IntrinsicTimescales.jl: A Julia package to estimate intrinsic (neural) timescales (INTs) from time-series data

TL;DR

IntrinsicTimescales.jl is a Julia package that provides methods to estimate neural timescales from time-series data using autocorrelation, spectral analysis, and advanced Bayesian techniques.

Contribution

The package introduces novel estimation methods for neural timescales, including adaptive Bayesian computation and variational inference, enhancing analysis capabilities.

Findings

Provides model-free estimates of neural timescales

Includes advanced Bayesian estimation techniques

Facilitates analysis of neural data in Julia

Abstract

IntrinsicTimescales.jl is a Julia package to perform estimation of intrinsic neural timescales (INTs). INTs are defined as the time window in which prior information from an ongoing stimulus can affect the processing of newly arriving information. INTs are estimated either from the autocorrelation function (ACF) or the power spectral density (PSD) of time-series data. In addition to the model-free estimates of INTs, IntrinsicTimescales.jl offers implementations of novel techniques of timescale estimation via performing parameter estimation of an Ornstein-Uhlenbeck process with adaptive approximate Bayesian computation (aABC) and automatic differentiation variational inference (ADVI).