Kuramoto model for excitation-inhibition-based oscillations

TL;DR

This paper extends the Kuramoto model to include excitatory-inhibitory feedback mechanisms, providing a biologically relevant and analytically solvable framework for understanding neuronal oscillations.

Contribution

It introduces a two-population Kuramoto model that captures EI-based neuronal rhythms, bridging a gap between theoretical models and biological realism.

Findings

Model accurately describes EI feedback-induced oscillations

Analytically solvable, facilitating large-scale neuronal analysis

Provides new insights into brain rhythmogenesis

Abstract

The Kuramoto model (KM) is a theoretical paradigm for investigating the emergence of rhythmic activity in large populations of oscillators. A remarkable example of rhythmogenesis is the feedback loop between excitatory (E) and inhibitory (I) cells in large neuronal networks. Yet, although the EI-feedback mechanism plays a central role in the generation of brain oscillations, it remains unexplored whether the KM has enough biological realism to describe it. Here we derive a two-population KM that fully accounts for the onset of EI-based neuronal rhythms and that, as the original KM, is analytically solvable to a large extent. Our results provide a powerful theoretical tool for the analysis of large-scale neuronal oscillations.