формат Adobe PDF, размер 2173 Кб - Информационно ...

ISBN 5–7262–0471–9ЛЕКЦИИ ПО НЕЙРОИНФОРМАТИКЕ11. Frolov A. A, Muraviev I. P. Informational characteristics of neural networks capableof associative learning based on Hebbian plasticity // Network. – 1993. – 4. – pp. 495–536.12. Gardner E., Derrida B., Mottishaw P. Zero temperature parallel dynamics for infiniterange spin glasses and neural networks // J. Physique. – 1987. – 48. – pp. 741–755.13. Goles-Chacc E., Fogelman-Soulie F., Pellegrin D. Decreasing energy functions asa tool for studying threshold networks // Discrete Mathematics. – 1985. – 12. –pp. 261–277.14. Hopfield J. J. Neural network and physical systems with emergent collectivecomputational abilities // Proceedings of the National Academy of Science, USA.– 1982. – 79. – pp. 2544–2548.15. Horner H. Neural networks with low levels of activity: Ising vs. McCulloch-Pittsneurons // Z. Physik B. – 1989. – 75. – pp. 133–136.16. Horner H., Bormann D., Frick M., Kinzelbach H., Schmidt A. Transients and basinsof attraction in neural network models // Z. Physik B. – 1989. – 76. – pp. 381–398.17. Husek D., Frolov A. A. Evaluation of the informational capacity of Hopfield networkby computer simulation // Neural Network World. – 1994. – 4. – pp. 53–65.18. Kinzel W. Learning and pattern recognition in spin glass models // Z. Physik B. –1985. – 60. – pp. 205–213.19. Kohring G. A. A high-precision study of the Hopfield model in the phase of brokenreplica symmetry // Journal of Statistical Physics. – 1990. – 59. – pp. 1077–1086.20. Kohring G. A. Convergence time and finite size effects in neural networks// Journal of Physics A: Math. Gen. – 1990. – 23. – pp. 2237–2241.21. Koyama H., Fujie N., Seyama H. Results from the Gardner–Derrida–Mottishawtheory of associative memory Neural Networks. – 1999. – 12. – pp. 247–257.22. Okada M. Notions of associative memory and sparse coding // Neural Networks. –1996. – 9. – pp. 1429–1458.23. Palm G., Sommer F. T. Information capacity in recurrent McCulloch–Pitts networkwith sparsely coded memory states // Network. – 1992. – 3. – pp. 177–186.24. Perez-Vicente C. J., Amit D. J. Optimized network for sparsely coded patterns// Journal of Physics A: Math. Gen.. – 1989. – 22. – pp. 559–569.25. Shigemitsu S., Okada M. Statistical neurodynamics of the sparsely encodedassociative memory (in Japanese) // The Brain & Neural Nerworks. – 1996. – 3.– pp. 58–64.26. Tsodyks M. V. Feigelman M. V. The enhanced storage capacity in neural network withlow activity level // Europhysical Letters. – 1988. – 6. – pp. 101–105.70 УДК 004.032.26 (06) Нейронные сети