Modeling Brain Function : The World of Attractor Neural Networks / by Daniel J. Amit. [Electronic Resource]
Material type: Computer filePublication details: Cambridge : Cambridge University Press, 1989Description: xviii, 504pISBN:- 9780511623257
- 591.188Â Am51M
Item type | Home library | Collection | Call number | Status | Notes | Date due | Barcode | Item holds | |
---|---|---|---|---|---|---|---|---|---|
e-Book | S. R. Ranganathan Learning Hub Online | Textbook | 591.188 Am51M (Browse shelf(Opens below)) | Available (e-Book For Access) | Platform : Cambridge Core | EB0370 |
Browsing S. R. Ranganathan Learning Hub shelves, Shelving location: Online, Collection: Textbook Close shelf browser (Hides shelf browser)
579 H424M Microbiology in Action | 579 M265B Brock Biology of Microorganisms | 579.316 5 H397H Horizontal Gene Transfer in the Evolution of Pathogenesis | 591.188 Am51M Modeling Brain Function : The World of Attractor Neural Networks | 610.28 D588N Neuroengineering | 610.28 M576B Biological Materials Science : Biological Materials, Bioinspired Materials, and Biomaterials | 610.28 N164M Medical Biosensors for Point of Care (POC) Applications |
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.
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