A stochastic neural mass model for typical rhythms during sleep-wake transitions: Insights into dynamics driven by...

Sleep-wake transitions are essential for normal brain function. Although auditory noise can trigger sleep-wake transitions, how it influences the neural dynamics underlying these transitions and associated EEG rhythms remains unclear. Here, we propose a stochastic neural mass model to investigate how auditory noise modulates shifts in typical EEG rhythms during sleep-wake transitions.

The model integrates the intrinsic sleep-wake regulatory circuit with rhythm-generating mechanisms and incorporates Gaussian colored noise (GCN) to represent auditory noise. Numerical simulations combined with deterministic and stochastic bifurcation-related analyses are used to evaluate how noise intensity, correlation rate, and intrinsic regulatory parameters influence transitions between alpha waves and slow oscillations.

The model reproduces characteristic EEG rhythms during sleep and wakefulness, namely slow oscillations and alpha waves, and shows that the system exhibits a bistable regime in which slow oscillation and alpha wave limit cycles coexist as distinct attractors, with GCN inducing transitions between them. Increasing noise intensity and correlation rate bias the system toward the alpha waves associated with wakefulness, whereas lower values favor the slow oscillations associated with sleep. In addition, variations in intrinsic sleep-wake regulatory parameters reshape the basins of attraction of attractors, thereby modulating susceptibility to GCN-induced transitions.

These results provide a model-based dynamical explanation for how auditory noise may modulate switching between typical EEG rhythm patterns associated with sleep-wake transitions and a reduced computational framework for investigating how auditory noise interacts with intrinsic neural regulation to influence sleep-wake rhythms. The framework may support future studies on sleep-wake regulation.