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Biological Cybernetics

, 96:615 | Cite as

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

  • Dominic Standage
  • Sajiya Jalil
  • Thomas Trappenberg
Original Paper

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.

Keywords

Spike Train Postsynaptic Spike EPSC Amplitude Power Rule Equilibrium Weight 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Dominic Standage
    • 1
  • Sajiya Jalil
    • 1
  • Thomas Trappenberg
    • 1
  1. 1.Faculty of Computer ScienceDalhousie UniversityHalifaxCanada

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