Neural Information Processing: Research and Development

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.

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.