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.

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Correspondence to Thomas Trappenberg.

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Standage, D., Jalil, S. & Trappenberg, T. Computational consequences of experimentally derived spike-time and weight dependent plasticity rules. Biol Cybern 96, 615 (2007). https://doi.org/10.1007/s00422-007-0152-6

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Keywords

  • Spike Train
  • Postsynaptic Spike
  • EPSC Amplitude
  • Power Rule
  • Equilibrium Weight