HIERARCHICAL ASSOCIATIVE MEMORY IN NEURAL NETWORKS WITH LOW ACTIVITY LEVEL

Abstract

A Hopfield-like neural network that can store hierarchically correlated patterns with low level of activity is studied. Three learning rules are proposed which enable to obtain nearly optimal storage capacity. These learning rules have different rate of biological relevancy and the restrictions they put upon the structure of hierarchical tree. By varying the value of the neural threshold, it is possible to climb up and down the hierarchical tree.