Advances in Neural Networks -- ISNN 2010

1. Front Matter

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2. Neurophysiological Foundation

   1. Stimulus-Dependent Noise Facilitates Tracking Performances of Neuronal Networks

      • Longwen Huang, Si Wu

      Pages 1-8

   2. Range Parameter Induced Bifurcation in a Single Neuron Model with Delay-Dependent Parameters

      • Min Xiao, Jinde Cao

      Pages 9-16

   3. Messenger RNA Polyadenylation Site Recognition in Green Alga Chlamydomonas Reinhardtii

      • Guoli Ji, Xiaohui Wu, Qingshun Quinn Li, Jianti Zheng

      Pages 17-26

   4. A Study to Neuron Ensemble of Cognitive Cortex ISI Coding Represent Stimulus

      • Hu Yi, Xin Tian

      Pages 27-32

   5. STDP within NDS Neurons

      • Mario Antoine Aoun

      Pages 33-43

   6. Synchronized Activities among Retinal Ganglion Cells in Response to External Stimuli

      • Lei Xiao, Ying-Ying Zhang, Pei-Ji Liang

      Pages 44-50

   7. Novel Method to Discriminate Awaking and Sleep Status in Light of the Power Spectral Density

      • Lengshi Dai, You Wang, Haigang Zhu, Walter J. Freeman, Guang Li

      Pages 51-57

   8. Current Perception Threshold Measurement via Single Channel Electroencephalogram Based on Confidence Algorithm

      • You Wang, Yi Qiu, Yuping Miao, Guiping Dai, Guang Li

      Pages 58-62

   9. Electroantennogram Obtained from Honeybee Antennae for Odor Detection

      • You Wang, Yuanzhe Zheng, Zhiyuan Luo, Guang Li

      Pages 63-66

   10. A Possible Mechanism for Controlling Timing Representation in the Cerebellar Cortex

       • Takeru Honda, Tadashi Yamazaki, Shigeru Tanaka, Tetsuro Nishino

       Pages 67-76

3. Theory and Models

   1. Parametric Sensitivity and Scalability of k-Winners-Take-All Networks with a Single State Variable and Infinity-Gain Activation Functions

      • Jun Wang, Zhishan Guo

      Pages 77-85

   2. Extension of the Generalization Complexity Measure to Real Valued Input Data Sets

      • Iván Gómez, Leonardo Franco, José M. Jerez, José L. Subirats

      Pages 86-94

   3. A New Two-Step Gradient-Based Backpropagation Training Method for Neural Networks

      • Xuewen Mu, Yaling Zhang

      Pages 95-101

   4. A Large-Update Primal-Dual Interior-Point Method for Second-Order Cone Programming

      • Liang Fang, Guoping He, Zengzhe Feng, Yongli Wang

      Pages 102-109

   5. A One-Step Smoothing Newton Method Based on a New Class of One-Parametric Nonlinear Complementarity Functions for P ₀-NCP

      • Liang Fang, Xianming Kong, Xiaoyan Ma, Han Li, Wei Zhang

      Pages 110-117

   6. A Neural Network Algorithm for Solving Quadratic Programming Based on Fibonacci Method

      • Jingli Yang, Tingsong Du

      Pages 118-125

   7. A Hybrid Particle Swarm Optimization Algorithm Based on Nonlinear Simplex Method and Tabu Search

      • Zhanchao Li, Dongjian Zheng, Huijing Hou

      Pages 126-135

   8. Fourier Series Chaotic Neural Networks

      • Jia-hai Zhang, Chen-zhi Sun, Yao-qun Xu

      Pages 136-143

   9. Multi-objective Optimization of Grades Based on Soft Computing

      • Yong He

      Pages 144-151

This book and its sister volume collect refereed papers presented at the 7th Inter- tional Symposium on Neural Networks (ISNN 2010), held in Shanghai, China, June 6-9, 2010. Building on the success of the previous six successive ISNN symposiums, ISNN has become a well-established series of popular and high-quality conferences on neural computation and its applications. ISNN aims at providing a platform for scientists, researchers, engineers, as well as students to gather together to present and discuss the latest progresses in neural networks, and applications in diverse areas. Nowadays, the field of neural networks has been fostered far beyond the traditional artificial neural networks. This year, ISNN 2010 received 591 submissions from more than 40 countries and regions. Based on rigorous reviews, 170 papers were selected for publication in the proceedings. The papers collected in the proceedings cover a broad spectrum of fields, ranging from neurophysiological experiments, neural modeling to extensions and applications of neural networks. We have organized the papers into two volumes based on their topics. The first volume, entitled “Advances in Neural Networks- ISNN 2010, Part 1,” covers the following topics: neurophysiological foundation, theory and models, learning and inference, neurodynamics. The second volume en- tled “Advance in Neural Networks ISNN 2010, Part 2” covers the following five topics: SVM and kernel methods, vision and image, data mining and text analysis, BCI and brain imaging, and applications.

• agent-based modeling
• algorithms
• artificial neural networks
• biomedical applications
• classification
• complexity
• component analysis
• data analysis
• evolutionary algorithm
• neural computing
• neural controll
• neural optimization
• neurophsiological foundation
• performance
• reinforcement learning

• Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

  Liqing Zhang, Bao-Liang Lu

• Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water bay, Kowloon, Hong Kong, China

  James Kwok

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