标题： IntrinsicTimescales.jl：用于从时间序列数据估计内在（神经）时间尺度（INTs）的Julia包 显示英文标题

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

摘要： IntrinsicTimescales.jl 是一个用于估算内在神经时间尺度（INTs）的 Julia 包。 INTs 被定义为先前来自持续刺激的信息能够影响新到达信息处理的时间窗口。 INTs 可以通过时间序列数据的自相关函数（ACF）或功率谱密度（PSD）来估算。 除了模型无关的 INTs 估算之外，IntrinsicTimescales.jl 还提供了通过采用自适应近似贝叶斯计算（aABC）和自动微分变分推断（ADVI）对奥恩斯坦-乌伦贝克过程进行参数估计的新技术实现时间尺度估算的方法。 显示英文摘要

摘要： 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).