Title: A Novel Kernel for Learning a Neuron Model from Spike Train Data
Abstract: From a functional viewpoint, a spiking neuron is a device that transforms input spike trains on its various synapses into an output spike train on its axon. We demonstrate in this paper that the function mapping underlying the device can be tractably learned based on input and output spike train data alone. We begin by posing the problem in a classification based framework. We then derive a novel kernel for an SRM0 model that is based on PSP and AHP like functions. With the kernel we demonstrate how the learning problem can be posed as a Quadratic Program. Experimental results demonstrate the strength of our approach.
Publication Year: 2010
Publication Date: 2010-12-06
Language: en
Type: article
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Cited By Count: 6
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