# Multitaper Spectral Analysis of Neuronal Spiking Activity Driven by Latent Stationary Processes

## TL;DR

This paper introduces a specialized multitaper spectral estimation method for neuronal spiking data, enabling more accurate analysis of brain rhythms underlying neural activity.

## Contribution

It develops a novel variant of the multitaper method tailored for point process data, improving spectral estimation accuracy in neuroscience applications.

## Key findings

- Significant reduction in bias and variance compared to existing methods
- Efficient computation of spectral estimates from spiking data
- Enhanced ability to infer latent neural processes

## Abstract

Investigating the spectral properties of the neural covariates that underlie spiking activity is an important problem in systems neuroscience, as it allows to study the role of brain rhythms in cognitive functions. While the spectral estimation of continuous time-series is a well-established domain, computing the spectral representation of these neural covariates from spiking data sets forth various challenges due to the intrinsic non-linearities involved. In this paper, we address this problem by proposing a variant of the multitaper method specifically tailored for point process data. To this end, we construct auxiliary spiking statistics from which the eigen-spectra of the underlying latent process can be directly inferred using maximum likelihood estimation, and thereby the multitaper estimate can be efficiently computed. Comparison of our proposed technique to existing methods using simulated data reveals significant gains in terms of the bias-variance trade-off.