Haken Synergetic Computers and Cognition

A Top-Down Approach to Neural Nets
Erscheinungsjahr 2013
ISBN: 978-3-662-22450-2
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 50, 226 Seiten, eBook

Reihe: Springer Series in Synergetics

This book will be of interest to graduate students, researchers and teachers in the computer sciences, in the cognitive sciences andin physics. It provides the reader with a novel approach to the design and study of neural nets. The applicability of this approach is shown explicitly by means of realistic examples. In addition, detailed models of the cognitive abilities of humans are included and compared with the performance of the synergetic computer presented in this book. The work presented here would not have been possible without the important help of my coworkers. Dr. Arne Wunderlin has helped me in many respects over many years and has made essential contributions, in particular to the slaving principle of synergetics. Drs. Michael Bestehorn, Rudolf Friedrich and Wolfgang W eimer have applied the methods of synergetics to spontaneous pattern forma tion in fluids and have further developed these methods. Armirr Fuchs has not only implemented my algorithm on a VAX computer, but has also made his own important contributions, in particular to pattern recognition that is invariant with respect to translation, rotation, and scaling. Thomas Ditzinger, Richard Haas, and Robert Hönlinger have contributed within the work on their diploma theses to the application of our approach to a number of problems that are shared by humans and computers in the field of pattern recognition. I wish to thank all of them.

Zielgruppe

Research

Autoren/Hrsg.

Haken, Hermann

Inhaltsverzeichnis

1. Goal.- I Synergetic Computers.- 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.- II Cognition and Synergetic Computers.- 12. Comparisons Between Human Perception and Machine “Perception”.- 13. Oscillations in the Perception of Ambiguous Patterns.- 14. Dynamic Pattern Recognition of Coordinated Biological Motion.- III Logical Operations and Outlook.- 15. Realization of the Logical Operation XOR by a Synergetic Computer.- 16. Towards the Neural Level.- 17. Concluding Remarks and Outlook.- Bibliography and Comments.