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Phys. Rev. Lett. 55, 1530–1533 (1985)

Storing Infinite Numbers of Patterns in a Spin-Glass Model of Neural Networks

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Daniel J. Amit and Hanoch Gutfreund
Racah Institute of Physics, Hebrew University, Jerusalem 91904, Israel

H. Sompolinsky
Department of Physics, Bar Ilan University, Ramat Gan, Israel

Received 11 July 1985; published in the issue dated 30 September 1985

The Hopfield model for a neural network is studied in the limit when the number p of stored patterns increases with the size N of the network, as p=αN. It is shown that, despite its spin-glass features, the model exhibits associative memory for α<αc, αc0.14. This is a result of the existence at low temperature of 2p dynamically stable degenerate states, each of which is almost fully correlated with one of the patterns. These states become ground states at α<0.05. The phase diagram of this rich spin-glass is described.

© 1985 The American Physical Society

URL:
http://link.aps.org/doi/10.1103/PhysRevLett.55.1530
DOI:
10.1103/PhysRevLett.55.1530
PACS:
87.30.Gy, 64.60.Cn, 75.10.Hk, 89.70.+c