Extreme Attraction: the Beneets of Corner Attractors
Connectionist attractor networks have played a central role in many cognitive models involving associative memory and soft constraint satisfaction. While early attractor networks used step activation functions , permitting the construction of attractors for only binary (or bipolar) patterns, much recent work has focused on networks with continuous sigmoidal activation functions. The incorporation of sig-moidal processing elements allows for the use of expressive real vector representations in attractor networks. The empirical studies reported here, however, reveal that the learning performance of sigmoidal at-tractor networks is best when such general real vectors are avoided | when training patterns are explicitly placed in the extreme corners of the network's activation space. Using binary (or bipolar) patterns produces beneets in the number of attractors learnable by a network, in the accuracy of the learned attractors, and in the amount of training required. These beneets persist under conditions of sparse patterns. Furthermore, these experiments show that the advantages of extreme-valued patterns are not solely eeects of the large separation between training patterns aaorded by corner attractors.