Spike Train kernels for multiple neuron recordings

Abstract

There is a growing interest in analyzing multineuron spike trains, which are spike timing data obtained from multiple neurons in the brain. Kernel methods have been successful in clustering and classification of single-neuron spike trains. We extend these methods to multineuron spike trains. Among various possible extensions, the mixture kernel was found to be most effective. The optimum parameter obtained from training this kernel was close to a biologically plausible value, suggesting that our approach is effective for seeking an appropriate model for the activity of a set of neurons.

Kernel Analysis for Estimating the Connectivity of a Network with Event Sequences

“…Finally, the current model only considers pair-wise connectivity. Introducing a multivariate spike train kernel [29,21,40] would extend the analysis to the case where spike trains are governed by population activity.…”

“…Li et al extended spike train kernels to the multivariate case using the sum kernel [21]. In a previous work, a general way to define multivariate spike train kernels based on linear combinations has been introduced [40,41].…”

Parallel spike trains analysis using positive definite kernels

“…To make the extension as natural as possible, we used a linear combination of cross-neuron interactions. We name the new kernel the linear combination of interactions kernel (LCIK) [ 9 ]. The parameters of the kernel can be set to make it positive definite.…”