We study a deterministic parallel feed-forward neural network. Exact results for the response of the network are presented; given an initial state that has finite overlap with one stored random key pattern, we calculate the overlap on all subsequent layers (time steps). A region of good recall is separated by a first-order line from one of vanishing asymptotic overlap. Relaxation time to the limiting state is shown to diverge at the overloading transition.
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Meir et al. (1987) studied this question.
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