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.

Careful collection of recent research and developments in neural information processing Includes supplementary material: sn.pub/extras

Autorentext
Lipo Wang's research interests are in computational intelligence, i.e., neural networks, evolutionary computation, and fuzzy systems, with applications to multimedia, bioinformatics, and data mining. He has published over 50 journal publications, 14 books (authored/edited), 70 conference presentations, and 7 book chapters. He holds a U.S. patent on a neural network for image sequence processing. He is an Associate Editor / Editorial Board member for 7 international journals, including IEEE Transactions on Neural Networks, IEEE Transactions on Evolutionary Computation. He is Chair of the Emergent Technologies Technical Committee, IEEE Neural Networks Society. He is also the Founding Chair of both IEEE Engineering in Medicine and Biology Chapter Singapore and IEEE Neural Networks Chapter Singapore. Dr. Wang has been on the Governing Board of the Asia-Pacific Neural Network Assembly since 1999 and served as its President in 2002/2003. He will be Technical Program Co-Chair for the 2006 IEEE International Joint Conference on Neural Networks. He served as General Chair for 4 international conferences and as member of steering / advisory / organizing / program committees of over 60 international conferences since 1998. More details may be found at his home page (http://www.ntu.edu.sg/home/elpwang/). Xiuju Fu received the BS degree and the MS degree from Beijing Institute of Technology (China) in 1995 and 1999, respectively. She won the Best Student Paper Award at the 2001 Augsburg Data Mining Symposium, Germany. She received the PhD degree in Electronic Engineering from Nanyang Technological University (Singapore) in 2003. She is currently a research scientist at the Institute for High Performance Computing, Singapore. Her current research areas include: neural networks, genetic algorithms, data mining, classification, data dimensionality reduction, and rule extraction.

Klappentext

This monograph presents a careful collection of recent research and developments in the field of neural information processing. This includes investigations in the functioning and engineering of biological neural networks and applications of artificial neural networks for solving real-world problems. The book is organized in three parts, architectures, learning algorithms and applications, with a variety of different examples and case studies from different fields such as the visual system, object detection, financial time series prediction, the auditory cortex, and robot manipulator control.

Inhalt
1: Architectures.- Scale Independence in the Visual System.- Dynamic Neuronal Information Processing of Vowel Sounds in Auditory Cortex.- Convolutional Spiking Neural Network for Robust Object Detection with Population Code using Structured Pulse Packets.- Networks Constructed of Neuroid Elements Capable of Temporal Summation of Signals.- Predictive Synchrony Organized by Spike-Based Hebbian Learning with Time-Representing Synfire Activities.- Improving Chow-Liu Tree Performance by Mining Association Rules.- A Reconstructed Missing Data-Finite Impulse Response Selective Ensemble (RMD-FSE) Network.- Higher Order Multidirectional Associative Memory with Decreasing Energy Function.- Fast Indexing of Codebook Vectors Using Dynamic Binary Search Trees with Fat Decision Hyperplanes.- 2: Learning Algorithms.- On Some External Characteristics of Brain-like Learning and Some Logical Flaws of Connectionism.- Superlinear Learning Algorithm Design.- Extension of Binary Neural Networks for Multi-class Output and Finite Automata.- A Memory-Based Reinforcement Learning Algorithm to Prevent Unlearning in Neural Networks.- Structural Optimization of Neural Networks by Genetic Algorithm with Degeneration (GAd).- Adaptive Training for Combining Classifier Ensembles.- Combination Strategies for Finding Optimal Neural Network Architecture and Weights.- 3: Applications.- Biologically Inspired Recognition System for Car Detection from Real-Time Video Streams.- Financial Time Series Prediction Using Non-Fixed and Asymmetrical Margin Setting with Momentum in Support Vector Regression.- A Method for Applying Neural Networks to Control of Nonlinear Systesm.- Robot Manipulator Control via Recurrent Neural Networks.- Gesture Recognition Based on SOM Using Multiple Sensors.- Enhanced Phrase-Based Document Clustering Using Self-Organizing Map (SOM) Architectures.- Discovering Gene Regulatory Networks from Gene Expression Data with the Use of Evolving Connectionist Systems.- Experimental Analysis of Knowledge Based Multiagent Credit Assignment.- Implementation of Visual Tracking System Using Artificial Retina Chip and Shape Memory Alloy Actuator.