Correlation based learning from spike timing dependent plasticity

We explore a synaptic plasticity model where potentiation and depression are induced by precisely timed pairs of synaptic events and postsynaptic spikes. We include the observation that strong synapses undergo relatively less potentiation than weak synapses, whereas depression is independent of synaptic strength. After random stimulation the synaptic weights reach a stable equilibrium distribution. Competition can be introduced separately by a mechanism that scales synaptic strengths as a function of postsynaptic activity. The plasticity rules select inputs which have a strong correlation with other inputs.