The Enhanced Storage Capacity in Neural Networks with Low Activity Level

Abstract

The modified Hopfield model defined in terms of "V-variables" (V = 0; 1), which is appropriate for storage of correlated patterns, is considered. The learning algorithm is proposed to enhance significantly the storage capacity in comparison with previous estimates. At low levels of neural activity, p ≪ 1, we obtain αc(p) ∼ (p|ln p|)-1 which resembles Gardner's estimate for the maximum storage capacity.