The sleep cycle: a mathematical analysis from a global workspace perspective

Dretske's technique of invoking necessary conditions from information theory to describe mental process can be used to derive a version of Hobson's AIM treatment of the sleep/wake cycle from a mathematical formulation of Baars' Global Workspace model of consciousness. One implication of the analysis is that some sleep disorders may be recognizably similar to many other chronic, developmental dysfunctions, including autoimmune and coronary heart disease, obesity, hypertension, and anxiety disorder, in that these afflictions often have roots in utero or adverse early childhood experiences or exposures to systematic patterns of structured stress. Identification and alteration of such factors might have considerable impact on population-level patterns of sleep disorders, suggesting the possibility of a public health approach rather than current exorbitantly expensive case-by-case medical intervention

Acetylcholine neuromodulation in normal and abnormal learning and memory: vigilance control in waking, sleep, autism, amnesia, and Alzheimer's disease

This article provides a unified mechanistic neural explanation of how learning, recognition, and cognition break down during Alzheimer's disease, medial temporal amnesia, and autism. It also clarifies whey there are often sleep disturbances during these disorders. A key mechanism is how acetylcholine modules vigilance control in cortical layer

REM sleep deprivation during 5 hours leads to an immediate REM sleep rebound and to suppression of non-REM sleep intensity

Nine healthy male subjects were deprived of REM sleep during the first 5 h after sleep onset. Afterwards recovery sleep was undisturbed. During the deprivation period the non-REM EEG power spectrum was reduced when compared to baseline for the frequencies up to 7 Hz, despite the fact that non-REM sleep was not experimentally disturbed. During the recovery interval a significant rebound of REM sleep was observed, which was only accompanied by a very slight increase of power in the lower non-REM EEG frequencies. In order to control for intermittent wakefulness, the same subjects were subjected to non-REM sleep interruption during the first 5 h after sleep onset 2 weeks later. Again subsequent recovery sleep was undisturbed. The interventions resulted in a similar amount of wakefulness in both conditions. During the intervention period, the non-REM EEG power spectrum was only marginally reduced in the delta frequency range. REM sleep duration was only slightly reduced. During the recovery interval, however, a substantial increase in EEG power in the delta frequency range was noted, without notable changes in REM time. It is concluded that an increased pressure for REM sleep results in longer REM episodes and a reduced intensity of non-REM sleep.

A Fast-Slow Analysis of the Dynamics of REM Sleep

Waking and sleep states are regulated by the coordinated activity of a number of neuronal population in the brainstem and hypothalamus whose synaptic interactions compose a sleep-wake regulatory network. Physiologically based mathematical models of the sleep-wake regulatory network contain mechanisms operating on multiple time scales including relatively fast synaptic-based interations between neuronal populations, and much slower homeostatic and circadian processes that modulate sleep-wake temporal patterning. In this study, we exploit the naturally arising slow time scale of the homeostatic sleep drive in a reduced sleep-wake regulatory network model to utilize fast-slow analysis to investigate the dynamics of rapid eye movement (REM) sleep regulation. The network model consists of a reduced number of wake-, non-REM (NREM) sleep-, and REM sleep-promoting neuronal populations with the synaptic interactions reflecting the mutually inhibitory flip-flop conceptual model for sleep-wake regulation and the reciprocal interaction model for REM sleep regulation. Network dynamics regularly alternate between wake and sleep states as goverend by the slow homeostatic sleep drive. By varying a parameter associated with the activation of the REM-promoting population, we cause REM dynamics during sleep episodes to vary from supression to single activations to regular REM-NREM cycling, corresponding to changes in REM patterning induced by circadian modulation and observed in different mammalian species. We also utilize fast-slow analysis to explain complex effects on sleep-wake patterning of simulated experiments in which agonists and antagonists of different neurotransmitters are microinjected into specific neuronal populations participating in the sleep-wake regulatory network