Spike-timing-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 pre-synaptic spikes and applied to e.ectively train a neuron in detecting temporal sequences of spike trains. The trained neuron responds reliably under di.erent regimes and types of noise.

@Article{PW04, 
 	 author =  {Panchev, Christo and Wermter, Stefan},  
 	 title = {Spike-timing-dependent Synaptic Plasticity: From Single Spikes to Spike Trains}, 
 	 journal = {Neurocomputing},
 	 number = {},
 	 volume = {58--60},
 	 pages = {365--371},
 	 year = {2004},
 	 month = {Jun},
 	 publisher = {Elsevier},
 	 doi = {10.1016/j.neucom.2004.01.068}, 
 }