Sparse Spike Train Deconvolution Using the Hunt Filter and a Thresholding Method

Abstract

A new deconvolution method of sparse spike trains is presented. It is based on the coupling of the Hunt filter with a thresholding. We show that a good model for the probability density function of the Hunt filter output is a Gaussian mixture, from which we derive the threshold that minimizes the probability of errors. Based on an interpretation of the method as a maximum a posteriori (MAP) estimator, the hyperparameters are estimated using a joint MAP approach. Simulations show that this method performs well at a very low computation time.