An LTP/LTD perspective on learning rules

Abstract

A single framework is shown to encompass several existing learning rules, separating them into positive and negative terms, respectively corresponding to long-term potentiation (LTP) and long-term depression (LTD) phenomena. Each term is expressed as an integral of a Hebbian product over time, modulated by a kernel function. Carefully chosen kernel functions are shown to exhibit computational properties of temporal contrast enhancement and prediction. Some preliminary simulation results are presented for illustration purposes.