Estimation of autocorrelation timescales with Approximate Bayesian Computations

Zeraati, Roxana, Engel, T. A., Levina, Anna (August 2020) Estimation of autocorrelation timescales with Approximate Bayesian Computations. bioRxiv. (Unpublished)

DOI: 10.1101/2020.08.11.245944


Timescales characterize the pace of change for many dynamic processes in nature: radioactive decay, metabolization of substances, memory decay in neural systems, and epidemic spreads. Measuring timescales from experimental data can reveal underlying mechanisms and constrain theoretical models. Timescales are usually estimated by fitting the autocorrelation of sample time-series with exponential decay functions. We show that this standard procedure often fails to recover the correct timescales, exhibiting large estimation errors due to a statistical bias in autocorrelations of finite data samples. To overcome this bias, we develop a method using adaptive Approximate Bayesian Computations. Our method estimates the timescales by fitting the autocorrelation of sample data with a generative model based on a mixture of Ornstein-Uhlenbeck processes. The method accounts for finite sample size and noise in data and returns a posterior distribution of timescales quantifying the estimation uncertainty. We demonstrate how the posterior distribution can be used for model selection to compare alternative hypotheses about the dynamics of the underlying process. Our method accurately recovers the correct timescales on synthetic data from various processes with known ground truth dynamics. We illustrate its application to electrophysiological recordings from the primate cortex.

Item Type: Paper
CSHL Authors:
Communities: CSHL labs > Engel lab
Depositing User: Matthew Dunn
Date: 12 August 2020
Date Deposited: 07 Dec 2020 15:18
Last Modified: 07 Dec 2020 15:18

Actions (login required)

Administrator's edit/view item Administrator's edit/view item
CSHL HomeAbout CSHLResearchEducationNews & FeaturesCampus & Public EventsCareersGiving