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.

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"...of interest to those following the neural net field...takes off from discoveries that link areas of physics with the emerging neural network paradigm." Intelligence Monthly "...regard this book as an opening of a discussion--undoubtedly a very qualified one." Journal of Mathematical Psychology

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