning Algorithms of Descriptive Models of High-Order Human Cognition

      • Toshihiko Matsuka, Arieta Chouchourelou

      Pages 41-49

   8. A Neural Model on Cognitive Process

      • Rubin Wang, Jing Yu, Zhi-kang Zhang

      Pages 50-59

3. Theoretical Analysis

   1. Approximation Bound of Mixture Networks in \(L^{p}_{\omega}\) Spaces

      • Zongben Xu, Jianjun Wang, Deyu Meng

      Pages 60-65

   2. Integral Transform and Its Application to Neural Network Approximation

      • Feng-jun Li, Zongben Xu

      Pages 66-71

   3. The Essential Approximation Order for Neural Networks with Trigonometric Hidden Layer Units

      • Chunmei Ding, Feilong Cao, Zongben Xu

      Pages 72-79

   4. Wavelets Based Neural Network for Function Approximation

      • Yong Fang, Tommy W. S. Chow

      Pages 80-85

   5. Passivity Analysis of Dynamic Neural Networks with Different Time-Scales

      • Alejandro Cruz Sandoval, Wen Yu

      Pages 86-92

   6. Exponential Dissipativity of Non-autonomous Neural Networks with Distributed Delays and Reaction-Diffusion Terms

      • Zhiguo Yang, Daoyi Xu, Yumei Huang

      Pages 93-99

   7. Convergence Analysis of Continuous-Time Neural Networks

      • Min-Jae Kang, Ho-Chan Kim, Farrukh A. Khan, Wang-Cheol Song, Jacek M. Zurada

      Pages 100-108

   8. Global Convergence of Continuous-Time Recurrent Neural Networks with Delays

      • Weirui Zhao, Huanshui Zhang

      Pages 109-114

   9. Global Exponential Stability in Lagrange Sense of Continuous-Time Recurrent Neural Networks

      • Xiaoxin Liao, Zhigang Zeng

      Pages 115-121

   10. Global Exponential Stability of Recurrent Neural Networks with Time-Varying Delay

       • Yi Shen, Meiqin Liu, Xiaodong Xu

       Pages 122-128

   11. New Criteria of Global Exponential Stability for a Class of Generalized Neural Networks with Time-Varying Delays

       • Gang Wang, Hua-Guang Zhang, Chong-Hui Song

       Pages 129-134

This book and its sister volumes constitute the Proceedings of the Third International Symposium on Neural Networks (ISNN 2006) held in Chengdu in southwestern China during May 28–31, 2006. After a successful ISNN 2004 in Dalian and ISNN 2005 in Chongqing, ISNN became a well-established series of conferences on neural computation in the region with growing popularity and improving quality. ISNN 2006 received 2472 submissions from authors in 43 countries and regions (mainland China, Hong Kong, Macao, Taiwan, South Korea, Japan, Singapore, Thailand, Malaysia, India, Pakistan, Iran, Qatar, Turkey, Greece, Romania, Lithuania, Slovakia, Poland, Finland, Norway, Sweden, Demark, Germany, France, Spain, Portugal, Belgium, Netherlands, UK, Ireland, Canada, USA, Mexico, Cuba, Venezuela, Brazil, Chile, Australia, New Zealand, South Africa, Nigeria, and Tunisia) across six continents (Asia, Europe, North America, South America, Africa, and Oceania). Based on rigorous reviews, 616 high-quality papers were selected for publication in the proceedings with the acceptance rate being less than 25%. The papers are organized in 27 cohesive sections covering all major topics of neural network research and development. In addition to the numerous contributed papers, ten distinguished scholars gave plenary speeches (Robert J. Marks II, Erkki Oja, Marios M. Polycarpou, Donald C. Wunsch II, Zongben Xu, and Bo Zhang) and tutorials (Walter J. Freeman, Derong Liu, Paul J. Werbos, and Jacek M. Zurada).

• Hardware
• algorithm
• algorithms
• bioinformatics
• classification
• communication
• computer
• computer vision
• image processing
• learning
• modeling
• neuroinformatics
• optimization
• robot
• system modeling
• algorithm analysis and problem complexity

From the reviews:

"It addresses the domain of artificial Intelligence (AI) and presents state-of-the-art research, development, and education in the field. … a useful resource for scientists and educators interested in the field of NN systems … . gives the reader a sense of the applications and challenges of the filed, and provides an extensive bibliography for further investigation. … The collection offers a broad perspective on a topic that is widely discussed and underlines many of its applications. This makes it highly recommendable to the AI community." (M. Caramihai, Computing Reviews, December, 2006)

• Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

  Jun Wang

• Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China

  Zhang Yi

• Department of Electrical Engineering, University of Louisville, Louisville, U.S.A

  Jacek M. Zurada

• Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, Shanghai, China

  Bao-Liang Lu

• School of Electrical and Electronic Engineering, University of Manchester, UK

  Hujun Yin

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