Input Statistics and Hebbian Crosstalk Effects

Abstract

As an extension of prior work, we study inspecific Hebbian learning using the classical Oja model. We use a combination of analytical tools and numerical simulations to investigate how the effects of inspecificity (or synaptic "cross-talk") depend on the input statistics. We investigated a variety of patterns that appear in dimensions higher than 2 (and classified them based on covariance type and input bias). The effects of inspecificity on the learning outcome were found to depend very strongly on the nature of the input, and in some cases were very dramatic, making unlikely the existence of a generic neural algorithm to correct learning inaccuracy due to cross-talk. We discuss the possibility that sophisticated learning, such as presumably occurs in the neocortex, is enabled as much by special proofreading machinery for enhancing specificity, as by special algorithms.