POSTER PRESENTATION Open Access Should Hebbian learning be selective for negative excess kurtosis?

Within the Hebbian learning paradigm, synaptic plasticity results in potentiation whenever pre- and postsynaptic activities are correlated, and in depression otherwise. This requirement is however not sufficient to determine the precise functional form for Hebbian learning, and a range of distinct formulations have been proposed hitherto. They differ, in particular, in the way runaway synaptic growth is avoided; by either imposing a hard upper bound for the synaptic strength, overall synaptic scaling, or addi-tive synaptic decay [1]. Here we propose [2] a multiplica-tive Hebbian learning rule which is, at the same time, self-limiting and selective for negative excess kurtosis (for the case of symmetric input distributions). Hebbian learning results naturally in a principal compo-nent analysis (PCA), whenever one is present. Alternative for