Synchronization of coupled Kuramoto oscillators competing for resources

TL;DR

This paper extends the Kuramoto model to include resource competition among oscillators, revealing how such dynamics can induce correlations between populations and potentially explain discrepancies in neural connectivity measures.

Contribution

It introduces a novel dynamical resource competition mechanism into the Kuramoto model and demonstrates its impact on synchronization and correlations in coupled oscillator populations.

Findings

Resource competition induces correlations between oscillator populations.

Dynamical resource allocation may explain discrepancies between structural and functional connectivity.

The model provides a framework for analyzing neural computation and connectivity differences.

Abstract

Populations of oscillators are present throughout nature. Very often synchronization is observed in such populations if they are allowed to interact. A paradigmatic model for the study of such phenomena has been the Kuramoto model. However, considering real oscillations are rarely isochronous as a function of energy, it is natural to extend the model by allowing the natural frequencies to vary as a function of some dynamical resource supply. Beyond just accounting for a dynamical supply of resources, however, competition over a \emph{shared} resource supply is important in a variety of biological systems. In neuronal systems, for example, resource competition enables the study of neural activity via fMRI. It is reasonable to expect that this dynamical resource allocation should have consequences for the synchronization behavior of the brain. This paper presents a modified Kuramoto dynamics which includes additional dynamical terms that provide a relatively simple model of resource competition among populations of Kuramoto oscillators. We design a mutlilayer system which highlights the impact of the competition dynamics, and we show that in this designed system, correlations can arise between the synchronization states of two populations of oscillators which share no phase-coupling edges. These correlations are interesting in light of the often observed variance between functional and structural connectivity measures in real systems. The model presented here then suggests that some of the observed discrepancy may be explained by the way in which the brain dynamically allocates resources to different regions according to demand. If true, models such as this one provide a theoretical framework for analyzing the differences between structural and functional measures, and possibly implicate dynamical resource allocation as an integral part of the neural computation process.