Steady states in an iterative model for multiplicative spike-timing-dependent plasticity.

Recent experimental evidence suggests that synaptic plasticity depends on the precise timing of pre- and post-synaptic activity. In this paper, an iterative model for a multiplicative form of this spike-timing-dependent plasticity (mSTDP) is introduced. This model is incorporated into a neural network with many input cells coupled via excitation to a single output cell. Analysis of this network yields a criterion for the output cell to fire on every iteration, as well as general formulae for the steady-state output firing rate and the steady-state value to which all synaptic weights are driven by mSTDP. These characterize the basic state of network operation generated by mSTDP.