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- W2077560650 abstract "The authors study the problem of learning and retrieving for a pair of correlated patterns within an extensive number of uncorrelated patterns, for networks where learning may be treated as an optimization process with respect to an arbitrary cost function. This formulation is then applied to several specific examples, where they study the pattern selectivity of these systems (i.e. their ability to differentiate correlated patterns) and investigate the process of basin shrinking with increasing loading levels. They define and discuss several different retrieval phases, whose existence depends on the competitive interplay of the loading level and the pattern correlation. Discussion of asymptotic retrieval is restricted to dilute asymmetric networks." @default.
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- W2077560650 date "1993-06-21" @default.
- W2077560650 modified "2023-09-27" @default.
- W2077560650 title "Pattern selectivity in neural networks as a means of understanding basin structures" @default.
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- W2077560650 doi "https://doi.org/10.1088/0305-4470/26/12/027" @default.
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