To the role of the choice of the neuron model in spiking network learning on base of Spike-Timing-Dependent Plasticity

https://doi.org/10.1016/j.procs.2018.01.066Get rights and content
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Abstract

The goal of this work is to study the influence of the neuron model choice on the results of STDP learning on base of simple toy tasks. As shown, the resulting mean output firing rate after STDP learning with restricted symmetric spike pairing scheme does not depend on the mean input rates for such neuron models as Leaky Integrate-and-Fire, Traub, and static neuron. Then this effect, being used to solve a typical classification task of Fishers Iris, demonstrates that the classification accuracy does not depend significantly on the choice of the neuron model. Thus, the independence of learning results on the neuron model gives the possibility to use simpler neuron models in further investigations.

Keywords

spike-timing-dependent plasticity
long-term synaptic plasticity
spiking neural networks
computational neuroscience

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