Semi-parametric dynamic time series modelling with applications to detecting neural dynamics

TL;DR

This paper introduces semi-parametric methods combining Bayesian updating and change-point detection for nonstationary time series, with applications to neural data analysis including EEG, behavioral, and spike train recordings.

Contribution

It proposes a novel semi-parametric framework using KL divergence for change-point detection in nonstationary time series, applicable to neuroscience data.

Findings

Effective detection of neural dynamics related to stimuli and learning.

Application to diverse neural data types demonstrating versatility.

Insights into functional neural connectivity changes over time.

Abstract

This paper illustrates novel methods for nonstationary time series modeling along with their applications to selected problems in neuroscience. These methods are semi-parametric in that inferences are derived by combining sequential Bayesian updating with a non-parametric change-point test. As a test statistic, we propose a Kullback--Leibler (KL) divergence between posterior distributions arising from different sets of data. A closed form expression of this statistic is derived for exponential family models, whereas standard Markov chain Monte Carlo output is used to approximate its value and its critical region for more general models. The behavior of one-step ahead predictive distributions under our semi-parametric framework is described analytically for a dynamic linear time series model. Conditions under which our approach reduces to fully parametric state-space modeling are also illustrated. We apply our methods to estimating the functional dynamics of a wide range of neural data, including multi-channel electroencephalogram recordings, longitudinal behavioral experiments and in-vivo multiple spike trains recordings. The estimated dynamics are related to the presentation of visual stimuli, to the evaluation of a learning performance and to changes in the functional connections between neurons over a sequence of experiments.