Brain-Like Computing and Intelligent Information Systems
Published on 1. January 1998
Book
Hardback
400 pages
978-981-3083-58-5 (ISBN)
Description
This book introduces and defines a new area in computer science and artificial intelligence called brain-like computing. Brain-like computing combines traditional computational techniques with computational and cognitive ideas, principles and models inspired by the brain for building information systems used in our every day life. Image and speech processing, creative planning and design, adaptive control, knowledge acquisition and database mining, are only a few areas where brain-like computing is applied. The more is known about the functionality of the brain the more intelligent the information systems will become. The book comprises chapters written by well known specialists in this field and can be used by scientists and graduate students from different areas, as well as by wider audience of people interested in the present and future development of this exciting area.
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Content
Part I Computer Vision and Image Processing: Active Vision: Neural Network Models/Kunihiko Fukushima; Image Recognition by Brains and Machines/Eric Postma, Jaap van den Herik, and Patrick Hudson; The Properties and Training of a Neural Network Based, Universal Window Filter Developedfor Image Processing Tasks/Ralph H. Pugmire, Robert M. Hodgson and Robert I. Chaplin Part II Speech Recognition and Language Processing: A Computational Model of the Auditory Pathway to the Superior Colliculus/Raymond J. W. Wang and Marwan Jabri; A Framework for Intelligent ,Conscious" Machines Utilising, Fuzzy Neural Networks and Spatio-Temporal Maps and a Case Study of Multilingual Speech Recognition/Nikola Kasabov Part III Dynamic Systems: Statistical and Chaos Modelling. Blind Source Separation; Noise-Mediated Cooperative Behavior in Integrate-Fire Models of Neuron Dynamics/Adi R. Bulsara; Blind Source Separation Mathematical Foundations/ Shun-ichi Amari; Neural Independent Component Analysis Approaches and Applications/Erkki Oja, Juha Karhunen, Aapo Hyvärinen, Ricardo Vigario and Jarmo Hurri; General Regression Techniques Based on Spherical Kernel Functions for Intelligent Processing/Anthony Zankich and Yianny Attikiouzel; Chaos and Fractal Analysis of Irregular Time Series Embedded in a Connectionist Structure/ Robert Kozma and Nikola Kasabov Part IV Learning Systems and Evolutionary Computation: Bayesian Ying-Yang System and Theory as a Unified Statistical Learning Approach (I): Unsupervised and Semi-Unsupervised Learning/Lei Xu ; Evolutionary Computation: An Introduction, Some Current Applications, and Future Directions/David B. Fogel; Biologically Inspired New Operations for Genetic Algorithms/Ashish Ghosh and Sankar K. Pal Part V Adaptive Learning for Navigation, Control and Decision Making: From Vision to Action via Distributed Computation/Michael A. Arbib; A Brain-like Design to Learn Optimal Decision Strategies in Complex Environments/Paul J. Werbos Part VI Knowledge Recovery and Information Retrieval: Structural Learning and Rule Discovery from Data/Masumi Ishikawa; Measuring the Significance and Contributions of Inputs in Backpropagation Neural Networks for Rules Extraction and Data Mining/Tams D. Gedeon; Applying Connectionist Models to Information Retrieval/Sally Jo Cunningham, Geoffrey Holmes, Jamie Littin, Russell Beale and Ian H. Witten Part VII Consciousness in Living and Artificial Systems: Neural Networks for Consciousness/John G. Taylor; Platonic Model of Mind as an Approximation to Neurodynamics/Wlodzislaw Duch; Towards Visual Awareness in a Neural System/Igor Aleksander, Chris Browne, Barry Dunmall and Tim Wright