An Introduction to the Modeling of Neural Networks

Erscheinungsjahr 2004
ISBN: 978-0-521-41451-7
Verlag: Cambridge University Press

Buch, Englisch, 491 Seiten, Format (B × H): 157 mm x 235 mm, Gewicht: 943 g

Reihe: Collection Alea-Saclay: Monographs and Texts in Statistical Physics

This text is a beginning graduate-level introduction to neural networks, focussing on current theoretical models, examining what these models can reveal about how the brain functions, and discussing the ramifications for psychology, artificial intelligence and the construction of a new generation of intelligent computers. The book is divided into four parts. The first part gives an account of the anatomy of the central nervous system, followed by a brief introduction to neurophysiology. The second part is devoted to the dynamics of neuronal states, and demonstrates how very simple models may stimulate associative memory. The third part of the book discusses models of learning, including detailed discussions on the limits of memory storage, methods of learning and their associated models, associativity, and error correction. The final part reviews possible applications of neural networks in artificial intelligence, expert systems, optimization problems, and the construction of actual neuronal supercomputers, with the potential for one-hundred-fold increase in speed over contemporary serial machines.

Autoren/Hrsg.

Peretto, Pierre

Fachgebiete

• Naturwissenschaften Physik Angewandte Physik Soziophysik, Wirtschaftsphysik
• Naturwissenschaften Physik Angewandte Physik Statistische Physik, Dynamische Systeme
• Naturwissenschaften Biowissenschaften Tierkunde / Zoologie Tierphysiologie

Inhaltsverzeichnis

Preface; Acknowledgments; 1. Introduction; 2. The biology of neural networks: a few features for the sake of non-biologists; 3. The dynamics of neural networks: a stochastic approach; 4. Hebbian models of associative memory; 5. Temporal sequences of patterns; 6. The problem of learning in neural networks; 7. Learning dynamics in 'visible' neural networks; 8. Solving the problem of credit assignment; 9. Self-organization; 10. Neurocomputation; 11. Neurocomputers; 12. A critical view of the modeling of neural networks; References; Index.