Neural Networks

1. Front Matter

   Pages I-XIII

   PDF

2. Models of Neural Networks

   1. Front Matter

      Pages 1-1

      PDF

   2. The Structure of the Central Nervous System

      • Berndt Müller, Joachim Reinhardt

      Pages 2-11

   3. Neural Networks Introduced

      • Berndt Müller, Joachim Reinhardt

      Pages 12-22

   4. Associative Memory

      • Berndt Müller, Joachim Reinhardt

      Pages 23-36

   5. Stochastic Neurons

      • Berndt Müller, Joachim Reinhardt

      Pages 37-44

   6. Cybernetic Networks

      • Berndt Müller, Joachim Reinhardt

      Pages 45-50

   7. Multilayered Perceptrons

      • Berndt Müller, Joachim Reinhardt

      Pages 51-61

   8. Applications

      • Berndt Müller, Joachim Reinhardt

      Pages 62-72

   9. Network Architecture and Generalization

      • Berndt Müller, Joachim Reinhardt

      Pages 73-86

   10. Associative Memory: Advanced Learning Strategies

       • Berndt Müller, Joachim Reinhardt

       Pages 87-103

   11. Combinatorial Optimization

       • Berndt Müller, Joachim Reinhardt

       Pages 104-112

   12. VLSI and Neural Networks

       • Berndt Müller, Joachim Reinhardt

       Pages 113-118

   13. Symmetrical Networks with Hidden Neurons

       • Berndt Müller, Joachim Reinhardt

       Pages 119-125

   14. Coupled Neural Networks

       • Berndt Müller, Joachim Reinhardt

       Pages 126-131

   15. Unsupervised Learning

       • Berndt Müller, Joachim Reinhardt

       Pages 132-144

3. Statistical Physics of Neural Networks

   1. Front Matter

      Pages 145-145

      PDF

   2. Statistical Physics and Spin Glasses

      • Berndt Müller, Joachim Reinhardt

      Pages 146-155

   3. The Hopfield Network for p/N → o

      • Berndt Müller, Joachim Reinhardt

      Pages 156-164

   4. The Hopfield Network for Finite p/N

      • Berndt Müller, Joachim Reinhardt

      Pages 165-186

The mysteries of the human mind have fascinated scientists and philosophers alike for centuries. Descartes identified our ability to think as the foundation stone of ontological philosophy. Others have taken the human mind as evidence of the existence of supernatural powers, or even of God. Serious scientific in­ vestigation, which began about half a century ago, has partially answered some of the simpler questions (such as how the brain processes visual information), but has barely touched upon the deeper ones concerned with the nature of consciousness and the possible existence of mental features transcending the biological substance of the brain, often encapsulated in the concept "soul". Besides the physiological and philosophical approaches to these questions, so impressively presented and contrasted in the recent book by Popper and Ec­ cles [P077), studies of formal networks composed of binary-valued information­ processing units, highly abstracted versions of biological neurons, either by mathematical analysis or by computer simulation, have emerged as a third route towards a better understanding of the brain, and possibly of the human mind. Long remaining - with the exception of a brief period in the early 1960s - a rather obscure research interest of a small group of dedicated scientists scattered around the world, neural-network research has recently sprung into the limelight as a "fashionable" research field.

• Konnektionismus (Kybern.)
• Künstliche Intelligenz
• Pa
• artificial intelligence
• associative memory
• brain research
• distributed processing
• information processing
• intelligence
• learning
• memory
• modeling
• neural computing
• neural network
• neural networks

• Department of Physics, Duke University, Durham, USA

  Berndt Müller

• Institut für Theoretische Physik, J.-W.-Goethe-Universität, Frankfurt 1, Fed. Rep. of Germany

  Joachim Reinhardt

Policies and ethics