Computational consequences of experimentally derived spike-time and weight dependent plasticity rules

  • Authors:
  • Dominic Standage;Sajiya Jalil;Thomas Trappenberg

  • Affiliations:
  • Dalhousie University, Faculty of Computer Science, 6050 University Avenue, Halifax, NS, Canada;Dalhousie University, Faculty of Computer Science, 6050 University Avenue, Halifax, NS, Canada;Dalhousie University, Faculty of Computer Science, 6050 University Avenue, Halifax, NS, Canada

  • Venue:
  • Biological Cybernetics
  • Year:
  • 2007

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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.