ArticleNature computational science2022
A flexible Bayesian framework for unbiased estimation of timescales.
Article in Nature computational science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Neural timescales from a computational perspective.Nature neuroscience · 2026Review
- Association-sensory spatiotemporal hierarchy and functional gradient-regularised recurrent neural network with implications for schizophrenia.NPJ systems biology and applications · 2026Article
- Emergent critical oscillations in motor cortex of Parkinson's patients.bioRxiv : the preprint server for biology · 2026Article
- iSTTC: A robust method for accurate estimation of intrinsic neural timescales from single-unit recordings.PLoS computational biology · 2026Article
- Article
- Neurons in auditory cortex integrate information within a constrained and context-invariant temporal window.Current biology : CB · 2025Article
- A doubly stochastic renewal framework for partitioning spiking variability.Nature communications · 2025Article
- Neurons in auditory cortex integrate information within constrained temporal windows that are invariant to the stimulus context and information rate.bioRxiv : the preprint server for biology · 2025Article
- Brain-wide organization of intrinsic timescales at single-neuron resolution.bioRxiv : the preprint server for biology · 2025Article
- Evaluating in-vivo spontaneous firing rate in the brain based on neuronal noise modeling.Communications biology · 2025Article
- Two modes in the absolute velocity statistics in cautious walks of laboratory rodents.Biophysical journal · 2025Article
- Detecting existence of a hidden mediator between a pair of individual time series.Scientific reports · 2025Article
- Ramping dynamics in the frontal cortex unfold over multiple timescales during motor planning.Journal of neurophysiology · 2025Article
- Signatures of hierarchical temporal processing in the mouse visual system.PLoS computational biology · 2024Article
- A neurophysiological basis for aperiodic EEG and the background spectral trend.Nature communications · 2024Article
- Ramping Dynamics in the Frontal Cortex Unfold Over Multiple Timescales During Motor Planning.bioRxiv : the preprint server for biology · 2024Article
- Strategies for motion- and respiration-robust estimation of fMRI intrinsic neural timescales.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Long-term changes in heart rate and electrical remodeling contribute to alternans formation in heart failure: a patient-specific in silico study.American journal of physiology. Heart and circulatory physiology · 2023Article
- Intrinsic timescales in the visual cortex change with selective attention and reflect spatial connectivity.Nature communications · 2023Article
- A flexible Bayesian framework for unbiased estimation of timescales.Nature computational science · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Timescales characterize the pace of change for many dynamic processes in nature. Timescales 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 which estimates timescales by fitting the sample autocorrelation or power spectrum with a generative model based on a mixture of Ornstein-Uhlenbeck (OU) processes using adaptive approximate Bayesian computations (aABC). 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 primate cortex. We provide a customizable Python package implementing our framework with different generative models suitable for diverse applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.