Convergence analysis of a linearized Rubner network with modified lateral weight behavior

Abstract

In this work a novel hebbian neural network architecture for Principal Component Analysis is presented. The proposed network is obtained via a linearization and modification of the standard Rubner model. The new antihebbian connections and learning laws define a partially decoupled net structure. This specific connectivity among the neurons allows for a stability analysis of the whole network where there is no need to assume a priori a time-scale hypothesis between the neurons dynamics.