Abstract
AbstractTimescales characterize the pace of change for many dynamic processes in nature. They are usually estimated by fitting the exponential decay of data autocorrelation in the time or frequency domain. Here we show that this standard procedure often fails to recover the correct timescales due to a statistical bias arising from the finite sample size. We develop an alternative approach to estimate timescales by fitting the sample autocorrelation or power spectrum with a generative model based on a mixture of Ornstein–Uhlenbeck processes using adaptive approximate Bayesian computations. Our method accounts for finite sample size and noise in data and returns a posterior distribution of timescales that quantifies the estimation uncertainty and can be used for model selection. We demonstrate the accuracy of our method on synthetic data and illustrate its application to recordings from the primate cortex. We provide a customizable Python package that implements our framework via different generative models suitable for diverse applications.
Funder
Volkswagen Foundation
Foundation for the National Institutes of Health
Pershing Square Foundation
Alfred P. Sloan Foundation
Alexander von Humboldt-Stiftung
Bundesministerium für Bildung und Forschung
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Computer Science Applications,Computer Science (miscellaneous)
Reference60 articles.
1. Murray, J. D. et al. A hierarchy of intrinsic timescales across primate cortex. Nat. Neurosci. 17, 1661 (2014).
2. Watanabe, T., Rees, G. & Masuda, N. Atypical intrinsic neural timescale in autism. eLife 8, e42256 (2019).
3. Gao, R., van den Brink, R. L., Pfeffer, T. & Voytek, B. Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture. eLife 9, e61277 (2020).
4. Zeraati, R. et al. Attentional modulation of intrinsic timescales in visual cortex and spatial networks. Preprint at https://www.biorxiv.org/content/10.1101/2021.05.17.444537v1 (2021).
5. Wilting, J. & Priesemann, V. Inferring collective dynamical states from widely unobserved systems. Nat. Commun. 9, 1–7 (2018).
Cited by
13 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献