论文信息 - Spike-timing-dependent synaptic plasticity: from single spikes to spike trains

Spike-timing-dependent synaptic plasticity: from single spikes to spike trains

Abstract 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 effectively train a neuron in detecting temporal sequences of spike trains. The trained neuron responds reliably under different regimes and types of noise.

Stefan Wermter | Christo Panchev | S. Wermter | C. Panchev

[1] Gustavo Deco,et al. Temporal clustering with spiking neurons and dynamic synapses: towards technological applications , 2001, Neural Networks.

[2] Henry Markram,et al. Neural Networks with Dynamic Synapses , 1998, Neural Computation.

[3] Stefan Wermter,et al. Spike-Timing Dependent Competitive Learning of Integrate-and-Fire Neurons with Active Dendrites , 2002, ICANN.

[4] L. Abbott,et al. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity , 2000, Nature Neuroscience.

[5] Stefan Wermter,et al. HEBBIAN SPIKE-TIMING DEPENDENT SELF-ORGANIZATION IN PULSED NEURAL NETWORKS , 2001 .

[6] Walter Senn,et al. Hebb in perspective , 2002, Biological Cybernetics.

[7] G. Bi,et al. Synaptic modification by correlated activity: Hebb's postulate revisited. , 2001, Annual review of neuroscience.

[8] Thomas Natschläger,et al. Pattern analysis with spiking neurons using delay coding , 1999, Neurocomputing.