Fluctuation induced intermittent transitions between distinct rhythms in balanced excitatory-inhibitory spiking networks

TL;DR

This paper presents a neuronal network model demonstrating that intrinsic fluctuations near a Hopf bifurcation can produce intermittent transitions between cortical rhythms, explaining observed power-law distributions in sleep-related brain activity.

Contribution

The study introduces a novel network model of excitatory and inhibitory neurons that accounts for intermittent cortical rhythm transitions through intrinsic fluctuations near a bifurcation point.

Findings

Power-law distributions of rhythm durations are reproduced by the model.

Connectivity modifications influence the power-law exponents.

Model aligns with empirical EEG observations of sleep dynamics.

Abstract

Intermittent transitions, associated with critical dynamics and characterized by power-law distributions, are commonly observed during sleep. These critical behaviors are evident at the microscopic level through neuronal avalanches and at the macroscopic level through transitions between sleep stages. To clarify these empirical observations, models grounded in statistical physics have been proposed. At the mesoscopic level of cortical activity, critical behavior is indicated by the intermittent transitions between various cortical rhythms. For instance, empirical investigations utilizing EEG data from rats have identified intermittent transitions between $\delta$ and $\theta$ rhythms, with the duration of $\theta$ rhythm exhibiting a power-law distribution. However, a dynamic model to account for this phenomenon is currently absent. In this study, we introduce a network of sparsely coupled excitatory and inhibitory populations of quadratic integrate-and-fire (QIF) neurons to demonstrate that intermittent transitions can emerge from the intrinsic fluctuations of a finite-sized system, particularly when the system is positioned near a Hopf bifurcation point, which is a critical point. The resulting power-law distributions and exponents are consistent with empirical observations. Additionally, we illustrate how modifications in network connectivity can affect the power-law exponent by influencing the attractivity and oscillation frequency of the stable limit cycle. Our findings, interpreted through the fundamental dynamics of neuronal networks, provide a plausible mechanism for the generation of intermittent transitions between cortical rhythms, in alignment with the power-law distributions documented in empirical researches.