Modeling Brain Function
The World of Attractor Neural Networks
Seiten
1992
Cambridge University Press (Verlag)
978-0-521-42124-9 (ISBN)
Cambridge University Press (Verlag)
978-0-521-42124-9 (ISBN)
A group of researchers, adapting knowledge and techniques from a wide range of scientific disciplines, have made progress understanding memory by studying the properties of models of neural networks. This book introduces and explains techniques bought from physics to the study of neural networks and the insights they have stimulated.
One of the most exciting and potentially rewarding areas of scientific research is the study of the principles and mechanisms underlying brain function. It is also of great promise to future generations of computers. A growing group of researchers, adapting knowledge and techniques from a wide range of scientific disciplines, have made substantial progress understanding memory, the learning process, and self organization by studying the properties of models of neural networks - idealized systems containing very large numbers of connected neurons, whose interactions give rise to the special qualities of the brain. This book introduces and explains the techniques brought from physics to the study of neural networks and the insights they have stimulated. It is written at a level accessible to the wide range of researchers working on these problems - statistical physicists, biologists, computer scientists, computer technologists and cognitive psychologists. The author presents a coherent and clear nonmechanical presentation of all the basic ideas and results. More technical aspects are restricted, wherever possible, to special sections and appendices in each chapter. The book is suitable as a text for graduate courses in physics, electrical engineering, computer science and biology.
One of the most exciting and potentially rewarding areas of scientific research is the study of the principles and mechanisms underlying brain function. It is also of great promise to future generations of computers. A growing group of researchers, adapting knowledge and techniques from a wide range of scientific disciplines, have made substantial progress understanding memory, the learning process, and self organization by studying the properties of models of neural networks - idealized systems containing very large numbers of connected neurons, whose interactions give rise to the special qualities of the brain. This book introduces and explains the techniques brought from physics to the study of neural networks and the insights they have stimulated. It is written at a level accessible to the wide range of researchers working on these problems - statistical physicists, biologists, computer scientists, computer technologists and cognitive psychologists. The author presents a coherent and clear nonmechanical presentation of all the basic ideas and results. More technical aspects are restricted, wherever possible, to special sections and appendices in each chapter. The book is suitable as a text for graduate courses in physics, electrical engineering, computer science and biology.
Preface; 1. Introduction; 2. The basic attractor neural network; 3. General ideas concerning dynamics; 4. Symmetric neural networks at low memory loading; 5. Storage and retrieval of temporal sequences; 6. Storage capacity of ANNs; 7. Robustness - getting closer to biology; 8. Memory data structures; 9. Learning; 10. Hareware implementations of neural networks; Glossary; Index.
| Erscheint lt. Verlag | 26.6.1992 |
|---|---|
| Zusatzinfo | 1 Tables, unspecified; 4 Halftones, unspecified; 103 Line drawings, unspecified |
| Verlagsort | Cambridge |
| Sprache | englisch |
| Maße | 152 x 229 mm |
| Gewicht | 710 g |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Naturwissenschaften ► Biologie ► Humanbiologie | |
| Naturwissenschaften ► Biologie ► Zoologie | |
| ISBN-10 | 0-521-42124-1 / 0521421241 |
| ISBN-13 | 978-0-521-42124-9 / 9780521421249 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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