Sleep-dependent synaptic down-selection (I): modeling the benefits of sleep on memory consolidation and integration

A systematic and meta-analytic review of the impact of sleep restriction on memory formation

Modern life causes a quarter of adults and half of teenagers to sleep for less than is recommended (Kocevska et al., 2021). Given well-documented benefits of sleep on memory, we must understand the cognitive costs of short sleep. We analysed 125 sleep restriction effect sizes from 39 reports involving 1234 participants. Restricting sleep (3-6.5 hours) compared to normal sleep (7-11 hours) negatively affects memory formation with a small effect size (Hedges' g = 0.29, 95 % CI = [0.13, 0.44]). We detected no evidence for publication bias. When sleep restriction effect sizes were compared with 185 sleep deprivation effect sizes (Newbury et al., 2021) no statistically significant difference was found, suggesting that missing some sleep has similar consequences for memory as not sleeping at all. When the analysis was restricted to post-encoding, rather than pre-encoding, sleep loss, sleep deprivation was associated with larger memory impairment than restriction. Our findings are best accounted for by the sequential hypothesis which emphasises complementary roles of slow-wave sleep and REM sleep for memory.

Factors influencing sleep-dependent consolidation: Sleep strengths memory based on encoding depth but not repetition

Sleep consolidates declarative memory after deep but not shallow incidental encoding, but little is known about this form of consolidation. One unexplored area is the extent to which the amount of exposure to incidentally encoded information affects consolidation processes. In two experiments, we manipulated the number of times information was presented. In Experiment 1, participants encoded words either one or three times in a deep or shallow incidental encoding task and completed a surprise recognition test after sleep or wake. Sleep consolidated information after deep encoding after one and three exposures, but not after shallow encoding. In Experiment 2, we explored the relationship between sleep architecture and memory after deep encoding. There was a trend for accuracy to be negatively related to N1 sleep, and reaction time to be negatively related to slow-wave sleep for words encoded once; however, the correlations did not survive corrections for multiple comparisons. These results are discussed with respect to active and passive consolidation processes.

Slow-wave sleep drives sleep-dependent renormalization of synaptic AMPA receptor levels in the hypothalamus

According to the synaptic homeostasis hypothesis (SHY), sleep serves to renormalize synaptic connections that have been potentiated during the prior wake phase due to ongoing encoding of information. SHY focuses on glutamatergic synaptic strength and has been supported by numerous studies examining synaptic structure and function in neocortical and hippocampal networks. However, it is unknown whether synaptic down-regulation during sleep occurs in the hypothalamus, i.e., a pivotal center of homeostatic regulation of bodily functions including sleep itself. We show that sleep, in parallel with the synaptic down-regulation in neocortical networks, down-regulates the levels of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) in the hypothalamus of rats. Most robust decreases after sleep were observed at both sites for AMPARs containing the GluA1 subunit. Comparing the effects of selective rapid eye movement (REM) sleep and total sleep deprivation, we moreover provide experimental evidence that slow-wave sleep (SWS) is the driving force of the down-regulation of AMPARs in hypothalamus and neocortex, with no additional contributions of REM sleep or the circadian rhythm. SWS-dependent synaptic down-regulation was not linked to EEG slow-wave activity. However, spindle density during SWS predicted relatively increased GluA1 subunit levels in hypothalamic synapses, which is consistent with the role of spindles in the consolidation of memory. Our findings identify SWS as the main driver of the renormalization of synaptic strength during sleep and suggest that SWS-dependent synaptic renormalization is also implicated in homeostatic control processes in the hypothalamus.

Mechanisms of Neuronal Reactivation in Memory Consolidation: A Perspective from Pathological Conditions

Memory consolidation refers to a process by which labile newly formed memory traces are progressively strengthened into long term memories and become more resistant to interference. Recent work has revealed that spontaneous hippocampal activity during rest, commonly referred to as "offline" activity, plays a critical role in the process of memory consolidation. Hippocampal reactivation occurs during sharp-wave ripples (SWRs), which are events associated with highly synchronous neural firing in the hippocampus and modulation of neural activity in distributed brain regions. Memory consolidation occurs primarily through a coordinated communication between hippocampus and neocortex. Cortical slow oscillations drive the repeated reactivation of hippocampal memory representations together with SWRs and thalamo-cortical spindles, inducing long-lasting cellular and network modifications responsible for memory stabilization.In this review, we aim to comprehensively cover the field of "reactivation and memory consolidation" research by detailing the physiological mechanisms of neuronal reactivation and firing patterns during SWRs and providing a discussion of more recent key findings. Several mechanistic explanations of neuropsychiatric diseases propose that impaired neural replay may underlie some of the symptoms of the disorders. Abnormalities in neuronal reactivation are a common phenomenon and cause pathological impairment in several diseases, such as Alzheimer's disease (AD), epilepsy and schizophrenia. However, the specific pathological changes and mechanisms of reactivation in each disease are different. Recent work has also enlightened some of the underlying pathological mechanisms of neuronal reactivation in these diseases. In this review, we further describe how SWRs, ripples and slow oscillations are affected in Alzheimer's disease, epilepsy, and schizophrenia. We then compare the differences of neuronal reactivation and discuss how different reactivation abnormalities cause pathological changes in these diseases. Aberrant neural reactivation provides insights into disease pathogenesis and may even serve as biomarkers for early disease progression and treatment response.

Sleep loss diminishes hippocampal reactivation and replay

Memories benefit from sleep1, and the reactivation and replay of waking experiences during hippocampal sharp-wave ripples (SWRs) are considered to be crucial for this process2. However, little is known about how these patterns are impacted by sleep loss. Here we recorded CA1 neuronal activity over 12 h in rats across maze exploration, sleep and sleep deprivation, followed by recovery sleep. We found that SWRs showed sustained or higher rates during sleep deprivation but with lower power and higher frequency ripples. Pyramidal cells exhibited sustained firing during sleep deprivation and reduced firing during sleep, yet their firing rates were comparable during SWRs regardless of sleep state. Despite the robust firing and abundance of SWRs during sleep deprivation, we found that the reactivation and replay of neuronal firing patterns was diminished during these periods and, in some cases, completely abolished compared to ad libitum sleep. Reactivation partially rebounded after recovery sleep but failed to reach the levels found in natural sleep. These results delineate the adverse consequences of sleep loss on hippocampal function at the network level and reveal a dissociation between the many SWRs elicited during sleep deprivation and the few reactivations and replays that occur during these events.

Consciousness and sleep

Sleep is a universal, essential biological process. It is also an invaluable window on consciousness. It tells us that consciousness can be lost but also that it can be regained, in all its richness, when we are disconnected from the environment and unable to reflect. By considering the neurophysiological differences between dreaming and dreamless sleep, we can learn about the substrate of consciousness and understand why it vanishes. We also learn that the ongoing state of the substrate of consciousness determines the way each experience feels regardless of how it is triggered-endogenously or exogenously. Dreaming consciousness is also a window on sleep and its functions. Dreams tell us that the sleeping brain is remarkably lively, recombining intrinsic activation patterns from a vast repertoire, freed from the requirements of ongoing behavior and cognitive control.

Computational role of sleep in memory reorganization

Sleep is considered to play an essential role in memory reorganization. Despite its importance, classical theoretical models did not focus on some sleep characteristics. Here, we review recent theoretical approaches investigating their roles in learning and discuss the possibility that non-rapid eye movement (NREM) sleep selectively consolidates memory, and rapid eye movement (REM) sleep reorganizes the representations of memories. We first review the possibility that slow waves during NREM sleep contribute to memory selection by using sequential firing patterns and the existence of up and down states. Second, we discuss the role of dreaming during REM sleep in developing neuronal representations. We finally discuss how to develop these points further, emphasizing the connections to experimental neuroscience and machine learning.

Circadian rhythm sleep disorders and time-of-day-dependent memory deficiency in Presenilin1/2 conditional knockout mice with long noncoding RNA expression profiling changes

Alzheimer's disease (AD) patients exhibit sleep and circadian disturbances prior to the onset of cognitive decline, and these disruptions worsen with disease severity. However, the molecular mechanisms behind sleep and circadian disruptions in AD patients are poorly understood. In this study, we investigated sleep pattern and circadian rhythms in Presenilin-1/2 conditional knockout (DKO) mice. Assessment of EEG and EMG recordings showed that DKO mice displayed increased NREM sleep time but not REM sleep during the dark phase compared to WT mice at the age of two months; at the age of six months, the DKO mice showed increased wakefulness periods and decreased total time spent in both NREM and REM sleep. WT exhibited time-of-day dependent modulation of contextual and cued memory. Compared with WT mice, 4-month-old DKO mice exhibited the deficiency regardless trained and tested in the same light/night phase or not. Particularly interesting was that DKO showed circadian modulation deficiency when trained in the resting period but not in the active period. Long noncoding RNAs (lncRNAs) are typically defined as transcripts longer than 200 nucleotides, and they have rhythmic expression in mammals. To date no study has investigated rhythmic lncRNA expression in Alzheimer's disease. We applied RNA-seq technology to profile hippocampus expression of lncRNAs in DKO mice during the light (/resting) and dark (/active) phases and performed gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses of the cis lncRNA targets. Expression alteration of lncRNAs associated with immune response and metallodipeptidase activity may contribute to the circadian disruptions of DKO mice. Especially we identified some LncRNAs which expression change oppositely between day and light in DKO mice compared to WT mice and are worthy to be studied further. Our results exhibited the circadian rhythm sleep disorders and a noteworthy time-of-day-dependent memory deficiency in AD model mice and provide a useful resource for studying the expression and function of lncRNAs during circadian disruptions in Alzheimer's disease.

Sleep loss diminishes hippocampal reactivation and replay

Memories benefit from sleep, and sleep loss immediately following learning has a negative impact on subsequent memory storage. Several prominent hypotheses ascribe a central role to hippocampal sharp-wave ripples (SWRs), and the concurrent reactivation and replay of neuronal patterns from waking experience, in the offline memory consolidation process that occurs during sleep. However, little is known about how SWRs, reactivation, and replay are affected when animals are subjected to sleep deprivation. We performed long duration (~12 h), high-density silicon probe recordings from rat hippocampal CA1 neurons, in animals that were either sleeping or sleep deprived following exposure to a novel maze environment. We found that SWRs showed a sustained rate of activity during sleep deprivation, similar to or higher than in natural sleep, but with decreased amplitudes for the sharp-waves combined with higher frequencies for the ripples. Furthermore, while hippocampal pyramidal cells showed a log-normal distribution of firing rates during sleep, these distributions were negatively skewed with a higher mean firing rate in both pyramidal cells and interneurons during sleep deprivation. During SWRs, however, firing rates were remarkably similar between both groups. Despite the abundant quantity of SWRs and the robust firing activity during these events in both groups, we found that reactivation of neurons was either completely abolished or significantly diminished during sleep deprivation compared to sleep. Interestingly, reactivation partially rebounded upon recovery sleep, but failed to reach the levels characteristic of natural sleep. Similarly, the number of replays were significantly lower during sleep deprivation and recovery sleep compared to natural sleep. These results provide a network-level account for the negative impact of sleep loss on hippocampal function and demonstrate that sleep loss impacts memory storage by causing a dissociation between the amount of SWRs and the replays and reactivations that take place during these events.

Remembering and forgetting in sleep: Selective synaptic plasticity during sleep driven by scaling factors Homer1a and Arc

Sleep is a conserved and essential process that supports learning and memory. Synapses are a major target of sleep function and a locus of sleep need. Evidence in the literature suggests that the need for sleep has a cellular or microcircuit level basis, and that sleep need can accumulate within localized brain regions as a function of waking activity. Activation of sleep promoting kinases and accumulation of synaptic phosphorylation was recently shown to be part of the molecular basis for the localized sleep need. A prominent hypothesis in the field suggests that some benefits of sleep are mediated by a broad but selective weakening, or scaling-down, of synaptic strength during sleep in order to offset increased excitability from synaptic potentiation during wake. The literature also shows that synapses can be strengthened during sleep, raising the question of what molecular mechanisms may allow for selection of synaptic plasticity types during sleep. Here I describe mechanisms of action of the scaling factors Arc and Homer1a in selective plasticity and links with sleep need. Arc and Homer1a are induced in neurons in response to waking neuronal activity and accumulate with time spent awake. I suggest that during sleep, Arc and Homer1a drive broad weakening of synapses through homeostatic scaling-down, but in a manner that is sensitive to the plasticity history of individual synapses, based on patterned phosphorylation of synaptic proteins. Therefore, Arc and Homer1a may offer insights into the intricate links between a cellular basis of sleep need and memory consolidation during sleep.

Dual orexin antagonist normalized sleep homeostatic drive, enhanced GABAergic inhibition, and suppressed seizures after traumatic brain injury

STUDY OBJECTIVES

Traumatic brain injury (TBI) can result in posttraumatic epilepsy (PTE) and sleep disturbances. We hypothesized that treatment with sleep aids after TBI can ameliorate PTE.

METHODS

CD-1 mice underwent controlled cortical impact (CCI), sham injury, or no craniotomy. Sham and CCI groups underwent a monthlong daily treatment with sleep aids including a dual orexin antagonist (DORA-22) or THIP (gaboxadol) or a respective vehicle starting on the day of CCI. We performed continuous EEG (electroencephalography) recordings at week 1 and months 1, 2, and 3 for ~1 week each time. Seizure analysis occurred at all-time points and sleep analysis occurred in week 1 and month-1/2 in all groups. Subsets of CCI and sham groups were subjected to voltageclamp experiments in hippocampal slices to evaluate GABAergic synaptic inhibition.

RESULTS

DORA-22 treatment suppressed seizures in month 1-3 recordings. TBI reduced the amplitude and frequency of miniature inhibitory synaptic currents (mIPSCs) in dentate granule cells and these changes were rescued by DORA-22 treatment. Sleep analysis showed that DORA-22 increased nonrapid eye movement (NREM) sleep during lights-off whereas THIP increased REM sleep during lights-on in week 1. Both treatments displayed subtle changes in time spent in NREM or REM at month-1/2 as well. TBI not only increased normalized EEG delta power (NΔ) at week-1 and month-1 but also resulted in the loss of the homeostatic diurnal oscillation of NΔ, which was restored by DORA-22 but not THIP treatment.

CONCLUSIONS

Dual orexin antagonists may have a therapeutic potential in suppressing PTE potentially by enhancing GABAergic inhibition and impacting sleep homeostatic drive.

Multiple traces and altered signal-to-noise in systems consolidation: Evidence from the 7T fMRI Natural Scenes Dataset

How do the neural correlates of recognition change over time? We study natural scene image recognition spanning a year with 7-Tesla functional magnetic resonance imaging (fMRI) of the human brain. We find that the medial temporal lobe (MTL) contribution to recognition persists over 200 d, supporting multiple-trace theory and contradicting a trace transfer (from MTL to cortex) point of view. We then test the hypothesis that the signal-to-noise ratio of traces increases over time, presumably a consequence of synaptic “desaturation” in the weeks following learning. The fMRI trace signature associates with the rate of removal of competing traces and reflects a time-related enhancement of image-feature selectivity. We conclude that multiple MTL traces and improved signal-to-noise may underlie systems-level memory consolidation.

The brain mechanisms of memory consolidation remain elusive. Here, we examine blood-oxygen-level-dependent (BOLD) correlates of image recognition through the scope of multiple influential systems consolidation theories. We utilize the longitudinal Natural Scenes Dataset, a 7-Tesla functional magnetic resonance imaging human study in which ∼135,000 trials of image recognition were conducted over the span of a year among eight subjects. We find that early- and late-stage image recognition associates with both medial temporal lobe (MTL) and visual cortex when evaluating regional activations and a multivariate classifier. Supporting multiple-trace theory (MTT), parts of the MTL activation time course show remarkable fit to a 20-y-old MTT time-dynamical model predicting early trace intensity increases and slight subsequent interference (R² > 0.90). These findings contrast a simplistic, yet common, view that memory traces are transferred from MTL to cortex. Next, we test the hypothesis that the MTL trace signature of memory consolidation should also reflect synaptic “desaturation,” as evidenced by an increased signal-to-noise ratio. We find that the magnitude of relative BOLD enhancement among surviving memories is positively linked to the rate of removal (i.e., forgetting) of competing traces. Moreover, an image-feature and time interaction of MTL and visual cortex functional connectivity suggests that consolidation mechanisms improve the specificity of a distributed trace. These neurobiological effects do not replicate on a shorter timescale (within a session), implicating a prolonged, offline process. While recognition can potentially involve cognitive processes outside of memory retrieval (e.g., re-encoding), our work largely favors MTT and desaturation as perhaps complementary consolidative memory mechanisms.

Integration of sleep homeostasis and navigation in Drosophila

During sleep, the brain undergoes dynamic and structural changes. In Drosophila, such changes have been observed in the central complex, a brain area important for sleep control and navigation. The connectivity of the central complex raises the question about how navigation, and specifically the head direction system, can operate in the face of sleep related plasticity. To address this question, we develop a model that integrates sleep homeostasis and head direction. We show that by introducing plasticity, the head direction system can function in a stable way by balancing plasticity in connected circuits that encode sleep pressure. With increasing sleep pressure, the head direction system nevertheless becomes unstable and a sleep phase with a different plasticity mechanism is introduced to reset network connectivity. The proposed integration of sleep homeostasis and head direction circuits captures features of their neural dynamics observed in flies and mice.

The why and how of sleep-dependent synaptic down-selection

Sleep requires that we disconnect from the environment, losing the ability to promptly respond to stimuli. There must be at least one essential function that justifies why we take this risk every day, and that function must depend on the brain being offline. We have proposed that this function is to renormalize synaptic weights after learning has led to a net increase in synaptic strength in many brain circuits. Without this renormalization, synaptic activity would become energetically too expensive and saturation would prevent new learning. There is converging evidence from molecular, electrophysiological, and ultrastructural experiments showing a net increase in synaptic strength after the major wake phase, and a net decline after sleep. The evidence also suggests that sleep-dependent renormalization is a smart process of synaptic down-selection, comprehensive and yet specific, which could explain the many beneficial effects of sleep on cognition. Recently, a key molecular mechanism that allows broad synaptic weakening during sleep was identified. Other mechanisms still being investigated should eventually explain how sleep can weaken most synapses but afford protection to some, including those directly activated by learning. That synaptic down-selection takes place during sleep is by now established; why it should take place during sleep has a plausible explanation; how it happens is still work in progress.

Sleep and synaptic down-selection

The synaptic homeostasis hypothesis (SHY) proposes that sleep is an essential process needed by the brain to maintain the total amount of synaptic strength under control. SHY predicts that by the end of a waking day the synaptic connections of many neural circuits undergo a net increase in synaptic strength due to ongoing learning, which is mainly mediated by synaptic potentiation. Stronger synapses require more energy and supplies and are prone to saturation, creating the need for synaptic renormalization. Such renormalization should mainly occur during sleep, when the brain is disconnected from the environment and neural circuits can be broadly reactivated off-line to undergo a systematic but specific synaptic down-selection. In short, according to SHY sleep is the price to pay for waking plasticity, to avoid runaway potentiation, decreased signal-to-noise ratio, and impaired learning due to saturation. In this review, we briefly discuss the rationale of the hypothesis and recent supportive ultrastructural evidence obtained in our laboratory. We then examine recent studies by other groups showing the causal role of cortical slow waves and hippocampal sharp waves/ripples in sleep-dependent down-selection of neural activity and synaptic strength. Finally, we discuss some of the molecular mechanisms that could mediate synaptic weakening during sleep.

How rhythms of the sleeping brain tune memory and synaptic plasticity

Decades of neurobehavioral research has linked sleep-associated rhythms in various brain areas to improvements in cognitive performance. However, it remains unclear what synaptic changes might underlie sleep-dependent declarative memory consolidation and procedural task improvement, and why these same changes appear not to occur across a similar interval of wake. Here we describe recent research on how one specific feature of sleep-network rhythms characteristic of rapid eye movement and non-rapid eye movement-could drive synaptic strengthening or weakening in specific brain circuits. We provide an overview of how these rhythms could affect synaptic plasticity individually and in concert. We also present an overarching hypothesis for how all network rhythms occurring across the sleeping brain could aid in encoding new information in neural circuits.

Sleep and the extraction of hidden regularities: A systematic review and the importance of temporal rules

As part of its role in memory consolidation, sleep has been repeatedly identified as critical for the extraction of regularities from wake experiences. However, many null results have been published as well, with no clear consensus emerging regarding the conditions that yield this sleep effect. Here, we systematically review the role of sleep in the extraction of hidden regularities, specifically those involving associative relations embedded in newly learned information. We found that the specific behavioral task used in a study had far more impact on whether a sleep effect was discovered than either the category of the cognitive processes targeted, or the particular experimental design employed. One emerging pattern, however, was that the explicit detection of hidden rules is more likely to happen when the rules are of a temporal nature (i.e., event A at time t predicts a later event B) than when they are non-temporal. We discuss this temporal rule sensitivity in reference to the compressed memory replay occurring in the hippocampus during slow-wave-sleep, and compare this effect to what happens when the extraction of regularities depends on prior knowledge and relies on structures other than the hippocampus.

Principles underlying the input-dependent formation and organization of memories

The neuronal system exhibits the remarkable ability to dynamically store and organize incoming information into a web of memory representations (items), which is essential for the generation of complex behaviors. Central to memory function is that such memory items must be (1) discriminated from each other, (2) associated to each other, or (3) brought into a sequential order. However, how these three basic mechanisms are robustly implemented in an input-dependent manner by the underlying complex neuronal and synaptic dynamics is still unknown. Here, we develop a mathematical framework, which provides a direct link between different synaptic mechanisms, determining the neuronal and synaptic dynamics of the network, to create a network that emulates the above mechanisms. Combining correlation-based synaptic plasticity and homeostatic synaptic scaling, we demonstrate that these mechanisms enable the reliable formation of sequences and associations between two memory items still missing the capability for discrimination. We show that this shortcoming can be removed by additionally considering inhibitory synaptic plasticity. Thus, the here-presented framework provides a new, functionally motivated link between different known synaptic mechanisms leading to the self-organization of fundamental memory mechanisms.

Sleep Modelling across Physiological Levels

Sleep and circadian rhythms are regulated across multiple functional, spatial and temporal levels: from genes to networks of coupled neurons and glial cells, to large scale brain dynamics and behaviour. The dynamics at each of these levels are complex and the interaction between the levels is even more so, so research have mostly focused on interactions within the levels to understand the underlying mechanisms—the so-called reductionist approach. Mathematical models were developed to test theories of sleep regulation and guide new experiments at each of these levels and have become an integral part of the field. The advantage of modelling, however, is that it allows us to simulate and test the dynamics of complex biological systems and thus provides a tool to investigate the connections between the different levels and study the system as a whole. In this paper I review key models of sleep developed at different physiological levels and discuss the potential for an integrated systems biology approach for sleep regulation across these levels. I also highlight the necessity of building mechanistic connections between models of sleep and circadian rhythms across these levels.

Abnormal Sleep Spindles, Memory Consolidation, and Schizophrenia

There is overwhelming evidence that sleep is crucial for memory consolidation. Patients with schizophrenia and their unaffected relatives have a specific deficit in sleep spindles, a defining oscillation of non-rapid eye movement (NREM) Stage 2 sleep that, in coordination with other NREM oscillations, mediate memory consolidation. In schizophrenia, the spindle deficit correlates with impaired sleep-dependent memory consolidation, positive symptoms, and abnormal thalamocortical connectivity. These relations point to dysfunction of the thalamic reticular nucleus (TRN), which generates spindles, gates the relay of sensory information to the cortex, and modulates thalamocortical communication. Genetic studies are beginning to provide clues to possible neurodevelopmental origins of TRN-mediated thalamocortical circuit dysfunction and to identify novel targets for treating the related memory deficits and symptoms. By forging empirical links in causal chains from risk genes to thalamocortical circuit dysfunction, spindle deficits, memory impairment, symptoms, and diagnosis, future research can advance our mechanistic understanding, treatment, and prevention of schizophrenia.

The Function(s) of Sleep

Sleep is a highly conserved phenomenon in endotherms, and therefore it must serve at least one basic function across this wide range of species. What that function is remains one of the biggest mysteries in neurobiology. By using the word neurobiology, we do not mean to exclude possible non-neural functions of sleep, but it is difficult to imagine why the brain must be taken offline if the basic function of sleep did not involve the nervous system. In this chapter we discuss several current hypotheses about sleep function. We divide these hypotheses into two categories: ones that propose higher-order cognitive functions and ones that focus on housekeeping or restorative processes. We also pose four aspects of sleep that any successful functional hypothesis has to account for: why do the properties of sleep change across the life span? Why and how is sleep homeostatically regulated? Why must the brain be taken offline to accomplish the proposed function? And, why are there two radically different stages of sleep?The higher-order cognitive function hypotheses we discuss are essential mechanisms of learning and memory and synaptic plasticity. These are not mutually exclusive hypotheses. Each focuses on specific mechanistic aspects of sleep, and higher-order cognitive processes are likely to involve components of all of these mechanisms. The restorative hypotheses are maintenance of brain energy metabolism, macromolecular biosynthesis, and removal of metabolic waste. Although these three hypotheses seem more different than those related to higher cognitive function, they may each contribute important components to a basic sleep function. Any sleep function will involve specific gene expression and macromolecular biosynthesis, and as we explain there may be important connections between brain energy metabolism and the need to remove metabolic wastes.A deeper understanding of sleep functions in endotherms will enable us to answer whether or not rest behaviors in species other than endotherms are homologous with mammalian and avian sleep. Currently comparisons across the animal kingdom depend on superficial and phenomenological features of rest states and sleep, but investigations of sleep functions would provide more insight into the evolutionary relationships between EEG-defined sleep in endotherms and rest states in ectotherms.

Computational models of memory consolidation and long-term synaptic plasticity during sleep

The brain stores memories by persistently changing the connectivity between neurons. Sleep is known to be critical for these changes to endure. Research on the neurobiology of sleep and the mechanisms of long-term synaptic plasticity has provided data in support of various theories of how brain activity during sleep affects long-term synaptic plasticity. The experimental findings - and therefore the theories - are apparently quite contradictory, with some evidence pointing to a role of sleep in the forgetting of irrelevant memories, whereas other results indicate that sleep supports the reinforcement of the most valuable recollections. A unified theoretical framework is in need. Computational modeling and simulation provide grounds for the quantitative testing and comparison of theoretical predictions and observed data, and might serve as a strategy to organize the rather complicated and diverse pool of data and methodologies used in sleep research. This review article outlines the emerging progress in the computational modeling and simulation of the main theories on the role of sleep in memory consolidation.

Memory corticalization triggered by REM sleep: mechanisms of cellular and systems consolidation

Once viewed as a passive physiological state, sleep is a heterogeneous and complex sequence of brain states with essential effects on synaptic plasticity and neuronal functioning. Rapid-eye-movement (REM) sleep has been shown to promote calcium-dependent plasticity in principal neurons of the cerebral cortex, both during memory consolidation in adults and during post-natal development. This article reviews the plasticity mechanisms triggered by REM sleep, with a focus on the emerging role of kinases and immediate-early genes for the progressive corticalization of hippocampus-dependent memories. The body of evidence suggests that memory corticalization triggered by REM sleep is a systemic phenomenon with cellular and molecular causes.

Dreaming of a learning task is associated with enhanced memory consolidation: Replication in an overnight sleep study

Sleep following learning benefits memory. One model attributes this effect to the iterative "reactivation" of memory traces in the sleeping brain, demonstrated in animal models. Although technical limitations prohibit using the same methods to observe memory reactivation in the human brain, the study of mental activity during sleep provides an alternative method of observing memory activation during sleep. In fact, the content of dream experience may reflect the process of memory reactivation and consolidation in the sleeping brain. In line with this hypothesis, we previously reported that dreaming about a spatial learning task during a nap strongly predicts subsequent performance improvements. Here, we replicate this observation in an overnight sleep study, for the first time demonstrating that pre-sleep training on a virtual maze navigation task is reflected in dreams reported from all phases of sleep, with unambiguous representation of the task in dream content associated with improved next-morning performance. These observations are consistent with reactivation-based models of memory consolidation in sleep, confirming our earlier finding that the cognitive-level activation of recent experience during sleep is associated with subsequent performance gains.

The effect of cathodal transcranial direct current stimulation during rapid eye-movement sleep on neutral and emotional memory

Sleep-dependent memory consolidation has been extensively studied. Neutral declarative memories and serial reaction time task (SRTT) performance can benefit from slow-wave activity, characterized by less than 1 Hz frequency cortical slow oscillations (SO). Emotional memories can benefit from theta activity, characterized by 4-8 Hz frequency cortical oscillations. Applying transcranial direct current stimulation (tDCS) during sleep entrains specific frequencies to alter sleep architecture. When applying cathodal tDCS (CtDCS), neural inhibition or excitation may depend on the waveform at the applied frequency. A double dissociation was predicted, with CtDCS at SO frequency improving neutral declarative memory and SRTT performance, and theta frequency CtDCS inhibiting negative emotional memory. Participants completed three CtDCS conditions (Theta: 5 Hz, SO: 0.75 Hz and control: sham) and completed an SRTT and word recognition task pre- and post-sleep, comprising emotional and neutral words to assess memory. In line with predictions, CtDCS improved neutral declarative memory when applied at SO frequency. When applied at theta frequency, no negative emotional word memory impairment was found but a positive association was found between post-stimulation theta power and emotional word recognition. SRTT performance was also not altered by either CtDCS frequency. Future studies should investigate overnight theta CtDCS and examine the effects of CtDCS during and after stimulation.

Activity-Dependent Downscaling of Subthreshold Synaptic Inputs during Slow-Wave-Sleep-like Activity In Vivo

Activity-dependent synaptic plasticity is critical for cortical circuit refinement. The synaptic homeostasis hypothesis suggests that synaptic connections are strengthened during wake and downscaled during sleep; however, it is not obvious how the same plasticity rules could explain both outcomes. Using whole-cell recordings and optogenetic stimulation of presynaptic input in urethane-anesthetized mice, which exhibit slow-wave-sleep (SWS)-like activity, we show that synaptic plasticity rules are gated by cortical dynamics in vivo. While Down states support conventional spike timing-dependent plasticity, Up states are biased toward depression such that presynaptic stimulation alone leads to synaptic depression, while connections contributing to postsynaptic spiking are protected against this synaptic weakening. We find that this novel activity-dependent and input-specific downscaling mechanism has two important computational advantages: (1) improved signal-to-noise ratio, and (2) preservation of previously stored information. Thus, these synaptic plasticity rules provide an attractive mechanism for SWS-related synaptic downscaling and circuit refinement.

Sleep increases explicit solutions and reduces intuitive judgments of semantic coherence

Sleep fosters the generation of explicit knowledge. Whether sleep also benefits implicit intuitive decisions about underlying patterns is unclear. We examined sleep's role in explicit and intuitive semantic coherence judgments. Participants encoded sets of three words and after a sleep or wake period were required to judge the potential convergence of these words on a common fourth associate. Compared with wakefulness, sleep increased the number of explicitly named common associates and decreased the number of intuitive judgments. This suggests that sleep enhances the extraction of explicit knowledge at the expense of the ability to make intuitive decisions about semantic coherence.

Plasticity during Sleep Is Linked to Specific Regulation of Cortical Circuit Activity

Sleep is thought to be involved in the regulation of synaptic plasticity in two ways: by enhancing local plastic processes underlying the consolidation of specific memories and by supporting global synaptic homeostasis. Here, we briefly summarize recent structural and functional studies examining sleep-associated changes in synaptic morphology and neural excitability. These studies point to a global down-scaling of synaptic strength across sleep while a subset of synapses increases in strength. Similarly, neuronal excitability on average decreases across sleep, whereas subsets of neurons increase firing rates across sleep. Whether synapse formation and excitability is down or upregulated across sleep appears to partly depend on the cell's activity level during wakefulness. Processes of memory-specific upregulation of synapse formation and excitability are observed during slow wave sleep (SWS), whereas global downregulation resulting in elimination of synapses and decreased neural firing is linked to rapid eye movement sleep (REM sleep). Studies of the excitation/inhibition balance in cortical circuits suggest that both processes are connected to a specific inhibitory regulation of cortical principal neurons, characterized by an enhanced perisomatic inhibition via parvalbumin positive (PV+) cells, together with a release from dendritic inhibition by somatostatin positive (SOM+) cells. Such shift towards increased perisomatic inhibition of principal cells appears to be a general motif which underlies the plastic synaptic changes observed during sleep, regardless of whether towards up or downregulation.

Linking Network Activity to Synaptic Plasticity during Sleep: Hypotheses and Recent Data

Research findings over the past two decades have supported a link between sleep states and synaptic plasticity. Numerous mechanistic hypotheses have been put forth to explain this relationship. For example, multiple studies have shown structural alterations to synapses (including changes in synaptic volume, spine density, and receptor composition) indicative of synaptic weakening after a period of sleep. Direct measures of neuronal activity and synaptic strength support the idea that a period of sleep can reduce synaptic strength. This has led to the synaptic homeostasis hypothesis (SHY), which asserts that during slow wave sleep, synapses are downscaled throughout the brain to counteract net strengthening of network synapses during waking experience (e.g., during learning). However, neither the cellular mechanisms mediating these synaptic changes, nor the sleep-dependent activity changes driving those cellular events are well-defined. Here we discuss potential cellular and network dynamic mechanisms which could underlie reductions in synaptic strength during sleep. We also discuss recent findings demonstrating circuit-specific synaptic strengthening (rather than weakening) during sleep. Based on these data, we explore the hypothetical role of sleep-associated network activity patterns in driving synaptic strengthening. We propose an alternative to SHY—namely that depending on experience during prior wake, a variety of plasticity mechanisms may operate in the brain during sleep. We conclude that either synaptic strengthening or synaptic weakening can occur across sleep, depending on changes to specific neural circuits (such as gene expression and protein translation) induced by experiences in wake. Clarifying the mechanisms underlying these different forms of sleep-dependent plasticity will significantly advance our understanding of how sleep benefits various cognitive functions.

Formation and suppression of acoustic memories during human sleep

Sleep and memory are deeply related, but the nature of the neuroplastic processes induced by sleep remains unclear. Here, we report that memory traces can be both formed or suppressed during sleep, depending on sleep phase. We played samples of acoustic noise to sleeping human listeners. Repeated exposure to a novel noise during Rapid Eye Movements (REM) or light non-REM (NREM) sleep leads to improvements in behavioral performance upon awakening. Strikingly, the same exposure during deep NREM sleep leads to impaired performance upon awakening. Electroencephalographic markers of learning extracted during sleep confirm a dissociation between sleep facilitating memory formation (light NREM and REM sleep) and sleep suppressing learning (deep NREM sleep). We can trace these neural changes back to transient sleep events, such as spindles for memory facilitation and slow waves for suppression. Thus, highly selective memory processes are active during human sleep, with intertwined episodes of facilitative and suppressive plasticity.Though memory and sleep are related, it is still unclear whether new memories can be formed during sleep. Here, authors show that people could learn new sounds during REM or light non-REM sleep, but that learning was suppressed when sounds were played during deep NREM sleep.

Dorsoventral and Proximodistal Hippocampal Processing Account for the Influences of Sleep and Context on Memory (Re)consolidation: A Connectionist Model

The context in which learning occurs is sufficient to reconsolidate stored memories and neuronal reactivation may be crucial to memory consolidation during sleep. The mechanisms of context-dependent and sleep-dependent memory (re)consolidation are unknown but involve the hippocampus. We simulated memory (re)consolidation using a connectionist model of the hippocampus that explicitly accounted for its dorsoventral organization and for CA1 proximodistal processing. Replicating human and rodent (re)consolidation studies yielded the following results. (1) Semantic overlap between memory items and extraneous learning was necessary to explain experimental data and depended crucially on the recurrent networks of dorsal but not ventral CA3. (2) Stimulus-free, sleep-induced internal reactivations of memory patterns produced heterogeneous recruitment of memory items and protected memories from subsequent interference. These simulations further suggested that the decrease in memory resilience when subjects were not allowed to sleep following learning was primarily due to extraneous learning. (3) Partial exposure to the learning context during simulated sleep (i.e., targeted memory reactivation) uniformly increased memory item reactivation and enhanced subsequent recall. Altogether, these results show that the dorsoventral and proximodistal organization of the hippocampus may be important components of the neural mechanisms for context-based and sleep-based memory (re)consolidations.

Neural reactivations during sleep determine network credit assignment

A fundamental goal of motor learning is to establish the neural patterns that produce a desired behavioral outcome. It remains unclear how and when the nervous system solves this 'credit assignment' problem. Using neuroprosthetic learning, in which we could control the causal relationship between neurons and behavior, we found that sleep-dependent processing was required for credit assignment and the establishment of task-related functional connectivity reflecting the casual neuron-behavior relationship. Notably, we observed a strong link between the microstructure of sleep reactivations and credit assignment, with downscaling of non-causal activity. Decoupling of spiking to slow oscillations using optogenetic methods eliminated rescaling. Thus, our results suggest that coordinated firing during sleep is essential for establishing sparse activation patterns that reflect the causal neuron-behavior relationship.

The Sleeping Brain

The role of sleep has long baffled scientists, but the latest research is providing new indicators about what it does for both the brain and body. While scientists believe that sleep re-energizes the body's cells, clears waste from the brain, and supports learning and memory, much still needs to be learned about the part it plays in regulating mood, appetite and libido. Source/Shutterstock.

The Sleeping Cerebellum

We sleep almost one-third of our lives and sleep plays an important role in critical brain functions like memory formation and consolidation. The role of sleep in cerebellar processing, however, constitutes an enigma in the field of neuroscience; we know little about cerebellar sleep-physiology, cerebro-cerebellar interactions during sleep, or the contributions of sleep to cerebellum-dependent memory consolidation. Likewise, we do not understand why cerebellar malfunction can lead to changes in the sleep-wake cycle and sleep disorders. In this review, we evaluate how sleep and cerebellar processing may influence one another and highlight which scientific routes and technical approaches could be taken to uncover the mechanisms underlying these interactions.

Cycle-Triggered Cortical Stimulation during Slow Wave Sleep Facilitates Learning a BMI Task: A Case Report in a Non-Human Primate

Slow wave sleep (SWS) has been identified as the sleep stage involved in consolidating newly acquired information. A growing body of evidence has shown that delta (1-4 Hz) oscillatory activity, the characteristic electroencephalographic signature of SWS, is involved in coordinating interaction between the hippocampus and the neocortex and is thought to take a role in stabilizing memory traces related to a novel task. This case report describes a new protocol that uses neuroprosthetics training of a non-human primate to evaluate the effects of surface cortical electrical stimulation triggered from SWS cycles. The results suggest that stimulation phase-locked to SWS oscillatory activity promoted learning of the neuroprosthetic task. This protocol could be used to elucidate mechanisms of synaptic plasticity underlying off-line learning during sleep and offers new insights into the role of brain oscillations in information processing and memory consolidation.

Sleep restriction can attenuate prioritization benefits on declarative memory consolidation

As chronic sleep restriction is a widespread problem among adolescents, the present study investigated the effects of a 1-week sleep restriction (SR) versus control period on the consolidation of long-term memory for prose passages. We also determined whether the benefit of prioritization on memory is modulated by adequate sleep occurring during consolidation. Fifty-six healthy adolescents (25 male, aged 15-19 years) were instructed to remember a prose passage in which half of the content was highlighted (prioritized), and were told that they would receive an additional bonus for remembering highlighted content. Following an initial free recall test, participants underwent a 7-night period in which they received either a 5-h (SR) or 9-h (control) nightly sleep opportunity, monitored by polysomnography on selected nights. Free recall of the passage was tested at the end of the sleep manipulation period (1 week after encoding), and again 6 weeks after encoding. Recall of highlighted content was superior to that of non-highlighted content at all three time-points (initial, 1 week, 6 weeks). This beneficial effect of prioritization on memory was stronger 1 week relative to a few minutes after encoding for the control, but not the SR group. N3 duration was similar in the control and SR groups. Overall, the present study shows that the benefits of prioritization on memory are enhanced over time, requiring time and sleep to unfold fully. Partial sleep deprivation (i.e. 5-h nocturnal sleep opportunity) may attenuate such benefits, but this may be offset by preservation of N3 sleep duration.

Regulation of Hippocampal Firing by Network Oscillations during Sleep

It has been hypothesized that waking leads to higher-firing neurons, with increased energy expenditure, and that sleep serves to return activity to baseline levels. Oscillatory activity patterns during different stages of sleep may play specific roles in this process, but consensus has been missing. To evaluate these phenomena in the hippocampus, we recorded from region CA1 neurons in rats across the 24-hr cycle, and we found that their firing increased upon waking and decreased 11% per hour across sleep. Waking and sleeping also affected lower- and higher-firing neurons differently. Interestingly, the incidences of sleep spindles and sharp-wave ripples (SWRs), typically associated with cortical plasticity, were predictive of ensuing firing changes and were more robustly predictive than other oscillatory events. Spindles and SWRs were initiated during non-REM sleep, yet the changes were incorporated in the network over the following REM sleep epoch. These findings indicate an important role for spindles and SWRs and provide novel evidence of a symbiotic relationship between non-REM and REM stages of sleep in the homeostatic regulation of neuronal activity.

Synaptic refinement during development and its effect on slow-wave activity: a computational study

Recent evidence suggests that synaptic refinement, the reorganization of synapses and connections without significant change in their number or strength, is important for the development of the visual system of juvenile rodents. Other evidence in rodents and humans shows that there is a marked drop in sleep slow-wave activity (SWA) during adolescence. Slow waves reflect synchronous transitions of neuronal populations between active and inactive states, and the amount of SWA is influenced by the connection strength and organization of cortical neurons. In this study, we investigated whether synaptic refinement could account for the observed developmental drop in SWA. To this end, we employed a large-scale neural model of primary visual cortex and sections of the thalamus, capable of producing realistic slow waves. In this model, we reorganized intralaminar connections according to experimental data on synaptic refinement: during prerefinement, local connections between neurons were homogenous, whereas in postrefinement, neurons connected preferentially to neurons with similar receptive fields and preferred orientations. Synaptic refinement led to a drop in SWA and to changes in slow-wave morphology, consistent with experimental data. To test whether learning can induce synaptic refinement, intralaminar connections were equipped with spike timing-dependent plasticity. Oriented stimuli were presented during a learning period, followed by homeostatic synaptic renormalization. This led to activity-dependent refinement accompanied again by a decline in SWA. Together, these modeling results show that synaptic refinement can account for developmental changes in SWA. Thus sleep SWA may be used to track noninvasively the reorganization of cortical connections during development.

Sleep-Dependent Potentiation in the Visual System Is at Odds with the Synaptic Homeostasis Hypothesis

STUDY OBJECTIVES

Two commentaries recently published in SLEEP came to very different conclusions regarding how data from a mouse model of sleep-dependent neural plasticity (orientation-specific response potentiation; OSRP) fit with the synaptic homeostasis hypothesis (SHY). To assess whether SHY offers an explanatory mechanism for OSRP, we present new data on how cortical neuron firing rates are modulated as a function of novel sensory experience and subsequent sleep in this model system.

METHODS

We carried out longitudinal extracellular recordings of single-neuron activity in the primary visual cortex across a period of novel visual experience and subsequent sleep or sleep deprivation. Spontaneous neuronal firing rates and visual responses were recorded from the same population of visual cortex neurons before control (blank screen) or novel (oriented grating) stimulus presentation, immediately after stimulus presentation, and after a period of subsequent ad lib sleep or sleep deprivation.

RESULTS

Firing rate responses to visual stimuli were unchanged across waking experience, regardless of whether a blank screen or an oriented grating stimulus was presented. Firing rate responses to stimuli of the presented stimulus orientation were selectively enhanced across post-stimulus sleep, but these changes were blocked by sleep deprivation. Neuronal firing increased significantly across bouts of post-stimulus rapid eye movement (REM) sleep and slow wave sleep (SWS), but not across bouts of wake.

CONCLUSIONS

The current data suggest that following novel visual experience, potentiation of a subset of V1 synapses occurs across periods of sleep. This finding cannot be explained parsimoniously by SHY.

Auditory feedback blocks memory benefits of cueing during sleep

It is now widely accepted that re-exposure to memory cues during sleep reactivates memories and can improve later recall. However, the underlying mechanisms are still unknown. As reactivation during wakefulness renders memories sensitive to updating, it remains an intriguing question whether reactivated memories during sleep also become susceptible to incorporating further information after the cue. Here we show that the memory benefits of cueing Dutch vocabulary during sleep are in fact completely blocked when memory cues are directly followed by either correct or conflicting auditory feedback, or a pure tone. In addition, immediate (but not delayed) auditory stimulation abolishes the characteristic increases in oscillatory theta and spindle activity typically associated with successful reactivation during sleep as revealed by high-density electroencephalography. We conclude that plastic processes associated with theta and spindle oscillations occurring during a sensitive period immediately after the cue are necessary for stabilizing reactivated memory traces during sleep.

Exposure to memory cues during sleep improves subsequent memory recall. Here the authors demonstrate that presenting an additional auditory stimulus during a critical time window following the memory cue abolishes the memory benefit of cueing and its oscillatory correlates during sleep in humans.

Synaptic Homeostasis and Restructuring across the Sleep-Wake Cycle

Sleep is critical for hippocampus-dependent memory consolidation. However, the underlying mechanisms of synaptic plasticity are poorly understood. The central controversy is on whether long-term potentiation (LTP) takes a role during sleep and which would be its specific effect on memory. To address this question, we used immunohistochemistry to measure phosphorylation of Ca2+/calmodulin-dependent protein kinase II (pCaMKIIα) in the rat hippocampus immediately after specific sleep-wake states were interrupted. Control animals not exposed to novel objects during waking (WK) showed stable pCaMKIIα levels across the sleep-wake cycle, but animals exposed to novel objects showed a decrease during subsequent slow-wave sleep (SWS) followed by a rebound during rapid-eye-movement sleep (REM). The levels of pCaMKIIα during REM were proportional to cortical spindles near SWS/REM transitions. Based on these results, we modeled sleep-dependent LTP on a network of fully connected excitatory neurons fed with spikes recorded from the rat hippocampus across WK, SWS and REM. Sleep without LTP orderly rescaled synaptic weights to a narrow range of intermediate values. In contrast, LTP triggered near the SWS/REM transition led to marked swaps in synaptic weight ranking. To better understand the interaction between rescaling and restructuring during sleep, we implemented synaptic homeostasis and embossing in a detailed hippocampal-cortical model with both excitatory and inhibitory neurons. Synaptic homeostasis was implemented by weakening potentiation and strengthening depression, while synaptic embossing was simulated by evoking LTP on selected synapses. We observed that synaptic homeostasis facilitates controlled synaptic restructuring. The results imply a mechanism for a cognitive synergy between SWS and REM, and suggest that LTP at the SWS/REM transition critically influences the effect of sleep: Its lack determines synaptic homeostasis, its presence causes synaptic restructuring.

Sleep slow-wave activity reveals developmental changes in experience-dependent plasticity

Experience-dependent plasticity, the ability of the brain to constantly adapt to an ever-changing environment, has been suggested to be highest during childhood and to decline thereafter. However, empirical evidence for this is rather scarce. Slow-wave activity (SWA; EEG activity of 1-4.5 Hz) during deep sleep can be used as a marker of experience-dependent plasticity. For example, performing a visuomotor adaptation task in adults increased SWA during subsequent sleep over a locally restricted region of the right parietal cortex, which is known to be involved in visuomotor adaptation. Here, we investigated whether local experience-dependent changes in SWA vary as a function of brain maturation. Three age groups (children, adolescents, and adults) participated in a high-density EEG study with two conditions (baseline and adaptation) of a visuomotor learning task. Compared with the baseline condition, sleep SWA was increased after visuomotor adaptation in a cluster of eight electrodes over the right parietal cortex. The local boost in SWA was highest in children. Baseline SWA in the parietal cluster and right parietal gray matter volume, which both indicate region-specific maturation, were significantly correlated with the local increase in SWA. Our findings indicate that processes of brain maturation favor experience-dependent plasticity and determine how sensitive a specific brain region is for learning experiences. Moreover, our data confirm that SWA is a highly sensitive tool to map maturational differences in experience-dependent plasticity.

Negative reinforcement impairs overnight memory consolidation

Post-learning sleep is beneficial for human memory. However, it may be that not all memories benefit equally from sleep. Here, we manipulated a spatial learning task using monetary reward and performance feedback, asking whether enhancing the salience of the task would augment overnight memory consolidation and alter its incorporation into dreaming. Contrary to our hypothesis, we found that the addition of reward impaired overnight consolidation of spatial memory. Our findings seemingly contradict prior reports that enhancing the reward value of learned information augments sleep-dependent memory processing. Given that the reward followed a negative reinforcement paradigm, consolidation may have been impaired via a stress-related mechanism.