English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Estimation of autocorrelation timescales with Approximate Bayesian Computations

Zeraati, R., Engel, T., & Levina, A. (submitted). Estimation of autocorrelation timescales with Approximate Bayesian Computations.

Item is

Files

show Files

Locators

show
hide
Description:
-

Creators

show
hide
 Creators:
Zeraati, R1, 2, Author              
Engel, TA, Author
Levina, A1, 2, Author              
Affiliations:
1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

Content

show
hide
Free keywords: -
 Abstract: 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.

Details

show
hide
Language(s):
 Dates: 2020-08
 Publication Status: Submitted
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1101/2020.08.11.245944
 Degree: -

Event

show

Legal Case

show

Project information

show

Source

show