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- W2168328168 abstract "Winner-Take-All (WTA) networks, in which inhibitory interconnections are used to determine the most highly-activated of a pool of units, are an important part of many neural network models. Unfortunately, convergence of normal WTA networks is extremely sensitive to the magnitudes of their weights, which must be hand-tuned and which generally only provide the right amount of inhibition across a relatively small range of initial conditions. This paper presents Dynamically Adaptive Winner-Teke-All (DAWTA) networks, which use a regulatory unit to provide the competitive inhibition to the units in the network. The DAWTA regulatory unit dynamically adjusts its level of activation during competition to provide the right amount of inhibition to differentiate between competitors and drive a single winner. This dynamic adaptation allows DAWTA networks to perform the winner-take-all function for nearly any network size or initial condition. using O(N) connections. In addition, the DAWTA regulatory unit can be biased to find the level of inhibition necessary to settle upon the K most highly-activated units, and therefore serve as a K-Winners-Take-All network." @default.
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- W2168328168 date "1991-12-02" @default.
- W2168328168 modified "2023-09-23" @default.
- W2168328168 title "Dynamically-Adaptive Winner-Take-All Networks" @default.
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