Learning the bistable cortical dynamics of the sleep-onset period

Humans just dont fall asleep like a log - or step-function. Rather, the sleep-onset period (SOP) exhibits dynamic and non-monotonous changes of electroencephalogram (EEG) with high, and so far poorly understood, intra- and inter-individual variability. Computational models of the sleep regulation network have suggested that the transition to sleep can be viewed as a noisy bifurcation at a saddle point which is determined by an underlying control signal or "sleep drive". However, such models do not describe how internal control signals in the SOP can produce rapid switches between stable wake and sleep states, nor how these state-space changes are translated in the macroscopic EEG. Here, we propose a minimally-parameterized stochastic dynamical model, in which one slowly-varying control parameter drives the wake-to-sleep transition while exhibiting noise-driven bistability. We provide a procedure for estimating the parameters of the model given single observations of experimental sleep EEG data, and show that it can reproduce a wide variety of SOP phenomenology. Using the model to analyze a pre-existing sleep EEG dataset, we find that the estimated model parameters correlate with subjective sleepiness reports. These results suggest that the bistable characteristics of the SOP can serve as biomarkers for tracking intra- and inter-individual variability of sleep-onset disorders. Author summaryRecent neuroscience research has showed a growing interest in understanding the complexity of how we fall asleep. Electroencephalographic (EEG) recordings of the sleep onset period show all the hallmarks of a noise-driven bistable system, but there currently exists no computational model that can be fitted on experimental data to understand this behavior. In this paper, we propose a minimally-parameterized model, which dynamics corresponds to the motion of an noisy overdamped particle in a slowly tilting bistable landscape, as well as a way to fit it to an individuals sleep-onset EEG. We show that the fitted parameters of individual participants correlate with their subjective reports of sleepiness, suggesting that the model can capture important aspects of inter-individual variability, as well as provide potential biomarkers for sleep-onset disorders.