Sparse spectral estimation from point process observations
Sina Miran, Patrick L. Purdon, Emery N. Brown, Behtash Babadi
Abstract
We consider the problem of estimating the power spectral density of the neural covariates underlying the spiking of a neuronal population. We assume the spiking of the neuronal ensemble to be described by Bernoulli statistics. Furthermore, we consider the conditional intensity function to be the logistic map of a second-order stationary process with sparse frequency content. Using the binary spiking data recorded from the population, we calculate the maximum a posteriori estimate of the power spectral density of the process while enforcing sparsity-promoting priors on the estimate. Using both simulated and clinically recorded data, we show that our method outperforms the existing methods for extracting a frequency domain representation from the spiking data of a neuronal population.
BibTeX
@inproceedings{icassp2017_sparsespectrales,
title = {Sparse spectral estimation from point process observations},
author = {Sina Miran and Patrick L. Purdon and Emery N. Brown and Behtash Babadi},
booktitle = {ICASSP 2017},
year = {2017}
}