Sleep homeostasis manifests as a relative constancy of its daily amount and intensity. Theoretical descriptions define ‘Process S’, a variable with dynamics dependent on global sleep-wake history, and reflected in electroencephalogram (EEG) slow wave activity (SWA, 0.5–4 Hz) during sleep. The notion of sleep as a local, activity-dependent process suggests that activity history must be integrated to determine the dynamics of global Process S. Here, we developed novel mathematical models of Process S based on cortical activity recorded in freely behaving mice, describing local Process S as a function of the deviation of neuronal firing rates from a locally defined set-point, independent of global sleep-wake state. Averaging locally derived Processes S and their rate parameters yielded values resembling those obtained from EEG SWA and global vigilance states. We conclude that local Process S dynamics reflects neuronal activity integrated over time, and global Process S reflects local processes integrated over space.

中文翻译：

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整体睡眠稳态反映了时间和空间上整合的局部皮质神经元活动


睡眠稳态表现为每日睡眠量和强度的相对恒定。理论描述定义了“过程 S”，这是一个动态变量，取决于整体睡眠-觉醒历史，并反映在睡眠期间的脑电图 (EEG) 慢波活动（SWA，0.5–4 Hz）中。睡眠作为一个局部的、依赖于活动的过程的概念表明，必须整合活动历史以确定全局过程 S 的动态。在这里，我们基于自由行为小鼠记录的皮层活动开发了新的过程 S 数学模型，描述了局部过程 S过程 S 作为神经元放电率与局部定义设定点的偏差的函数，与全局睡眠-觉醒状态无关。对本地导出的过程 S 及其速率参数进行平均，得到的值与从 EEG SWA 和全局警戒状态获得的值类似。我们得出的结论是，局部过程 S 动态反映了随时间整合的神经元活动，而全局过程 S 反映了随空间整合的局部过程。