Sleep Origins: Restful Jellyfish Are Sleeping Jellyfish

The interplay between sleep and ecophysiology, behaviour and responses to environmental change in fish

Evidence of behavioural sleep has been observed in every animal species studied to date, but current knowledge of the behaviour, neurophysiology and ecophysiology associated with sleep is concentrated on mammals and birds. Fish are a hugely diverse group that can offer novel insights into a variety of sleep-related behaviours across environments, but the ecophysiological relevance of sleep in fish has been largely overlooked. Here, we systematically reviewed the literature to assess the current breadth of knowledge on fish sleep, and surveyed the diverse physiological effects and behaviours associated with sleep. We also discuss possible ways in which unstudied external factors may alter sleep behaviours. For example, predation risk may alter sleep patterns, as has been shown in mammalian, avian and reptilian species. Other environmental factors - such as water temperature and oxygen availability - have the potential to alter sleep patterns in fish differently than for terrestrial endotherms. Understanding the ecological influences on sleep in fish is vital, as sleep deprivation has the potential to affect waking behaviour and fitness owing to cognitive and physiological impairments, possibly affecting ecological phenomena and sensitivity to environmental stressors in ways that have not been considered.

Neurobiology of dream activity and effects of stimulants on dreams

The sleep-wake cycle is the result of the activity of a multiple neurobiological network interaction. Dreaming feature is one interesting sleep phenomena that represents sensorial components, mostly visual perceptions, accompanied with intense emotions. Further complexity has been added to the topic of the neurobiological mechanism of dreams generation by the current data that suggests the influence of drugs on dream generation. Here, we discuss the review on some of the neurobiological mechanism of the regulation of dream activity, with special emphasis on the effects of stimulants on dreaming.

Do animals dream?

The understanding of biological functions of sleep has improved recently, including an understanding of the deep evolutionary roots of sleep among animals. However, dreaming as an element of sleep may be particularly difficult to address in non-human animals because in humans dreaming involves a non-wakeful form of awareness typically identified through verbal report. Here, we argue that parallels that exist between the phenomenology, physiology, and sleep behaviors during human dreaming provide an avenue to investigate dreaming in non-human animals. We review three alternative measurements of human dreaming - neural correlates of dreaming, 'replay' of newly-acquired memories, and dream-enacting behaviors - and consider how these may be applied to non-human animal models. We suggest that while animals close in brain structure to humans (such as mammals and birds) may be optimal models for the first two of these measurements, cephalopods, especially octopuses, may be particularly good candidates for the third.

Exploring phylogeny to find the function of sleep

During sleep, animals do not eat, reproduce or forage. Sleeping animals are vulnerable to predation. Yet, the persistence of sleep despite evolutionary pressures, and the deleterious effects of sleep deprivation, indicate that sleep serves a function or functions that cannot easily be bypassed. Recent research demonstrates sleep to be phylogenetically far more pervasive than previously appreciated; it is possible that the very first animals slept. Here, we give an overview of sleep across various species, with the aim of determining its original purpose. Sleep exists in animals without cephalized nervous systems and can be influenced by non-neuronal signals, including those associated with metabolic rhythms. Together, these observations support the notion that sleep serves metabolic functions in neural and non-neural tissues.

Evidence for Sleep in Sharks and Rays: Behavioural, Physiological, and Evolutionary Considerations

Sleep is widespread across the animal kingdom. However, most comparative sleep data exist for terrestrial vertebrates, with much less known about sleep in amphibians, bony fishes, and invertebrates. There is an absence of knowledge on sleep in cartilaginous fishes. Sharks and rays are amongst the earliest vertebrates, and may hold clues to the evolutionary history of sleep and sleep states found in more derived animals, such as mammals and birds. Here, we review the literature concerning activity patterns, sleep behaviour, and electrophysiological evidence for sleep in cartilaginous (and bony) fishes following an exhaustive literature search that found more than 80 relevant studies in laboratory and field environments. Evidence for sleep in sharks and rays that respire without swimming is preliminary; evidence for sleep in continuously swimming fishes is currently absent. We discuss ways in which the latter group might sleep concurrent with sustained movement, and conclude with suggestions for future studies in order to provide more comprehensive data on when, how, and why sharks and rays sleep.

Validation of 'Somnivore', a Machine Learning Algorithm for Automated Scoring and Analysis of Polysomnography Data

Manual scoring of polysomnography data is labor-intensive and time-consuming, and most existing software does not account for subjective differences and user variability. Therefore, we evaluated a supervised machine learning algorithm, Somnivore^TM, for automated wake–sleep stage classification. We designed an algorithm that extracts features from various input channels, following a brief session of manual scoring, and provides automated wake-sleep stage classification for each recording. For algorithm validation, polysomnography data was obtained from independent laboratories, and include normal, cognitively-impaired, and alcohol-treated human subjects (total n = 52), narcoleptic mice and drug-treated rats (total n = 56), and pigeons (n = 5). Training and testing sets for validation were previously scored manually by 1–2 trained sleep technologists from each laboratory. F-measure was used to assess precision and sensitivity for statistical analysis of classifier output and human scorer agreement. The algorithm gave high concordance with manual visual scoring across all human data (wake 0.91 ± 0.01; N1 0.57 ± 0.01; N2 0.81 ± 0.01; N3 0.86 ± 0.01; REM 0.87 ± 0.01), which was comparable to manual inter-scorer agreement on all stages. Similarly, high concordance was observed across all rodent (wake 0.95 ± 0.01; NREM 0.94 ± 0.01; REM 0.91 ± 0.01) and pigeon (wake 0.96 ± 0.006; NREM 0.97 ± 0.01; REM 0.86 ± 0.02) data. Effects of classifier learning from single signal inputs, simple stage reclassification, automated removal of transition epochs, and training set size were also examined. In summary, we have developed a polysomnography analysis program for automated sleep-stage classification of data from diverse species. Somnivore enables flexible, accurate, and high-throughput analysis of experimental and clinical sleep studies.