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Related Experiment Video

Updated: Jun 29, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Linking fast and slow: The case for generative models.

Johan Medrano¹, Karl Friston¹, Peter Zeidman¹

¹The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK.

Network Neuroscience (Cambridge, Mass.)

|April 2, 2024

Summary

This summary is machine-generated.

Neuroscience research can now analyze brain activity across milliseconds to years. Hierarchical models and Bayesian inference reveal underlying brain mechanisms, not just correlations.

Keywords:

Bayesian statistics Dynamical systems Generative models Hidden Markov models Hierarchical modelling Temporal scales

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Area of Science:

Neuroscience
Computational Neuroscience
Complex Systems Modeling

Background:

Analyzing neuronal connectivity changes over time is a key neuroscience challenge.
Advances in recording technology allow for longer, more naturalistic neuronal data acquisition.
Understanding the self-organized brain requires methods linking diverse temporal scales.

Purpose of the Study:

To demonstrate how hierarchical generative models and Bayesian inference can characterize neuronal activity across multiple timescales.
To provide an overview of state-space modeling concepts and a taxonomy for these methods.
To introduce mathematical principles for temporal scale separation and review Bayesian methods for hypothesis testing.

Main Methods:

Hierarchical generative models
Bayesian inference
State-space modeling
Slaving principle

Main Results:

Hierarchical models and Bayesian inference enable inference about underlying neuronal mechanisms.
These methods link neuronal dynamics across milliseconds to years.
The review provides a framework for analyzing multiscale brain data.

Conclusions:

Hierarchical generative models and Bayesian inference are powerful tools for understanding neuronal connectivity changes.
These approaches move beyond statistical associations to mechanistic inference.
The review serves as a primer for neuroscientists on multiscale data analysis techniques.