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Extreme Attraction: On the Discrete Representation Preference of Attractor Networks

By David C. Noelle, Garrison W. Cottrell and Fred R. Wilms

Abstract

ch, 1982), would benefit from attractor dynamics. In this work we argue that these apparent advantages of continuous targets are illusory, at least when attractors are being learned using standard methods. Our simulation results indicate that attractor networks, even those with continuous activation functions, are best suited for use with target vectors consisting of polarized discrete elements. Simulation Results For this investigation we used single layer networks, with complete interconnections between processing elements (including self-connections), asymmetric weights, and sigmoidal activation functions. Unit activity spanned between \Gamma1 and +1. The networks were trained to form fixed-point attractors for a collection of target vectors using backpropagationthrough -time, backpropagating the error signal for 10 time steps. A low learning rate of 0:001 was used, with no momentum. Networks were presented with eac

Year: 1997
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