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- W281268388 abstract "We analyze the behavior of a neural network with an energy function of the form where n is an integer, the are the stored patterns and is any state of the network with components 1 or −1. For an arbitrary initial state the network evolves to states that decrease the energy function E n through a single spin flip dynamics. After establishing the condition for the to be stable, by means of simple probabilistic considerations we are able to estimate an upper bound for the size of the basin of attraction of the stored patterns as a function of n. We have performed some numerical simulations in order to verify these predictions." @default.
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- W281268388 date "1991-01-01" @default.
- W281268388 modified "2023-10-16" @default.
- W281268388 title "THE EFFICIENCY OF HIGH ORDER NEURAL NETWORKS" @default.
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- W281268388 doi "https://doi.org/10.1016/b978-0-444-88791-7.50013-2" @default.
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