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Review
, 81 (1), 12-34

Sleep and the Price of Plasticity: From Synaptic and Cellular Homeostasis to Memory Consolidation and Integration

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Review

Sleep and the Price of Plasticity: From Synaptic and Cellular Homeostasis to Memory Consolidation and Integration

Giulio Tononi et al. Neuron.

Abstract

Sleep is universal, tightly regulated, and its loss impairs cognition. But why does the brain need to disconnect from the environment for hours every day? The synaptic homeostasis hypothesis (SHY) proposes that sleep is the price the brain pays for plasticity. During a waking episode, learning statistical regularities about the current environment requires strengthening connections throughout the brain. This increases cellular needs for energy and supplies, decreases signal-to-noise ratios, and saturates learning. During sleep, spontaneous activity renormalizes net synaptic strength and restores cellular homeostasis. Activity-dependent down-selection of synapses can also explain the benefits of sleep on memory acquisition, consolidation, and integration. This happens through the offline, comprehensive sampling of statistical regularities incorporated in neuronal circuits over a lifetime. This Perspective considers the rationale and evidence for SHY and points to open issues related to sleep and plasticity.

Figures

Figure 1
Figure 1. The Synaptic Homeostasis Hypothesis (SHY)
Figure 2
Figure 2. SHY, wake-sleep cycles, and the plasticity-stability dilemma
Top: during wake the brain interacts with the environment (grand loop) and samples a limited number of inputs dictated by current events (current sampling, here represented by a new acquaintance). High levels of neuromodulators, such as noradrenaline released by the locus coeruleus (LC), ensure that suspicious coincidences related to the current sampling percolate through the brain and lead to synaptic potentiation. Bottom: during sleep, when the brain is disconnected from the environment on both the sensory and motor sides, spontaneous activity permits a comprehensive sampling of the brain's knowledge of the environment, including old memories about people, places, etc. Low levels of neuromodulators, combined with the synchronous, ON and OFF firing pattern of many neurons during NREM sleep events such as slow waves, spindles, and sharp-wave ripples, are conducive to synaptic down-selection: synapses belonging to the fittest circuits, those that were strengthened repeatedly during wake and/or are better integrated with older memories are protected and survive. By contrast, synapses belonging to circuits that were only rarely activated during wake and/or fit less well with old memories, are progressively depressed and eventually eliminated over many wake/sleep cycles. The green lines in the sleeping brain (right), taken from (Murphy et al., 2009), illustrate the propagation of slow waves during NREM sleep, as established using high-density EEG and source modeling.
Figure 3
Figure 3. Evidence supporting SHY
A, experiments in rats and mice show that the number and phosphorylation levels of GluA1-AMPARs increase after wake (data from rats (Vyazovskiy et al., 2008)). B, B′, electrophysiological analysis of cortical evoked responses using electrical stimulation (in rats; from (Vyazovskiy et al., 2008)) and TMS (in humans, from (Huber et al., 2012)) shows increased slope after wake and decreased slope after sleep. In B, W0 and W1 indicate onset and end of ∼ 4h of wake; S0 and S1 indicate onset and end of ∼ 4h of sleep, including at least 2h of NREM sleep. In B′, pink and blue bars indicate a night of sleep deprivation and a night of recovery sleep, respectively. B″, in vitro analysis of mEPSCs in rats and mice shows increased frequency and amplitude of mEPSCs after wake and sleep deprivation (SD) relative to sleep (control). Data from rats (Liu et al., 2010). C, in flies, the number of spines and dendritic branches in the visual neuron VS1 increase after enriched wake (ew) and decrease only if flies are allowed to sleep (from (Bushey et al., 2011). C′, structural studies in adolescent mice show a net increase in cortical spine density after wake and sleep deprivation (SD) and a net decrease after sleep (from (Maret et al., 2011).
Figure 4
Figure 4. SHY and slow wave activity (SWA)
A, SWA, a quantitative measure of the number and amplitude of slow waves (left), is high in NREM sleep and low in REM sleep and wake (middle). SWA increases with time spent awake and decreases during sleep, thus reflecting sleep pressure (right). B, in rats kept awake for 6 hours by exposure to novel objects, longer times spent exploring result in greater cortical induction of BDNF during wake, as well as in larger subsequent increases in SWA at sleep onset (from (Huber et al., 2007b). C, after bilateral lesions of the LC expression of plasticity-related genes during wake is low; during subsequent sleep, SWA is lower than in non-lesioned controls (from (Cirelli et al., 1996; Cirelli and Tononi, 2000). D, during wake, subjects learn to adapt to systematic rotations imposed on the perceived cursor trajectory, a task which activates right parietal areas (Ghilardi et al., 2000); during subsequent NREM sleep, SWA in the same areas shows a local increase, which correlates with post-sleep improvements in performance (from (Huber et al., 2004)). E, after a subject's arm is immobilized during the day, motor performance in a reaching task deteriorates, and the P45 cortical component of the response evoked by stimulation of the median nerve (SEP) decreases in contralateral sensorimotor cortex. In sleep post-immobilization, the same area shows a local decrease in SWA (from (Huber et al., 2006). F, Control loop for the homeostatic regulation of connection strength and firing rate/synchrony, based on the results of computer simulations of slow wave sleep (Olcese et al., 2010). Here connection strength (s) affects firing rates and synchrony (f) via activity mechanisms (A). During slow wave sleep, plasticity mechanisms (P) lead to a depression of synaptic strength (ds/dt) that is proportional to f. The resulting integrated value of connection strength (), in turn, determines the new value of firing rates and synchrony (f). As an example, strong average connection strength will lead to high firing rates and synchrony which, in turn, will strongly depress synapses, to bring the system back to baseline values of connection strength. Conversely, when connections are renormalized, activity levels will not be able to induce significant plastic changes and the system will reach a self-limiting equilibrium point.

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