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

IntrinsicTimescales.jl: A Julia package to estimate intrinsic (neural) timescales (INTs) from time-series data - Published in JOSS (2025)

https://github.com/duodenum96/intrinsictimescales.jl

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Keywords

bayesian-inference intrinsic-neural-timescales neuroscience neuroscience-methods parameter-estimation

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A Julia package to estimate Intrinsic (Neural) Timescales (INTs) from time-series data

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Topics
bayesian-inference intrinsic-neural-timescales neuroscience neuroscience-methods parameter-estimation
Created about 1 year ago · Last pushed 2 months ago
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README.md

Documentation Documentation

IntrinsicTimescales.jl

IntrinsicTimescales.jl is a software package for estimating Intrinsic Neural Timescales (INTs) from time-series data. It uses model-free methods (ACW-50, ACW-0, fitting an exponential decay function etc.) and simulation-based methods (adaptive approximate Bayesian computation: aABC, automatic differentiation variational inference: ADVI) to estimate INTs.

The documentation is available here.

Installation

This package is written in Julia. If you do not have Julia installed, you can install it from here. Once you have Julia installed, you can install IntrinsicTimescales.jl by running the following command in the Julia REPL:

julia using Pkg Pkg.add("IntrinsicTimescales") Soon, there will also be a Python wrapper called INTpy, which will allow you to use IntrinsicTimescales.jl from Python.

Quickstart

IntrinsicTimescales.jl uses two ways to estimate INTs: model-free methods and simulation-based inference. Model-free methods include ACW-50, ACW-0, ACW-e, area under the curve (AUC), decay rate of an exponential fit to ACF and knee freqency of a lorentzian fit to PSD. Simulation-based methods are based on Zeraati et al. (2022) paper and do parameter estimation by assuming the data came from an Ornstein-Uhlenbeck process. For estimation, in addition to the adaptive approximate Bayesian computation (aABC) method used in Zeraati et al. (2022), we also present automatic differentiation variational inference (ADVI). Additionally, we adapt the aABC method with adaptive choice of epsilon. See documentation for details.

For model-free methods, simply use the acw function.

```julia using IntrinsicTimescales

data = randn(10, 5000) # Data in the form of (trials x time) fs = 100.0 # Sampling frequency

acwresults = acw(data, fs; acwtypes = [:acw0, :acw50, :acweuler, :auc, :tau, :knee], dims=ndims(data))

or even simpler:

acwresults = acw(data, fs) ```

where fs is sampling frequency, optional parameters acwtypes is a vector of symbols (indicated with :) telling which methods to use and dims is indicating the dimension of time in your array (by default, the last dimension). The resulting acwresults gives the results in the same order of acwtypes.

For simulation based methods, pick one of the one_timescale_model, one_timescale_with_missing_model, one_timescale_and_osc_model and one_timescale_and_osc_with_missing_model functions. These models correspond to different generative models depending on whether there is an oscillation or not. For each generative model, there are with or without missing variants which use different ways to calculate ACF and PSD. Once you pick the model, the syntax is

```julia

Simulate some data

using Random timescale = 0.3 # true timescales variance = 1.0 # variance of data duration = 10.0 # duration of data ntrials = 2 # How many trials fs = 500.0 # Sampling rate rng = Xoshiro(123); deqseed = 123 # For reproducibility data = generateouprocess(timescale, variance, 1/fs, duration, ntrials, rng=rng, deqseed=deqseed) # Data in the form of (trials x time) time = (1/fs):(1/fs):duration # Vector of time points model = onetimescalemodel(data, time, :abc) result = intfit(model) ```

or

julia model = one_timescale_model(data, time, :advi) result = int_fit(model)

These functions are highly customizable, see the documentation for details.

Getting Help and Making Contributions

Questions and contributions are welcome. Use the issues section of our github page to report bugs, make feature requests, ask questions or tackle the issues by making pull requests.

Want to learn more?

Kindly read the fine manual.

Roadmap

The current roadmap of the package is here. Feel free to make suggestions / contributions.

License

This project is licensed under the MIT License - see the LICENSE file for details. Note that certain dependencies (such as FFTW) may not have MIT licenses.

Owner

  • Login: duodenum96
  • Kind: user

JOSS Publication

IntrinsicTimescales.jl: A Julia package to estimate intrinsic (neural) timescales (INTs) from time-series data
Published
October 28, 2025
DOI
Volume 10, Issue 114, Page 8261
Authors
Yasir Çatal ORCID
Mind, Brain Imaging and Neuroethics Research Unit, University of Ottawa, Ontario, ON, Canada., University of Ottawa Institute of Mental Health Research, Ottawa, ON, Canada.
Georg Northoff ORCID
Mind, Brain Imaging and Neuroethics Research Unit, University of Ottawa, Ontario, ON, Canada., University of Ottawa Institute of Mental Health Research, Ottawa, ON, Canada.
Editor
Britta Westner ORCID
Tags
neuroscience neuroscience methods intrinsic timescales bayesian estimation
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juliahub.com: IntrinsicTimescales

A Julia package to estimate Intrinsic (Neural) Timescales (INTs) from time-series data

  • Versions: 15
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