The Phylogeny of Sleep Database: A New Resource for Sleep Scientists

Capellini, Isabella; Harris, Erica; Nunn, Charles L.; Barton, Robert A.; Preston, Brian T.

doi:10.2174/1874620900801010011

Cited by 37 publications

(27 citation statements)

References 17 publications

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“…This is an important constraint, which we have shown to limit the maximal possible body size of primates, as well as the number of neurons that they can afford-a constraint that human ancestors escaped by radically changing their diet, possibly with cooking [35], but that still did not suffice to decrease their sleep requirement. It will be interesting to examine whether the present hypothesis also explains the long sleep hours of species of the order Carnivora compared with artiodactyls and even primates of similar brain size [7], once data on numbers of neurons and neuronal density become available for carnivores.…”

Section: (D) Implications For Mammalian Evolutionmentioning

confidence: 99%

Decreasing sleep requirement with increasing numbers of neurons as a driver for bigger brains and bodies in mammalian evolution

Herculano‐Houzel

2015

Proc. R. Soc. B.

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Mammals sleep between 3 and 20 h d 21 , but what regulates daily sleep requirement is unknown. While mammalian evolution has been characterized by a tendency towards larger bodies and brains, sustaining larger bodies and brains requires increasing hours of feeding per day, which is incompatible with a large sleep requirement. Mammalian evolution, therefore, must involve mechanisms that tie increasing body and brain size to decreasing sleep requirements. Here I show that daily sleep requirement decreases across mammalian species and in rat postnatal development with a decreasing ratio between cortical neuronal density and surface area, which presumably causes sleepinducing metabolites to accumulate more slowly in the parenchyma. Because addition of neurons to the non-primate cortex in mammalian evolution decreases this ratio, I propose that increasing numbers of cortical neurons led to decreased sleep requirement in evolution that allowed for more hours of feeding and increased body mass, which would then facilitate further increases in numbers of brain neurons through a larger caloric intake per hour. Coupling of increasing numbers of neurons to decreasing sleep requirement and increasing hours of feeding thus may have not only allowed but also driven the trend of increasing brain and body mass in mammalian evolution.

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Section: (D) Implications For Mammalian Evolutionmentioning

confidence: 99%

Decreasing sleep requirement with increasing numbers of neurons as a driver for bigger brains and bodies in mammalian evolution

Herculano‐Houzel

2015

Proc. R. Soc. B.

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“…W = Wake; N = non-REM sleep; R = REM sleep. 85 [Campbell and Tobler, 1984;Tobler, 1995;Tobler and Deboer, 2001;McNamara et al, 2008;Bhagwandin et al, 2011]. Thus, the Cape mole rat, like the previously studied giant Zambian mole rat [Bhagwandin et al, 2011], appears to sleep for a substantially shorter time than the average rodent of a similar body mass [e.g.…”

Section: Discussionmentioning

confidence: 99%

“…Behavioural sleep has been identified in both invertebrates and vertebrates [Campbell and Tobler, 1984;Lesku et al, 2006;Siegel, 2008], with studies of mammalian sleep having been conducted on over 127 species [McNamara et al, 2008]. In general, mammals exhibit both rapid eye movement (REM) and non-REM sleep, but there are exceptions [e.g.…”

Section: Introductionmentioning

confidence: 99%

Sleep in the Cape Mole Rat: A Short-Sleeping Subterranean Rodent

Kruger

Gravett²,

Bhagwandin³

et al. 2016

Brain Behav Evol

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The Cape mole rat Georychus capensis is a solitary subterranean rodent found in the western and southern Cape of South Africa. This approximately 200-gram bathyergid rodent shows a nocturnal circadian rhythm, but sleep in this species is yet to be investigated. Using telemetric recordings of the electroencephalogram (EEG) and electromyogram (EMG) in conjunction with video recordings, we were able to show that the Cape mole rat, like all other rodents, has sleep periods composed of both rapid eye movement (REM) and slow-wave (non-REM) sleep. These mole rats spent on average 15.4 h awake, 7.1 h in non-REM sleep and 1.5 h in REM sleep each day. Cape mole rats sleep substantially less than other similarly sized terrestrial rodents but have a similar percentage of total sleep time occupied by REM sleep. In addition, the duration of both non-REM and REM sleep episodes was markedly shorter in the Cape mole rat than has been observed in terrestrial rodents. Interestingly, these features (total sleep time and episode duration) are similar to those observed in another subterranean bathyergid mole rat, i.e. Fukomys mechowii. Thus, there appears to be a bathyergid type of sleep amongst the rodents that may be related to their environment and the effect of this on their circadian rhythm. Investigating further species of bathyergid mole rats may fully define the emerging picture of sleep in these subterranean African rodents.

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“…At phase 3, 20% -30% of delta frequency (2 -4 Hz) is observed per epoch, and at phase 4, delta frequency (2 -4 Hz) pattern of 50 or more is shown. During the REM sleep, it shows characteristics of pattern that is all mixed frequencies [2]. Table 1 shows a frequency band by the sleep stage and the Figure 1 shows EEG signal waveform by the sleep stage.…”

Section: Introductionmentioning

confidence: 99%

Electroencephalography Analysis Using Neural Network and Support Vector Machine during Sleep

Lee¹,

Yoo²

2013

ENG

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The purpose of this paper is to analyze sleep stages accurately using fast and simple classifiers based on the frequency domain of electroencephalography(EEG) signal. To compare and evaluate system performance, the rules of Rechtschaffen and Kales(R&K rule) were used. Parameters were extracted from preprocessing process of EEG signal as feature vectors of each sleep stage analysis system through representatives of back propagation algorithm and support vector machine (SVM). As a result, SVM showed better performance as pattern recognition system for classification of sleep stages. It was found that easier analysis of sleep stage was possible using such simple system. Since accurate estimation of sleep state is possible through combination of algorithms, we could see the potential for the classifier to be used for sleep analysis system.

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The Phylogeny of Sleep Database: A New Resource for Sleep Scientists

Cited by 37 publications

References 17 publications

Decreasing sleep requirement with increasing numbers of neurons as a driver for bigger brains and bodies in mammalian evolution

Decreasing sleep requirement with increasing numbers of neurons as a driver for bigger brains and bodies in mammalian evolution

Sleep in the Cape Mole Rat: A Short-Sleeping Subterranean Rodent

Electroencephalography Analysis Using Neural Network and Support Vector Machine during Sleep

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