Brain-Like Computing and Intelligent Information Systems

By Shun-Ichi Amari (editor), Nikola K. Kasabov (editor)

bookHardcover - 400 pages
Springer Verlag, March 1998
ISBN: 9813083581
Dimensions (in inches): 1.35 x 9.60 x 6.54 page

This book introduces and defines a new area in computer science ant artificial intelligence called brain-like computing. Brain-like computing combines traditional computational techniques with computational and cognitive ideas, principles and models inspired by the human brain for building intelligent information systems, to be used in our everyday life. Image and speech processing, blind signal separation, creative planning and design, decision making, 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. Modelling mind and consciousness are topics also presented in the book along with other new theoretical models and applications in the area of artificial intelligence.

The book comprises chapters written by well-known specialists in the field and can be used by scientists and graduate students from different areas, as well as by a wider audience of people interested in the present and future development of computer science and artificial intelligence.

Table of Contents
Part I Computer Vision and Image Processing
Chapter 1. Active Vision: Neural Network Models (Kunihiko Fukushima)
Chapter 2. Image Recognition by Brains and Machines (Eric Postma, Jaap van den Herik and Patrick Hudson
Chapter 3. 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

Chapter 4. A Computational Model of the Auditory Pathway to the Superior Colliculus (Raymond J.W. Wang and Marwan Jabri)
Chapter 5. 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

Chapter 6. Noise-Mediated Cooperative Behavior in Integrate-Fire Models of Neuron Dynamics (Adi R. Bulsara)
Chapter 7. Blind Source Separation -- Mathematical Foundations (Shun-ichi Amari)
Chapter 8. Neural Independent Component Analysis -- Approaches and Applications (Erkki Oja, Juha Karhunen, Aapo Hyvarinen, Ricardo Vigario and Jarmo Hurri)
Chapter 9. General Regression Techniques Based on Spherical Kernel Functions for Intelligent Processin (Anthony Zankich and Yianny Attikiouzel)
Chapter 10. 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

Chapter 11. Bayesian Ying-Yang System and Theory as a Unified Statistical Learning Approach (I): Unsupervised and Semi-Unsupervised Learning (Lei Xu)
Chapter 12. Evolutionary Computation: An Introduction, Some Current Applications, and Future Directions (David B. Fogel)
Chapter 13. Biologically Inspired New Operations for Genetic Algorithms (Ashish Ghosh and Sankar K. Pal)

Part V Adaptive Learning for Navigation, Control and Decision Making

Chapter 14. From Vision to Action via Distributed Computation (Michael A. Arbib)
Chapter 15. A Brain-like Design to Learn Optimal Decision Strategies in Complex Environments (Paul J. Werbos)

Part VI Knowledge Recovery and Information Retrieval

Chapter 16. Structural Learning and Rule Discovery from Data (Masumi Ishikawa)
Chapter 17. Measuring the Significance and Contributions of Inputs in Backpropagation Neural Networks for Rules Extraction and Data Mining (Tamas D. Gedeon)
Chapter 18. 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

Chapter 19. Neural Networks for Consciousness (John G. Taylor)
Chapter 20. Platonic Model of Mind as an Approximation to Neurodynamics (Wlodzislaw Duch)
Chapter 21. Towards Visual Awareness in a Neural System (Igor Aleksander, Chris Browne, Barry Dunmall and Tim Wright)