title: Computational consequences of experimentally derived spike-time and weight dependent plasticity rules.
abstract: We present two weight- and spike-time dependent synaptic plasticity rules consistent with the physiological data of Bi and Poo (J Neurosci 18:10464-10472, 1998). One rule assumes synaptic saturation, while the other is scale free. We extend previous analyses of the asymptotic consequences of weight-dependent STDP to the case of strongly correlated pre- and post-synaptic spiking, more closely resembling associative learning. We further provide a general formula for the contribution of any number of spikes to synaptic drift. Asymptotic weights are shown to principally depend on the correlation and rate of pre- and post-synaptic activity, decreasing with increasing rate under correlated activity, and increasing with rate under uncorrelated activity. Spike train statistics reveal a quantitative effect only in the pre-asymptotic regime, and we provide a new interpretation of the relation between BCM and STDP data.
authors.affiliation: Faculty of Computer Science, Dalhousie University, 6050 University Avenue, Halifax, NS, Canada B3H 1W5.
authors.forename: Dominic
authors.initials: D
authors.lastname: Standage
authors.forename: Sajiya
authors.initials: S
authors.lastname: Jalil
authors.forename: Thomas
authors.initials: T
authors.lastname: Trappenberg
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D003198:Computer Simulation; D008959:Models, Neurological; D009415:Nerve Net; D009473:Neuronal Plasticity / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D013569:Synapses / Q000502:physiology; D009435:Synaptic Transmission; D013997:Time Factors