Synergetic Computers and Cognition: A Top-Down Approach to Neural Nets / Edition 2

by Hermann Haken

Overview

The first edition of this book has found great interest among scientists and engineers dealing with pattern recognition and among psychologists working on psychophysics or Gestalt psychology. This book also proved highly useful for graduate students of informatics. The concept of the synergetic computer offers an important alternative to the by now more traditional neural nets. I just mention a few advantages: There are no ghost states so that time-consuming methods such as simulated annealing can be avoided; the synaptic strengths are explicitly determined by the prototype patterns to be stored, but they can equally well be learned, and the learning procedure allows a classification. Also a precise meaning and function can be attributed to "hidden variables". The synergetic computer has found a number of important practical applications in industry. I use the opportunity of this second edition to include a new section on transformation properties of the equations of the synergetic computer and on the invariance properties of its order parameter equations. A new section is devoted to the problem of stereopsis that is dealt with by the basic concept of the synergetic computer. Finally, attention is paid to a recent development, namely to the use of pulse-coupled neural nets for pattern recognition.

Product Details

ISBN-13:	9783642075735
Publisher:	Springer Berlin Heidelberg
Publication date:	12/06/2010
Series:	Springer Series in Synergetics , #50
Edition description:	Softcover reprint of hardcover 2nd ed. 2004
Pages:	245
Product dimensions:	6.10(w) x 9.25(h) x 0.24(d)

Table of Contents

1. Goal.- 2. What Are Patterns?.- 3. Associative Memory.- 4. Synergetics — An Outline.- 5. The Standard Model of Synergetics for Pattern Recognition.- 6. Examples: Recognition of Faces and of City Maps.- 7. Possible Realizations by Networks.- 8. Simultaneous Invariance with Respect to Translation, Rotation and Scaling.- 9. Recognition of Complex Scenes. Scene-Selective Attention.- 10. Learning Algorithms.- 11. Learning of Processes and Associative Action.- 12. Comparisons Between Human Perception and Machine “Perception”.- 13. Oscillations in the Perception of Ambiguous Patterns.- 14. Dynamic Pattern Recognition of Coordinated Biological Motion.- 15. Realization of the Logical Operation XOR by a Synergetic Computer.- 16. Towards the Neural Level.- 17. Concluding Remarks and Outlook.- Bibliography and Comments.