Part of Advances in Neural Information Processing Systems 4 (NIPS 1991)
Trent Lange
Winner-Take-All (WTA) networks. in which inhibitory interconnec(cid:173) tions are used to determine the most highly-activated of a pool of unilS. are an important part of many neural network models. Unfortunately, convergence of normal WT A networks is extremely sensitive to the magnitudes of their weights, which must be hand-tuned and which gen(cid:173) erally only provide the right amount of inhibition across a relatively small range of initial conditions. This paper presents Dynamjcally(cid:173) Adaptive Winner-Telke-All (DA WTA) netw