Associative Memory in Asymmetric Diluted Network with Low Level of Activity

Abstract

We extend the analysis of asymmetric diluted networks to the case of low-activity level. The same learning algorithm which was used for the symmetric model turns out to be successful. The use of "V-variables" (V = 0; 1) leads to significant enhancing of the storage capacity. The overloading phase transition is found to be of the first order, which means good retrieval quality in all associative memory phases. The intensity of time-dependent nonthermal noise can be diminished considerably by the appropriate choice of the neural threshold. Some sort of "universality" of the performance of the networks with low-activity level can be noted.