Introduction and reflections on the comparative physiology of sleep and circadian rhythms.

Circadian rhythms and the sleep/wake cycle allows us, and most life on Earth, to function optimally in a dynamic world, adjusting all aspects of biology to the varied and complex demands imposed by the 24-hour rotation of the Earth upon its axis. A key element in understanding these rhythms, and the success of the field in general, has been because researchers have adopted a comparative approach. Across all taxa, fundamental questions relating to the generation and regulation of sleep and circadian rhythms have been address using biochemical, molecular, cellular, system and computer modelling techniques. Furthermore, findings have been placed into an ecological and evolutionary context. By addressing both the "How" - mechanistic, and "Why" - evolutionary questions in parallel, the field has achieved remarkable successes, including how circadian rhythms are generated and regulated by light. Yet many key questions remain. In this special issue on the Comparative Physiology of Sleep and Circadian Rhythms, celebrating the 100th anniversary of the Journal of Comparative Physiology, important new discoveries are detailed. These findings illustrate the power of comparative physiology to address novel questions and demonstrate that sleep and circadian physiology are embedded within the biological framework of an organism.

Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions.

Electrophysiological recordings from freely behaving animals are a widespread and powerful mode of investigation in sleep research. These recordings generate large amounts of data that require sleep stage annotation (polysomnography), in which the data is parcellated according to three vigilance states: awake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep. Manual and current computational annotation methods ignore intermediate states because the classification features become ambiguous, even though intermediate states contain important information regarding vigilance state dynamics. To address this problem, we have developed "Somnotate"-a probabilistic classifier based on a combination of linear discriminant analysis (LDA) with a hidden Markov model (HMM). First we demonstrate that Somnotate sets new standards in polysomnography, exhibiting annotation accuracies that exceed human experts on mouse electrophysiological data, remarkable robustness to errors in the training data, compatibility with different recording configurations, and an ability to maintain high accuracy during experimental interventions. However, the key feature of Somnotate is that it quantifies and reports the certainty of its annotations. We leverage this feature to reveal that many intermediate vigilance states cluster around state transitions, whereas others correspond to failed attempts to transition. This enables us to show for the first time that the success rates of different types of transition are differentially affected by experimental manipulations and can explain previously observed sleep patterns. Somnotate is open-source and has the potential to both facilitate the study of sleep stage transitions and offer new insights into the mechanisms underlying sleep-wake dynamics.