Synergetic Computers and Cognition

1. Front Matter

   Pages I-IX

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2. Goal

   1. Goal

      • Hermann Haken

      Pages 1-6

3. Synergetic Computers

   1. Front Matter

      Pages 7-7

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   2. What are Patterns?

      • Hermann Haken

      Pages 9-17

   3. Associative Memory

      • Hermann Haken

      Pages 18-19

   4. Synergetics — An Outline

      • Hermann Haken

      Pages 20-35

   5. The Standard Model of Synergetics for Pattern Recognition

      • Hermann Haken

      Pages 36-50

   6. Examples: Recognition of Faces and of City Maps

      • Hermann Haken

      Pages 51-55

   7. Possible Realizations by Networks

      • Hermann Haken

      Pages 56-59

   8. Simultaneous Invariance with Respect to Translation, Rotation and Scaling

      • Hermann Haken

      Pages 60-83

   9. Recognition of Complex Scenes. Scene-Selective Attention

      • Hermann Haken

      Pages 85-87

   10. Learning Algorithms

       • Hermann Haken

       Pages 88-124

   11. Learning of Processes and Associative Action

       • Hermann Haken

       Pages 125-133

4. Cognition and Synergetic Computers

   1. Front Matter

      Pages 135-135

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   2. Comparisons Between Human Perception and Machine “Perception”

      • Hermann Haken

      Pages 137-162

   3. Oscillations in the Perception of Ambiguous Patterns

      • Hermann Haken

      Pages 163-184

   4. Dynamic Pattern Recognition of Coordinated Biological Motion

      • Hermann Haken

      Pages 185-202

5. Logical Operations and Outlook

   1. Front Matter

      Pages 203-203

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   2. Realization of the Logical Operation XOR by a Synergetic Computer

      • Hermann Haken

      Pages 205-208

   3. Towards the Neural Level

      • Hermann Haken

      Pages 209-230

The first edition of this book has found great interest among scientists and en­ gineers 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 transfor­ mation 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 de­ velopment, namely to the use of pulse-coupled neural nets for pattern recognition.

• Neuro computers
• Parallel computers
• Puls-Coupled Neural Networks
• Stereo
• Stereopsis
• Synergetic Computer
• Synergetics
• cognition
• learning

• Institut für Theoretische Physik und Synergetik, Universität Stuttgart, Stuttgart, Germany

  Hermann Haken

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