论文信息 - Neural network models of learning and adaptation

Neural network models of learning and adaptation

Abstract Recent work has applied ideas from many fields including biology, physics and computer science, in order to understand how a highly interconnected network of simple processing elements can perform useful computation. Such networks can be used as associative memories, or as analog computers to solve optimization problems. This article reviews the workings of a standard model with particular emphasis on various schemes for learning and adaptation.

J S Denker | J. Denker

[1] Stephen Grossberg,et al. Studies of mind and brain , 1982 .

[2] J. Nicholls. From neuron to brain , 1976 .

[3] J J Hopfield,et al. Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[4] Francis Crick,et al. The function of dream sleep , 1983, Nature.

[5] Forman S. Acton,et al. Numerical methods that work , 1970 .

[6] Teuvo Kohonen,et al. Self-Organization and Associative Memory , 1988 .

[7] Teuvo Kohonen,et al. Representation of Associated Data by Matrix Operators , 1973, IEEE Transactions on Computers.

[8] Sompolinsky,et al. Storing infinite numbers of patterns in a spin-glass model of neural networks. , 1985, Physical review letters.

[9] W. Daniel Hillis,et al. The connection machine , 1985 .

[10] J. J. Hopfield,et al. ‘Unlearning’ has a stabilizing effect in collective memories , 1983, Nature.

[11] D. O. Hebb,et al. The organization of behavior , 1988 .