A NEW MODULATED HEBBIAN LEARNING RULE - BIOLOGICALLY PLAUSIBLE METHOD FOR LOCAL COMPUTATION OF A PRINCIPAL SUBSPACE

摘要

This paper presents one possible implementation of a transformation that performs linear mapping to a lower-dimensional subspace. Principal component subspace will be the one that will be analyzed. Idea implemented in this paper represents generalization of the recently proposed ∞OH neural method for principal component extraction. The calculations in the newly proposed method are performed locally - a feature which is usually considered as desirable from the biological point of view. Comparing to some other wellknown methods, proposed synaptic efficacy learning rule requires less information about the value of the other efficacies to make single efficacy modification. Synaptic efficacies are modified by implementation of Modulated Hebb-type (MH) learning rule. Slightly modified MH algorithm named Modulated Hebb Oja (MHO) algorithm, will be also introduced. Structural similarity of the proposed network with part of the retinal circuit will be presented, too.