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Analysing Physiological Data from the Wake-sleep State Transition with Competing Predictors

We demonstrate that competing neural networks can analyse and predict time series which originate from biological systems with multiple modes of behaviour. Physiological data (eye movements and respiratory signals) measured from wake and sleep phases of humans are studied. With the presented unsupervised method each expert network specializes on a diierent dynam-ical mode and the time series is segmented into diier-ent wake and sleep phases respectively. At the same time the underlying dynamics of the diierent modes is learned by the respective experts.