Neural Information Processing: Research and Development

1. Front Matter

   Pages I-IX

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2. Architectures

   1. Scale Independence in the Visual System

      • Raul C. Muresan

      Pages 1-18

   2. Dynamic Neuronal Information Processing of Vowel Sounds in Auditory Cortex

      • Osamu Hoshino, Meihong Zheng

      Pages 19-38

   3. Convolutional Spiking Neural Network for Robust Object Detection with Population Code Using Structured Pulse Packets

      • Masakazu Matsugu, Katsuhiko Mori, Yusuke Mitarai

      Pages 39-55

   4. Networks constructed of neuroid elements capable of temporal summation of signals

      • Alexander A. Kharlamov, Vladimir V. Raevsky

      Pages 56-76

   5. Predictive synchrony organized by spike-based Hebbian learning with time-representing synfire activities

      • Katsunori Kitano, Tomoki Fukai

      Pages 77-93

   6. Improving Chow-Liu Tree Performance by Mining Association Rules

      • Kaizhu Huang, Irwin King, Michael R. Lyu, Haiqin Yang

      Pages 94-112

   7. A Reconstructed Missing Data-Finite Impulse Response Selective Ensemble (RMD-FSE) Network

      • Sirapat Chiewchanwattana, Chidchanok Lursinsap, Chee-Hung Henry Chu

      Pages 113-127

   8. Higher Order Multidirectional Associative Memory with Decreasing Energy Function

      • Hiromi Miyajima, Noritaka Shigei, Nobuaki Kiriki

      Pages 128-149

   9. Fast Indexing of Codebook Vectors Using Dynamic Binary Search Trees With Fat Decision Hyperplanes

      • Frederic Maire, Sebastian Bader, Frank Wathne

      Pages 150-166

3. Learning Algorithms

   1. On Some External Characteristics of Brain-like Learning and Some Logical Flaws of Connectionism

      • Asim Roy

      Pages 167-179

   2. Superlinear Learning Algorithm Design

      • Peter Géczy, Shiro Usui

      Pages 180-210

   3. Extension of Binary Neural Networks for Multi-class Output and Finite Automata

      • Narendra S. Chaudhari, Aruna Tiwari

      Pages 211-237

   4. A Memory-Based Reinforcement Learning Algorithm to Prevent Unlearning in Neural Networks

      • Seiichi Ozawa, Shigeo Abe

      Pages 238-255

   5. Structural Optimization of Neural Networks by Genetic Algorithm with Degeneration (GA^d)

      • Tetsuyuki Takahama, Setsuko Sakai, Yoshinori Isomichi

      Pages 256-277

   6. Adaptive Training for Combining Classifier Ensembles

      • Nayer M. Wanas, Mohamed S. Kamel

      Pages 278-293

   7. Combination Strategies for Finding Optimal Neural Network Architecture and Weights

      • Brijesh Verma, Ranadhir Ghosh

      Pages 294-319

4. Applications

   1. Biologically inspired recognition system for car detection from real-time video streams

      • Predrag Neskovic, David Schuster, Leon N. Cooper

      Pages 320-333

   2. Financial Time Series Prediction Using Non-fixed and Asymmetrical Margin Setting with Momentum in Support Vector Regression

      • Haiqin Yang, Irwin King, Laiwan Chan, Kaizhu Huang

      Pages 334-350

   3. A Method for Applying Neural Networks to Control of Nonlinear Systems

      • Jinglu Hu, Kotaro Hirasawa

      Pages 351-369

The field of neural information processing has two main objects: investigation into the functioning of biological neural networks and use of artificial neural networks to sol ve real world problems. Even before the reincarnation of the field of artificial neural networks in mid nineteen eighties, researchers have attempted to explore the engineering of human brain function. After the reincarnation, we have seen an emergence of a large number of neural network models and their successful applications to solve real world problems. This volume presents a collection of recent research and developments in the field of neural information processing. The book is organized in three Parts, i.e., (1) architectures, (2) learning algorithms, and (3) applications. Artificial neural networks consist of simple processing elements called neurons, which are connected by weights. The number of neurons and how they are connected to each other defines the architecture of a particular neural network. Part 1 of the book has nine chapters, demonstrating some of recent neural network architectures derived either to mimic aspects of human brain function or applied in some real world problems. Muresan provides a simple neural network model, based on spiking neurons that make use of shunting inhibition, which is capable of resisting small scale changes of stimulus. Hoshino and Zheng simulate a neural network of the auditory cortex to investigate neural basis for encoding and perception of vowel sounds.

• algorithm
• algorithms
• architecture
• artificial neural network
• control
• development
• genetic algorithms
• knowledge base
• learning
• network
• neural network
• neural networks
• optimization
• proving
• robot

• School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

  Jagath Chandana Rajapakse, Lipo Wang

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