Abstract
Local iterative learning algorithms for the interactions between Ising spins in neural network models are discussed. They converge to solutions with basins of attraction whose shape is determined by the noise in the training data, provided such solutions exist. The training is applied both to the storage of random patterns and to a model for the storage of correlated words. The existence of correlations increases the storage capacity of a given network beyond that for random patterns. The model can be modified to store cycles of patterns and in particular is applied to the storage of continuous items of English text.
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