Correlation based learning from spike timing dependent plasticity

We explore a synaptic plasticity model where potentiation and depression ar e induced by precisely timed pairs of synaptic events and postsynaptic spik es. We include the observation that strong synapses undergo relatively less potentiation than weak synapses, whereas depression is independent of syna ptic strength. After random stimulation the synaptic weights reach a stable equilibrium distribution. Competition can be introduced separately by a me chanism that scales synaptic strengths as a function of postsynaptic activi ty. The plasticity rules select inputs which have a strong correlation with other inputs. (C) 2001 Published by Elsevier Science B.V.