# Time-varying coupling functions: dynamical inference and cause of synchronization transitions

## TL;DR

This paper investigates how time-varying coupling functions, independent of coupling strength, can induce synchronization transitions in biological systems, demonstrated through EEG analysis and phase oscillator models.

## Contribution

It introduces a method to detect synchronization caused solely by changes in coupling functions using dynamical Bayesian inference.

## Key findings

- Time-varying coupling functions are present in biological interactions.
- Synchronization transitions can occur without changes in coupling strength.
- Dynamical Bayesian inference effectively detects time-varying coupling functions.

## Abstract

Interactions in nature can be described by their coupling strength, direction of coupling and coupling function. The coupling strength and directionality are relatively well understood and studied, at least for two interacting systems, however there can be a complexity in the interactions uniquely dependent on the coupling functions. Such a special case is studied here { synchronization transition occurs only due to the time-variability of the coupling functions, while the net coupling strength is constant throughout the observation time. To motivate the investigation, an example is used to present an analysis of cross-frequency coupling functions between delta and alpha brainwaves extracted from the electroencephalography (EEG) recording of a healthy human subject in a freerunning resting state. The results indicate that time-varying coupling functions are a reality for biological interactions. A model of phase oscillators is used to demonstrate and detect the synchronization transition caused by the varying coupling functions, during an invariant coupling strength. The ability to detect this phenomenon is discussed with the method of dynamical Bayesian inference, which was able to infer the time-varying coupling functions. The form of the coupling function acts as an additional dimension for the interactions and it should be taken into account when detecting biological or other interactions from data.