Spiking-time-dependent Synaptic Plasticity: From Single Spikes to Spike Trains

We present a neurobiologically motivated model of a neuron with active dendrites and dynamic synapses, and a training algorithm which builds upon single spike-timing-dependent synaptic plasticity derived from neurophysiological evidence. We show that in the presence of a moderate level of noise, the plasticity rule can be extended from single to multiple presynaptic spikes and applied to effectively train a neuron in detecting temporal sequences of spike trains. The trained neuron responds reliably under different regimes and types of noise.

@Proceedings{PW03,
 	 author =  {Panchev, Christo and Wermter, Stefan},
 	 title = {Spiking-time-dependent Synaptic Plasticity: From Single Spikes to Spike Trains},
 	 booktitle = {None},
 	 journal = {None},
 	 editors = {}
 	 number = {}
 	 volume = {}
 	 pages = {None},
 	 year = {2003},
 	 month = {Jul},
 	 publisher = {None},
 	 doi = {}
 }