Multitaper Analysis of Evolutionary Spectral Density Matrix From Multivariate Spiking Observations

Extracting the spectral representations of the neural processes that underlie spiking activity is key to understanding how the brain rhythms mediate cognitive functions. While spectral estimation of continuous time-series is well studied, inferring the spectral representation of latent processes from spiking observations is a challenging problem. In this paper, we address this issue by proposing a spectral estimation methodology that can be directly applied to multivariate spiking observations in order to extract the evolutionary spectral representation of the latent non-stationary processes. We compare the performance of our proposed technique with several existing methods using simulated data, which reveals significant gains in terms of the bias-variance trade-off.