Abstract
The Hopfield model for a neural network is studied in the limit when the number of stored patterns increases with the size of the network, as . It is shown that, despite its spin-glass features, the model exhibits associative memory for , . This is a result of the existence at low temperature of dynamically stable degenerate states, each of which is almost fully correlated with one of the patterns. These states become ground states at . The phase diagram of this rich spin-glass is described.
- Received 11 July 1985
DOI:https://doi.org/10.1103/PhysRevLett.55.1530
©1985 American Physical Society