Two significant things have happened since the writing of the first edition in 1983. One of them is recent arousal of strong interest in general aspects of "neural computing", or "neural networks", as the previous neural models are nowadays called. The incentive, of course, has been to develop new com puters. Especially it may have been felt that the so-called fifth-generation computers, based on conventional logic programming, do not yet contain in formation processing principles of the same type as those encountered in the brain. All new ideas for the "neural computers" are, of course, welcome. On the other hand, it is not very easy to see what kind of restrictions there exist to their implementation. In order to approach this problem systematically, cer tain lines of thought, disciplines, and criteria should be followed. It is the pur pose of the added Chapter 9 to reflect upon such problems from a general point of view. Another important thing is a boom of new hardware technologies for dis tributed associative memories, especially high-density semiconductor circuits, and optical materials and components. The era is very close when the parallel processors can be made all-optical. Several working associative memory archi tectures, based solely on optical technologies, have been constructed in recent years. For this reason it was felt necessary to include a separate chapter (Chap. 10) which deals with the optical associative memories. Part of its con tents is taken over from the first edition.
Reihe
Auflage
Sprache
Verlagsort
Verlagsgruppe
Illustrationen
167 s/w Abbildungen
XV, 312 p. 167 illus.
ISBN-13
978-3-662-00784-6 (9783662007846)
DOI
10.1007/978-3-662-00784-6
Schweitzer Klassifikation
1. Various Aspects of Memory.- 1.1 On the Purpose and Nature of Biological Memory.- 1.2 Questions Concerning the Fundamental Mechanisms of Memory.- 1.3 Elementary Operations Implemented by Associative Memory.- 1.4 More Abstract Aspects of Memory.- 2. Pattern Mathematics.- 2.1 Mathematical Notations and Methods.- 2.2 Distance Measures for Patterns.- 3. Classical Learning Systems.- 3.1 The Adaptive Linear Element (Adaline).- 3.2 The Perceptron.- 3.3 The Learning Matrix.- 3.4 Physical Realization of Adaptive Weights.- 4. A New Approach to Adaptive Filters.- 4.1 Survey of Some Necessary Functions.- 4.2 On the "Transfer Function" of the Neuron.- 4.3 Models for Basic Adaptive Units.- 4.4 Adaptive Feedback Networks.- 5. Self-Organizing Feature Maps.- 5.1 On the Feature Maps of the Brain.- 5.2 Formation of Localized Responses by Lateral Feedback.- 5.3 Computational Simplification of the Process.- 5.4 Demonstrations of Simple Topology-Preserving Mappings.- 5.5 Tonotopic Map.- 5.6 Formation of Hierarchical Representations.- 5.7 Mathematical Treatment of Self-Organization.- 5.8 Automatic Selection of Feature Dimensions.- 6. Optimal Associative Mappings.- 6.1 Transfer Function of an Associative Network.- 6.2 Autoassociative Recall as an Orthogonal Projection.- 6.3 The Novelty Filter.- 6.4 Autoassociative Encoding.- 6.5 Optimal Associative Mappings.- 6.6 Relationship Between Associative Mapping, Linear Regression, and Linear Estimation.- 6.7 Recursive Computation of the Optimal Associative Mapping..- 6.8 Special Cases.- 7. Pattern Recognition.- 7.1 Discriminant Functions.- 7.2 Statistical Formulation of Pattern Classification.- 7.3 Comparison Methods.- 7.4 The Subspace Methods of Classification.- 7.5 Learning Vector Quantization.- 7.6 Feature Extraction.- 7.7 Clustering.- 7.8Structural Pattern Recognition Methods.- 8. More About Biological Memory.- 8.1 Physiological Foundations of Memory.- 8.2 The Unified Cortical Memory Model.- 8.3 Collateral Reading.- 9. Notes on Neural Computing.- 9.1 First Theoretical Views of Neural Networks.- 9.2 Motives for the Neural Computing Research.- 9.3 What Could the Purpose of the Neural Networks be?.- 9.4 Definitions of Artificial "Neural Computing" and General Notes on Neural Modelling.- 9.5 Are the Biological Neural Functions Localized or Distributed?.- 9.6 Is Nonlinearity Essential to Neural Computing?.- 9.7 Characteristic Differences Between Neural and Digital Computers.- 9.8 "Connectionist Models".- 9.9 How can the Neural Computers be Programmed?.- 10. Optical Associative Memories.- 10.1 Nonholographic Methods.- 10.2 General Aspects of Holographic Memories.- 10.3 A Simple Principle of Holographic Associative Memory.- 10.4 Addressing in Holographic Memories.- 10.5 Recent Advances of Optical Associative Memories.- Bibliography on Pattern Recognition.- References.
