REM sleep rescues learning from interference

REM refines and rescues memory representations: a new theory

Despite extensive evidence on the roles of nonrapid eye movement (NREM) and REM sleep in memory processing, a comprehensive model that integrates their complementary functions remains elusive due to a lack of mechanistic understanding of REM's role in offline memory processing. We present the REM Refining and Rescuing (RnR) Hypothesis, which posits that the principal function of REM sleep is to increase the signal-to-noise ratio within and across memory representations. As such, REM sleep selectively enhances essential nodes within a memory representation while inhibiting the majority (Refine). Additionally, REM sleep modulates weak and strong memory representations so they fall within a similar range of recallability (Rescue). Across multiple NREM-REM cycles, tuning functions of individual memory traces get sharpened, allowing for integration of shared features across representations. We hypothesize that REM sleep's unique cellular, neuromodulatory, and electrophysiological milieu, marked by greater inhibition and a mixed autonomic state of both sympathetic and parasympathetic activity, underpins these processes. The RnR Hypothesis offers a unified framework that explains diverse behavioral and neural outcomes associated with REM sleep, paving the way for future research and a more comprehensive model of sleep-dependent cognitive functions.

Neither fifty percent slow-wave sleep suppression nor fifty percent rapid eye movement sleep suppression does impair memory consolidation

Establishing well-defined relationships between sleep features and memory consolidation is essential in comprehending the pathophysiology of cognitive decline commonly seen in patients with insomnia, depression, and other sleep-disrupting conditions. Twenty-eight volunteers participated in two experimental sessions: a session with selective SWS suppression during one night and a session with undisturbed night sleep (as a control condition). Fifteen of them also participated in a third session with REM suppression. Suppression was achieved by presenting an acoustic tone. In the evening and the morning, the participants completed procedural and declarative memory tasks and the Psychomotor vigilance task (PVT). Heart rate variability analysis and salivary cortisol were used to control possible stress reactions to sleep interference. SWS and REM suppression led to more than 50 percent reduction in the amount of these stages. Neither vigilance nor memory consolidation was impaired after SWS or REM suppression. Unexpectedly, a beneficial effect of selective SWS suppression on PVT performance was found. Similarly, after a night with SWS suppression, the overnight improvement in procedural skills was higher than after a night with REM suppression and after a night with undisturbed sleep. Our data brings into question the extent to which SWS and REM are truly necessary for effective memory consolidation to proceed. Moreover, SWS suppression may even improve the performance of some tasks, possibly by reducing sleep inertia associated with undisturbed sleep.

The role of REM sleep in neural differentiation of memories in the hippocampus

When faced with a familiar situation, we can use memory to make predictions about what will happen next. If such predictions turn out to be erroneous, the brain can adapt by differentiating the representations of the cues that generated the prediction from the mispredicted item itself, reducing the likelihood of future prediction errors. Prior work by Kim et al. (2017) found that violating a sequential association in a statistical learning paradigm triggered differentiation of the neural representations of the associated items in the hippocampus. Here, we used fMRI to test the preregistered hypothesis that this hippocampal differentiation occurs only when violations are followed by rapid eye movement (REM) sleep. In the morning, participants first learned that some items predict others (e.g., A predicts B) then encountered a violation in which a predicted item (B) failed to appear when expected after its associated item (A); the predicted item later appeared on its own after an unrelated item. Participants were then randomly assigned to one of three conditions: remain awake, take a nap containing non-REM sleep only, or take a nap with both non-REM and REM sleep. While the predicted results were not observed in the preregistered left CA2/3/DG ROI, we did observe evidence for our hypothesis in closely related hippocampal ROIs, uncorrected for multiple comparisons: In right CA2/3/DG, differentiation in the group with REM sleep was greater than in the groups without REM sleep (wake and non-REM nap); this differentiation was item-specific and concentrated in right DG. Differentiation effects were also greater in bilateral DG when the predicted item was more strongly reactivated during the violation. Overall, the results presented here provide initial evidence linking REM sleep to changes in the hippocampal representations of memories in humans.

Can stimulants make you smarter, despite stealing your sleep?

Nonmedical use of psychostimulants for cognitive enhancement is widespread and growing in neurotypical individuals, despite mixed scientific evidence of their effectiveness. Sleep benefits cognition, yet the interaction between stimulants, sleep, and cognition in neurotypical adults has received little attention. We propose that one effect of psychostimulants, namely decreased sleep, may play an important and unconsidered role in the effect of stimulants on cognition. We discuss the role of sleep in cognition, the alerting effects of stimulants in the context of sleep loss, and the conflicting findings of stimulants for complex cognitive processes. Finally, we hypothesize that sleep may be one unconsidered factor in the mythology of stimulants as cognitive enhancers and propose a methodological approach to systematically assess this relation.

Memory Reactivation during Sleep Does Not Act Holistically on Object Memory

Memory reactivation during sleep is thought to facilitate memory consolidation. Most sleep reactivation research has examined how reactivation of specific facts, objects, and associations benefits their overall retention. However, our memories are not unitary, and not all features of a memory persist in tandem over time. Instead, our memories are transformed, with some features strengthened and others weakened. Does sleep reactivation drive memory transformation? We leveraged the Targeted Memory Reactivation technique in an object category learning paradigm to examine this question. Participants (20 female, 14 male) learned three categories of novel objects, where each object had unique, distinguishing features as well as features shared with other members of its category. We used a real-time EEG protocol to cue the reactivation of these objects during sleep at moments optimized to generate reactivation events. We found that reactivation improved memory for distinguishing features while worsening memory for shared features, suggesting a differentiation process. The results indicate that sleep reactivation does not act holistically on object memories, instead supporting a transformation where some features are enhanced over others.

J, Tandoc M, Antony J, Schapiro A. Memory reactivation during sleep does not act holistically on object memory

Memory reactivation during sleep is thought to facilitate memory consolidation. Most sleep reactivation research has examined how reactivation of specific facts, objects, and associations benefits their overall retention. However, our memories are not unitary, and not all features of a memory persist in tandem over time. Instead, our memories are transformed, with some features strengthened and others weakened. Does sleep reactivation drive memory transformation? We leveraged the Targeted Memory Reactivation technique in an object category learning paradigm to examine this question. Participants (20 female, 14 male) learned three categories of novel objects, where each object had unique, distinguishing features as well as features shared with other members of its category. We used a real-time EEG protocol to cue the reactivation of these objects during sleep at moments optimized to generate reactivation events. We found that reactivation improved memory for distinguishing features while worsening memory for shared features, suggesting a differentiation process. The results indicate that sleep reactivation does not act holistically on object memories, instead supporting a transformation process where some features are enhanced over others.

Neuromodulation via muscarinic acetylcholine pathway can facilitate distinct, complementary, and sequential roles for NREM and REM states during sleep-dependent memory consolidation

Across vertebrate species, sleep consists of repeating cycles of NREM followed by REM. However, the respective functions of NREM, REM, and their stereotypic cycling pattern are not well understood. Using a simplified biophysical network model, we show that NREM and REM sleep can play differential and critical roles in memory consolidation primarily regulated, based on state-specific changes in cholinergic signaling. Within this network, decreasing and increasing muscarinic acetylcholine (ACh) signaling during bouts of NREM and REM, respectively, differentially alters neuronal excitability and excitatory/inhibitory balance. During NREM, deactivation of inhibitory neurons leads to network-wide disinhibition and bursts of synchronized activity led by firing in engram neurons. These features strengthen connections from the original engram neurons to less-active network neurons. In contrast, during REM, an increase in network inhibition suppresses firing in all but the most-active excitatory neurons, leading to competitive strengthening/pruning of the memory trace. We tested the predictions of the model against in vivo recordings from mouse hippocampus during active sleep-dependent memory storage. Consistent with modeling results, we find that functional connectivity between CA1 neurons changes differentially at transition from NREM to REM sleep during learning. Returning to the model, we find that an iterative sequence of state-specific activations during NREM/REM cycling is essential for memory storage in the network, serving a critical role during simultaneous consolidation of multiple memories. Together these results provide a testable mechanistic hypothesis for the respective roles of NREM and REM sleep, and their universal relative timing, in memory consolidation.

Significance statement

Using a simplified computational model and in vivo recordings from mouse hippocampus, we show that NREM and REM sleep can play differential roles in memory consolidation. The specific neurophysiological features of the two sleep states allow for expansion of memory traces (during NREM) and prevention of overlap between different memory traces (during REM). These features are likely essential in the context of storing more than one new memory simultaneously within a brain network.

Plasticity-stability dynamics during post-training processing of learning

Learning continues beyond the end of training. Post-training learning is supported by changes in plasticity and stability in the brain during both wakefulness and sleep. However, the lack of a unified measure for assessing plasticity and stability dynamics during training and post-training periods has limited our understanding of how these dynamics shape learning. Focusing primarily on procedural learning, we integrate work using behavioral paradigms and a recently developed measure, the excitatory-to-inhibitory (E/I) ratio, to explore the delicate balance between plasticity and stability and its relationship to post-training learning. This reveals plasticity-stability cycles during both wakefulness and sleep that enhance learning and protect it from new learning during post-training processing.

The phase of plasticity-induced neurochemical changes of high-frequency repetitive transcranial magnetic stimulation are different from visual perceptual learning

Numerous studies have found that repetitive transcranial magnetic stimulation (rTMS) modulates plasticity. rTMS has often been used to change neural networks underlying learning, often under the assumption that the mechanism of rTMS-induced plasticity should be highly similar to that associated with learning. The presence of visual perceptual learning (VPL) reveals the plasticity of early visual systems, which is formed through multiple phases. Hence, we tested how high-frequency (HF) rTMS and VPL modulate the effect of visual plasticity by investigating neurometabolic changes in early visual areas. We employed an excitatory-to-inhibitory (E/I) ratio, which refers to glutamate concentration divided by GABA+ concentration, as an index of the degree of plasticity. We compared neurotransmitter concentration changes after applying HF rTMS to the visual cortex with those after training in a visual task, in otherwise identical procedures. Both the time courses of the E/I ratios and neurotransmitter contributions to the E/I ratio significantly differed between HF rTMS and training conditions. The peak E/I ratio occurred 3.5 h after HF rTMS with decreased GABA+, whereas the peak E/I ratio occurred 0.5 h after visual training with increased glutamate. Furthermore, HF rTMS temporally decreased the thresholds for detecting phosphene and perceiving low-contrast stimuli, indicating increased visual plasticity. These results suggest that plasticity in early visual areas induced by HF rTMS is not as involved in the early phase of development of VPL that occurs during and immediately after training.

Sleep strengthens resting-state functional communication between brain areas involved in the consolidation of problem-solving skills

Sleep consolidates procedural memory for motor skills, and this process is associated with strengthened functional connectivity in hippocampal-striatal-cortical areas. It is unknown whether similar processes occur for procedural memory that requires cognitive strategies needed for problem-solving. It is also unclear whether a full night of sleep is indeed necessary for consolidation to occur, compared with a daytime nap. We examined how resting-state functional connectivity within the hippocampal-striatal-cortical network differs after offline consolidation intervals of sleep, nap, or wake. Resting-state fMRI data were acquired immediately before and after training on a procedural problem-solving task that requires the acquisition of a novel cognitive strategy and immediately prior to the retest period (i.e., following the consolidation interval). ROI to ROI and seed to whole-brain functional connectivity analyses both specifically and consistently demonstrated strengthened hippocampal-prefrontal functional connectivity following a period of sleep versus wake. These results were associated with task-related gains in behavioral performance. Changes in functional communication were also observed between groups using the striatum as a seed. Here, we demonstrate that at the behavioral level, procedural strategies benefit from both a nap and a night of sleep. However, a full night of sleep is associated with enhanced functional communication between regions that support problem-solving skills.

Sleep-like unsupervised replay reduces catastrophic forgetting in artificial neural networks

Artificial neural networks are known to suffer from catastrophic forgetting: when learning multiple tasks sequentially, they perform well on the most recent task at the expense of previously learned tasks. In the brain, sleep is known to play an important role in incremental learning by replaying recent and old conflicting memory traces. Here we tested the hypothesis that implementing a sleep-like phase in artificial neural networks can protect old memories during new training and alleviate catastrophic forgetting. Sleep was implemented as off-line training with local unsupervised Hebbian plasticity rules and noisy input. In an incremental learning framework, sleep was able to recover old tasks that were otherwise forgotten. Previously learned memories were replayed spontaneously during sleep, forming unique representations for each class of inputs. Representational sparseness and neuronal activity corresponding to the old tasks increased while new task related activity decreased. The study suggests that spontaneous replay simulating sleep-like dynamics can alleviate catastrophic forgetting in artificial neural networks.

A failure of sleep-dependent consolidation of visuoperceptual procedural learning in young adults with ADHD

ADHD has been associated with cortico-striatal dysfunction that may lead to procedural memory abnormalities. Sleep plays a critical role in consolidating procedural memories, and sleep problems are an integral part of the psychopathology of ADHD. This raises the possibility that altered sleep processes characterizing those with ADHD could contribute to their skill-learning impairments. On this basis, the present study tested the hypothesis that young adults with ADHD have altered sleep-dependent procedural memory consolidation. Participants with ADHD and neurotypicals were trained on a visual discrimination task that has been shown to benefit from sleep. Half of the participants were tested after a 12-h break that included nocturnal sleep (sleep condition), whereas the other half were tested after a 12-h daytime break that did not include sleep (wakefulness condition) to assess the specific contribution of sleep to improvement in task performance. Despite having a similar degree of initial learning, participants with ADHD did not improve in the visual discrimination task following a sleep interval compared to neurotypicals, while they were on par with neurotypicals during the wakefulness condition. These findings represent the first demonstration of a failure in sleep-dependent consolidation of procedural learning in young adults with ADHD. Such a failure is likely to disrupt automatic control routines that are normally provided by the non-declarative memory system, thereby increasing the load on attentional resources of individuals with ADHD.

The role of sleep and wakefulness in the recognition of emotional pictures

Sleep has a beneficial effect on memory consolidation. However, its role in emotional memory is currently debated. Here, we investigate the role of sleep and a similar period of wakefulness on the recognition of emotional pictures and subjective emotional reactivity. Forty participants without any major physical, neurological or psychological condition were randomly assigned to the Sleep First Group or Wake First Group. The two groups underwent the encoding phase of an emotional images task with negative and neutral pictures at either 09:00 hours (Wake First Group) or 21:00 hours (Sleep First Group). Then participants performed an immediate recognition test (T1), and two delayed tests 12 hr (T2) and 24 hr (T3) later. Perceived arousal and valence levels were collected for each picture. Sleep parameters were recorded at participants' homes with a portable device. No differences were observed at T1, whereas at T2 the Sleep First Group showed a higher memory performance than the Wake First Group. At T3, performance decreased in the Sleep First Group (who spent the previous 12 hr awake), but not in the Wake First Group (who slept during the previous 12 hr). Overall, negative images were remembered better than neutral ones. We also observed a positive association between memory performance for negative items at the immediate test and the percentage of rapid eye movement sleep the night before the encoding. Our data confirm that negative information is remembered better over time than neutral information, and that sleep benefits the retention of declarative information. However, sleep seems not to preferentially improve emotional memory, although it may affect the encoding of negative information.

Sleep prevents catastrophic forgetting in spiking neural networks by forming a joint synaptic weight representation

Artificial neural networks overwrite previously learned tasks when trained sequentially, a phenomenon known as catastrophic forgetting. In contrast, the brain learns continuously, and typically learns best when new training is interleaved with periods of sleep for memory consolidation. Here we used spiking network to study mechanisms behind catastrophic forgetting and the role of sleep in preventing it. The network could be trained to learn a complex foraging task but exhibited catastrophic forgetting when trained sequentially on different tasks. In synaptic weight space, new task training moved the synaptic weight configuration away from the manifold representing old task leading to forgetting. Interleaving new task training with periods of off-line reactivation, mimicking biological sleep, mitigated catastrophic forgetting by constraining the network synaptic weight state to the previously learned manifold, while allowing the weight configuration to converge towards the intersection of the manifolds representing old and new tasks. The study reveals a possible strategy of synaptic weights dynamics the brain applies during sleep to prevent forgetting and optimize learning.

A model of autonomous interactions between hippocampus and neocortex driving sleep-dependent memory consolidation

How do we build up our knowledge of the world over time? Many theories of memory formation and consolidation have posited that the hippocampus stores new information, then "teaches" this information to the neocortex over time, especially during sleep. But it is unclear, mechanistically, how this actually works-How are these systems able to interact during periods with virtually no environmental input to accomplish useful learning and shifts in representation? We provide a framework for thinking about this question, with neural network model simulations serving as demonstrations. The model is composed of hippocampus and neocortical areas, which replay memories and interact with one another completely autonomously during simulated sleep. Oscillations are leveraged to support error-driven learning that leads to useful changes in memory representation and behavior. The model has a non-rapid eye movement (NREM) sleep stage, where dynamics between the hippocampus and neocortex are tightly coupled, with the hippocampus helping neocortex to reinstate high-fidelity versions of new attractors, and a REM sleep stage, where neocortex is able to more freely explore existing attractors. We find that alternating between NREM and REM sleep stages, which alternately focuses the model's replay on recent and remote information, facilitates graceful continual learning. We thus provide an account of how the hippocampus and neocortex can interact without any external input during sleep to drive useful new cortical learning and to protect old knowledge as new information is integrated.

Order matters: sleep spindles contribute to memory consolidation only when followed by rapid-eye-movement sleep

Sleep is known to benefit memory consolidation, but little is known about the contribution of sleep stages within the sleep cycle. The sequential hypothesis proposes that memories are first replayed during nonrapid-eye-movement (NREM or N) sleep and then integrated into existing networks during rapid-eye-movement (REM or R) sleep, two successive critical steps for memory consolidation. However, it lacks experimental evidence as N always precedes R sleep in physiological conditions. We tested this sequential hypothesis in patients with central hypersomnolence disorder, including patients with narcolepsy who present the unique, anti-physiological peculiarity of frequently falling asleep in R sleep before entering N sleep. Patients performed a visual perceptual learning task before and after daytime naps stopped after one sleep cycle, starting in N or R sleep and followed by the other stage (i.e. N-R vs. R-N sleep sequence). We compared over-nap changes in performance, reflecting memory consolidation, depending on the sleep sequence during the nap. Thirty-six patients who slept for a total of 67 naps were included in the analysis. Results show that sleep spindles are associated with memory consolidation only when N is followed by R sleep, that is in physiologically ordered N-R naps, thus providing support to the sequential hypothesis in humans. In addition, we found a negative effect of rapid-eye-movements in R sleep on perceptual consolidation, highlighting the complex role of sleep stages in the balance to remember and to forget.

Links between the brain and body during sleep: implications for memory processing

Sleep is intimately related to memory processes. The established view is that the transformation of experiences into long-term memories is linked to sleep-related CNS function. However, there is increasing evidence that the autonomic nervous system (ANS), long recognized to modulate cognition during waking, can impact memory processing during sleep. Here, we review human research that examines the role of autonomic activity and sleep in memory formation. We argue that autonomic activity during sleep may set the stage for the CNS dynamics associated with sleep and memory stability and integration. Further, we consider how the link between ANS activity and polysomnographic markers of sleep may help elucidate both healthy and pathological cognitive aging in humans.

Replay in Deep Learning: Current Approaches and Missing Biological Elements

Replay is the reactivation of one or more neural patterns that are similar to the activation patterns experienced during past waking experiences. Replay was first observed in biological neural networks during sleep, and it is now thought to play a critical role in memory formation, retrieval, and consolidation. Replay-like mechanisms have been incorporated in deep artificial neural networks that learn over time to avoid catastrophic forgetting of previous knowledge. Replay algorithms have been successfully used in a wide range of deep learning methods within supervised, unsupervised, and reinforcement learning paradigms. In this letter, we provide the first comprehensive comparison between replay in the mammalian brain and replay in artificial neural networks. We identify multiple aspects of biological replay that are missing in deep learning systems and hypothesize how they could be used to improve artificial neural networks.

Stability of neural encoding moderates the contribution of sleep and repeated testing to memory consolidation

There is evidence suggesting that online consolidation during retrieval-mediated learning interacts with offline consolidation during subsequent sleep to transform memory. Here we investigate whether this interaction persists when retrieval-mediated learning follows post-training sleep and whether the direction of this interaction is conditioned by the quality of encoding resulting from manipulation of the amount of sleep on the previous night. The quality of encoding was determined by computing the degree of similarity between EEG-activity patterns across restudy of face pairs in two groups of young participants, one who slept the last 4 h of the pre-training night, and another who slept 8 h. The offline consolidation was assessed by computing the degree of coupling between slow oscillations (SOs) and spindles (SPs) during post-training sleep, while the online consolidation was evaluated by determining the degree of similarity between EEG-activity patterns recorded during the study phase and during repeated recognition of either the same face pair (i.e., specific similarity) or face pairs sharing sex and profession (i.e., categorical similarity) to evaluate differentiation and generalization, respectively. The study and recognition phases were separated by a night of normal sleep duration. Mixed-effects models revealed that the stability of neural encoding moderated the relationship between sleep- and retrieval-mediated consolidation processes over left frontal regions. For memories showing lower encoding stability, the enhanced SO-SP coupling was associated with increased reinstatement of category-specific encoding-related activity at the expense of content-specific activity, whilst the opposite occurred for memories showing greater encoding stability. Overall, these results suggest that offline consolidation during post-training sleep interacts with online consolidation during retrieval the next day to favor the reorganization of memory contents, by increasing specificity of stronger memories and generalization of the weaker ones.

The Black Box effect: sensory stimulation after learning interferes with the retention of long-term object location memory in rats

Reducing sensory experiences during the period that immediately follows learning improves long-term memory retention in healthy humans, and even preserves memory in patients with amnesia. To date, it is entirely unclear why this is the case, and identifying the neurobiological mechanisms underpinning this effect requires suitable animal models, which are currently lacking. Here, we describe a straightforward experimental procedure in rats that future studies can use to directly address this issue. Using this method, we replicated the central findings on quiet wakefulness obtained in humans: We show that rats that spent 1 h alone in a familiar dark and quiet chamber (the Black Box) after exploring two objects in an open field expressed long-term memory for the object locations 6 h later, while rats that instead directly went back into their home cage with their cage mates did not. We discovered that both visual stimulation and being together with conspecifics contributed to the memory loss in the home cage, as exposing rats either to light or to a cage mate in the Black Box was sufficient to disrupt memory for object locations. Our results suggest that in both rats and humans, everyday sensory experiences that normally follow learning in natural settings can interfere with processes that promote long-term memory retention, thereby causing forgetting in form of retroactive interference. The processes involved in this effect are not sleep-dependent because we prevented sleep in periods of reduced sensory experience. Our findings, which also have implications for research practices, describe a potentially useful method to study the neurobiological mechanisms that might explain why normal sensory processing after learning impairs memory both in healthy humans and in patients suffering from amnesia.

Sleep Enhances Consolidation of Memory Traces for Complex Problem-Solving Skills

Sleep consolidates memory for procedural motor skills, reflected by sleep-dependent changes in the hippocampal-striatal-cortical network. Other forms of procedural skills require the acquisition of a novel strategy to solve a problem, which recruit overlapping brain regions and specialized areas including the caudate and prefrontal cortex. Sleep preferentially benefits strategy and problem-solving skills over the accompanying motor execution movements. However, it is unclear how acquiring new strategies benefit from sleep. Here, participants performed a task requiring the execution of a sequence of movements to learn a novel cognitive strategy. Participants performed this task while undergoing fMRI before and after an interval of either a full night sleep, a daytime nap, or wakefulness. Participants also performed a motor control task, which precluded the opportunity to learn the strategy. In this way, we subtracted motor execution-related brain activations from activations specific to the strategy. The sleep and nap groups experienced greater behavioral performance improvements compared to the wake group on the strategy-based task. Following sleep, we observed enhanced activation of the caudate in addition to other regions in the hippocampal-striatal-cortical network, compared to wakefulness. This study demonstrates that sleep is a privileged time to enhance newly acquired cognitive strategies needed to solve problems.

Replay in minds and machines

Experience-related brain activity patterns reactivate during sleep, wakeful rest, and brief pauses from active behavior. In parallel, machine learning research has found that experience replay can lead to substantial performance improvements in artificial agents. Together, these lines of research suggest replay has a variety of computational benefits for decision-making and learning. Here, we provide an overview of putative computational functions of replay as suggested by machine learning and neuroscientific research. We show that replay can lead to faster learning, less forgetting, reorganization or augmentation of experiences, and support planning and generalization. In addition, we highlight the benefits of reactivating abstracted internal representations rather than veridical memories, and discuss how replay could provide a mechanism to build internal representations that improve learning and decision-making.

'Sleep-dependent' memory consolidation? Brief periods of post-training rest and sleep provide an equivalent benefit for both declarative and procedural memory

Sleep following learning facilitates the consolidation of memories. This effect has often been attributed to sleep-specific factors, such as the presence of sleep spindles or slow waves in the electroencephalogram (EEG). However, recent studies suggest that simply resting quietly while awake could confer a similar memory benefit. In the current study, we examined the effects of sleep, quiet rest, and active wakefulness on the consolidation of declarative and procedural memory. We hypothesized that sleep and eyes-closed quiet rest would both benefit memory compared with a period of active wakefulness. After completing a declarative and a procedural memory task, participants began a 30-min retention period with PSG (polysomnographic) monitoring, in which they either slept (n = 24), quietly rested with their eyes closed (n = 22), or completed a distractor task (n = 29). Following the retention period, participants were again tested on their memory for the two learning tasks. As hypothesized, sleep and quiet rest both led to better performance on the declarative and procedural memory tasks than did the distractor task. Moreover, the performance advantages conferred by rest were indistinguishable from those of sleep. These data suggest that neurobiology specific to sleep might not be necessary to induce the consolidation of memory, at least across very short retention intervals. Instead, offline memory consolidation may function opportunistically, occurring during either sleep or stimulus-free rest, provided a favorable neurobiological milieu and sufficient reduction of new encoding.

Does sleep-dependent consolidation favour weak memories?

Sleep stabilizes newly acquired memories, a process referred to as memory consolidation. According to recent studies, sleep-dependent consolidation processes might be deployed to different extents for different types of memories. In particular, weaker memories might benefit greater from post-learning sleep than stronger memories. However, under standard testing conditions, sleep-dependent consolidation effects for stronger memories might be obscured by ceiling effects. To test this possibility, we devised a new memory paradigm (Memory Arena) in which participants learned temporospatial arrangements of objects. Prior to a delay period spent either awake or asleep, training thresholds were controlled to yield relatively weak or relatively strong memories. After the delay period, retrieval difficulty was controlled via the presence or absence of a retroactive interference task. Under standard testing conditions (no interference), a sleep-dependent consolidation effect was indeed observed for weaker memories only. Critically though, with increased retrieval demands, sleep-dependent consolidation effects were seen for both weaker and stronger memories. These results suggest that all memories are consolidated during sleep, but that memories of different strengths require different testing conditions to unveil their benefit from post-learning sleep.

Comparing the effect of daytime sleep and wakefulness on mnemonic discrimination

Sleep is considered the optimal state to consolidate hippocampal-dependent memories. A particular memory process is mnemonic discrimination. Mnemonic discrimination refers to the ability to differentiate between novel and previously encountered information. Previous studies have found that mnemonic discrimination is impaired by sleep deprivation, whereas nocturnal sleep seems to protect memory representations when compared to a similar period of wakefulness. In this study we tested whether a daytime nap can facilitate mnemonic discrimination as assessed by the Mnemonic Similarity Task. Thirty-eight participants performed incidental learning of 256 images of unique everyday items at about 12:00 PM. Fifteen minutes later, in a recognition test, they were presented with 192 images: 64 targets (Old), 64 foils (New) and 64 lures (Similar to targets). For each image they had to decide whether it was already presented, never presented, or similar to an image presented during the encoding session. Then participants were split into a Nap group (N=19), who had a 90-min nap opportunity in the lab, and a Wake group (N=19), who stayed in the lab playing a low-arousing game. At 3:00 PM all participants performed a delayed recognition test, similar to the immediate test but with different images. Similar memory discrimination was observed in both the Nap and Wake group. The lack of a beneficial effect of sleep could be due to the differences between diurnal and nocturnal sleep and/or the potential role of videogames in facilitating memory discrimination during wakefulness.

Can sleep protect memories from catastrophic forgetting?

Continual learning remains an unsolved problem in artificial neural networks. The brain has evolved mechanisms to prevent catastrophic forgetting of old knowledge during new training. Building upon data suggesting the importance of sleep in learning and memory, we tested a hypothesis that sleep protects old memories from being forgotten after new learning. In the thalamocortical model, training a new memory interfered with previously learned old memories leading to degradation and forgetting of the old memory traces. Simulating sleep after new learning reversed the damage and enhanced old and new memories. We found that when a new memory competed for previously allocated neuronal/synaptic resources, sleep replay changed the synaptic footprint of the old memory to allow overlapping neuronal populations to store multiple memories. Our study predicts that memory storage is dynamic, and sleep enables continual learning by combining consolidation of new memory traces with reconsolidation of old memory traces to minimize interference.

Complementary contributions of non-REM and REM sleep to visual learning

Sleep is beneficial for learning. However, it remains unclear whether learning is facilitated by non-REM (NREM) sleep or by REM sleep, whether it results from plasticity increases or stabilization, and whether facilitation results from learning-specific processing. Here, we trained volunteers on a visual task, and measured the excitatory and inhibitory (E/I) balance in early visual areas during subsequent sleep as an index of plasticity. E/I balance increased during NREM sleep irrespective of whether pre-sleep learning occurred, but it was associated with post-sleep performance gains relative to pre-sleep performance. By contrast, E/I balance decreased during REM sleep but only after pre-sleep training, and the decrease was associated with stabilization of pre-sleep learning. These findings indicate that NREM sleep promotes plasticity, leading to performance gains independent of learning, while REM sleep decreases plasticity to stabilize learning in a learning-specific manner.

Transient cholinergic enhancement does not significantly affect either the magnitude or selectivity of perceptual learning of visual texture discrimination

Perceptual learning (PL), often characterized by improvements in perceptual performance with training that are specific to the stimulus conditions used during training, exemplifies experience-dependent cortical plasticity. An improved understanding of how neuromodulatory systems shape PL promises to provide new insights into the mechanisms of plasticity, and by extension how PL can be generated and applied most efficiently. Previous studies have reported enhanced PL in human subjects following administration of drugs that increase signaling through acetylcholine (ACh) receptors, and physiological evidence indicates that ACh sharpens neuronal selectivity, suggesting that this neuromodulator supports PL and its stimulus specificity. Here we explored the effects of enhancing endogenous cholinergic signaling during PL of a visual texture discrimination task. We found that training on this task in the lower visual field yielded significant behavioral improvement at the trained location. However, a single dose of the cholinesterase inhibitor donepezil, administered before training, did not significantly impact either the magnitude or the location specificity of texture discrimination learning compared with placebo. We discuss potential explanations for discrepant findings in the literature regarding the role of ACh in visual PL, including possible differences in plasticity mechanisms in the dorsal and ventral cortical processing streams.

Binocular disparity-based learning is retinotopically specific and independent of sleep

Sleep supports the consolidation of recently encoded declarative and procedural memories. An important component of this effect is the repeated reactivation of neuronal ensemble activity elicited during memory encoding. For perceptual learning, however, sleep benefits have only been reported for specific tasks and it is not clear whether sleep targets low-level perceptual, higher-order temporal or attentional aspects of performance. Here, we employed a coarse binocular disparity discrimination task, known to rely on low-level stereoscopic vision. We show that human subjects improve over training and retain the same performance level across a 12-h retention period. Improvements do not generalize to other parts of the visual field and are unaffected by whether the retention period contains sleep or not. These results are compatible with the notion that behavioural improvements in binocular disparity discrimination do not additionally benefit from sleep when compared with the same time spent awake. We hypothesize that this might generalize to other strictly low-level perceptual tasks. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.

Moderating effects of visual attention and action video game play on perceptual learning with the texture discrimination task

There is currently substantial controversy regarding the reliability of observed patterns of perceptual learning. Contributing to this controversy are a lack of accounting for individual differences and how variations in training can give rise to different patterns of learning. Here we sought to investigate the impact of individual differences in attention, as measured with the Useful Field of View (UFOV) task, and action video game use on perceptual learning in a large sample of subjects trained on a Texture Discrimination Task (TDT). We examined baseline performance on the TDT, learning on the initially trained TDT stimuli and transfer to a subsequently trained background orientation. We find that participants showing better performance on the UFOV task performed better on the TDT, and also showed greater learning and transfer to an untrained background orientation. On the other hand, self-report of action video game play only inconsistently related performance, learning or transfer on the TDT. Further, we failed to replicate previous findings that training with different backgrounds gives rise to interference on the TDT. Together these results suggest that, while differences between individuals and differences in task structure play a role in perceptual learning, previous findings on the impact of action video game use and interference between training stimuli in perceptual learning may be idiosyncratic.

Weakly encoded memories due to acute sleep restriction can be rescued after one night of recovery sleep

Sleep is thought to play a complementary role in human memory processing: sleep loss impairs the formation of new memories during the following awake period and, conversely, normal sleep promotes the strengthening of the already encoded memories. However, whether sleep can strengthen deteriorated memories caused by insufficient sleep remains unknown. Here, we showed that sleep restriction in a group of participants caused a reduction in the stability of EEG activity patterns across multiple encoding of the same event during awake, compared with a group of participants that got a full night's sleep. The decrease of neural stability patterns in the sleep-restricted group was associated with higher slow oscillation-spindle coupling during a subsequent night of normal sleep duration, thereby suggesting the instantiation of restorative neural mechanisms adaptively supporting cognition and memory. Importantly, upon awaking, the two groups of participants showed equivalent retrieval accuracy supported by subtle differences in the reinstatement of encoding-related activity: it was longer lasting in sleep-restricted individuals than in controls. In addition, sustained reinstatement over time was associated with increased coupling between spindles and slow oscillations. Taken together, these results suggest that the strength of prior encoding might be an important moderator of memory consolidation during sleep. Supporting this view, spindles nesting in the slow oscillation increased the probability of correct recognition only for weakly encoded memories. Current results demonstrate the benefit that a full night's sleep can induce to impaired memory traces caused by an inadequate amount of sleep.

Comparing the Effects of Sleep and Rest on Memory Consolidation

INTRODUCTION

There is ample evidence that overnight sleep and daytime naps benefit memory retention, compared to comparable amounts of active wakefulness. Yet recent evidence also suggests that a period of post-training rest (eg, quiet wakefulness with eyes closed) provides a similar memory benefit compared to wake. However, the relative benefits of sleep vs quiet waking rest on memory remain poorly understood. Here, we assessed the extent to which sleep provides a unique memory benefit, above and beyond that conferred by quiet waking rest.

METHODS

In a sample of healthy undergraduate students (N=83), we tested the effect of 30 mins of post-learning sleep, rest, or active wake on concept learning (dot pattern classification) and declarative memory (word pair associates) across a 4-hr daytime training-retest interval.

RESULTS AND CONCLUSIONS

Contrary to our hypotheses, we found no differences in performance between the three conditions for either task. The findings are interpreted with reference to methodological considerations including the length of the experimental interval, the nature of the tasks used, and challenges inherent in creating experimental conditions that can be executed by participants.

Timing between Cortical Slow Oscillations and Heart Rate Bursts during Sleep Predicts Temporal Processing Speed, but Not Offline Consolidation

Central and autonomic nervous system activities are coupled during sleep. Cortical slow oscillations (SOs; <1 Hz) coincide with brief bursts in heart rate (HR), but the functional consequence of this coupling in cognition remains elusive. We measured SO-HR temporal coupling (i.e., the peak-to-peak interval between downstate of SO event and HR burst) during a daytime nap and asked whether this SO-HR timing measure was associated with temporal processing speed and learning on a texture discrimination task by testing participants before and after a nap. The coherence of SO-HR events during sleep strongly correlated with an individual's temporal processing speed in the morning and evening test sessions, but not with their change in performance after the nap (i.e., consolidation). We confirmed this result in two additional experimental visits and also discovered that this association was visit-specific, indicating a state (not trait) marker. Thus, we introduce a novel physiological index that may be a useful marker of state-dependent processing speed of an individual.

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.

Melatonin modulates daytime-dependent synaptic plasticity and learning efficiency

Mechanisms of hippocampus-related memory formation are time-of-day-dependent. While the circadian system and clock genes are related to timing of hippocampal mnemonic processes (acquisition, consolidation, and retrieval of long-term memory [LTM]) and long-term potentiation (LTP), little is known about temporal gating mechanisms. Here, the role of the neurohormone melatonin as a circadian time cue for hippocampal signaling and memory formation was investigated in C3H/He wildtype (WT) and melatonin receptor-knockout ( MT 1 / 2 - / - ) mice. Immunohistochemical and immunoblot analyses revealed the presence of melatonin receptors on mouse hippocampal neurons. Temporal patterns of time-of-day-dependent clock gene protein levels were profoundly altered in MT 1 / 2 - / - mice compared to WT animals. On the behavioral level, WT mice displayed better spatial learning efficiency during daytime as compared to nighttime. In contrast, high error scores were observed in MT 1 / 2 - / - mice during both, daytime and nighttime acquisition. Day-night difference in LTP, as observed in WT mice, was absent in MT 1 / 2 - / - mice and in WT animals, in which the sympathetic innervation of the pineal gland was surgically removed to erase rhythmic melatonin synthesis. In addition, treatment of melatonin-deficient C57BL/6 mice with melatonin at nighttime significantly improved their working memory performance at daytime. These results illustrate that melatonin shapes time-of-day-dependent learning efficiency in parallel to consolidating expression patterns of clock genes in the mouse hippocampus. Our data suggest that melatonin imprints a time cue on mouse hippocampal signaling and gene expression to foster better learning during daytime.

The effect of LTP- and LTD-like visual stimulation on modulation of human orientation discrimination

Studies showing that repetitive visual stimulation protocols alter perception and induce cortical reorganization, as well-reported for the tactile domain, have been sparse. In this study, we investigated how “long-term potentiation [LTP]-like” and “long-term depression [LTD]-like” repetitive visual stimulation affects orientation discrimination ability in human observers. LTP-like stimulation with features most closely resembling the stimuli used during behavioral assessment evoked the largest improvement, while the effects were smaller in protocols that differed in shape or orientation features. This gradient suggests lower learning specificity than classical perceptual learning experiments, possibly because of an interplay of task- and feature-based factors. All modulatory effects of repetitive stimulation were superimposed on top of spontaneous task learning. Moreover, blockwise analysis revealed that LTP-like stimulation, in contrast to LTD-like or sham stimulation, prevented a loss of practice-related gain of orientation discrimination thresholds. This observation highlights a critical role of LTP-like stimulation for consolidation, typically observed during sleep.

The impact of frequent napping and nap practice on sleep-dependent memory in humans

Napping benefits long-term memory formation and is a tool many individuals use to improve daytime functioning. Despite its potential advantages, approximately 47% of people in the United States eschew napping. The goal of this study was to determine whether people who endorse napping at least once a week (nap+) show differences in nap outcomes, including nap-dependent memory consolidation, compared with people who rarely or never nap (nap-). Additionally, we tested whether four weeks of nap practice or restriction would change sleep and performance profiles. Using a perceptual learning task, we found that napping enhanced performance to a greater degree in nap+ compared with nap- individuals (at baseline). Additionally, performance change was associated with different electrophysiological sleep features in each group. In the nap+ group, spindle density was positively correlated with performance improvement, an effect specific to spindles in the hemisphere contralateral to the trained visual field. In the nap- group, slow oscillatory power (0.5-1 Hz) was correlated with performance. Surprisingly, no changes to performance or brain activity during sleep emerged after four weeks of nap practice or restriction. These results suggest that individual differences may impact the potential benefits of napping on performance and the ability to become a better napper.

Human hippocampal replay during rest prioritizes weakly learned information and predicts memory performance

The hippocampus replays experiences during quiet rest periods, and this replay benefits subsequent memory. A critical open question is how memories are prioritized for this replay. We used functional magnetic resonance imaging (fMRI) pattern analysis to track item-level replay in the hippocampus during an awake rest period after participants studied 15 objects and completed a memory test. Objects that were remembered less well were replayed more during the subsequent rest period, suggesting a prioritization process in which weaker memories—memories most vulnerable to forgetting—are selected for replay. In a second session 12 hours later, more replay of an object during a rest period predicted better subsequent memory for that object. Replay predicted memory improvement across sessions only for participants who slept during that interval. Our results provide evidence that replay in the human hippocampus prioritizes weakly learned information, predicts subsequent memory performance, and relates to memory improvement across a delay with sleep.

The hippocampus is known to 'replay' experiences and memories during rest periods, but it is unclear how particular memories are prioritized for replay. Here, the authors show that information that is remembered less well is replayed more often, suggesting that weaker memories are selected for replay.

Differential roles of sleep spindles and sleep slow oscillations in memory consolidation

Sleep plays an important role in the consolidation of recent memories. However, the cellular and synaptic mechanisms of consolidation during sleep remain poorly understood. In this study, using a realistic computational model of the thalamocortical network, we tested the role of Non-Rapid Eye Movement (NREM) sleep in memory consolidation. We found that sleep spindles (the hallmark of N2 stage sleep) and slow oscillations (the hallmark of N3 stage sleep) both promote replay of the spike sequences learned in the awake state and replay was localized at the trained network locations. Memory performance improved after a period of NREM sleep but not after the same time period in awake. When multiple memories were trained, the local nature of the spike sequence replay during spindles allowed replay of the distinct memory traces independently, while slow oscillations promoted competition that could prevent replay of the weak memories in a presence of the stronger memory traces. This could lead to extinction of the weak memories unless when sleep spindles (N2 sleep) preceded slow oscillations (N3 sleep), as observed during the natural sleep cycle. Our study presents a mechanistic explanation for the role of sleep rhythms in memory consolidation and proposes a testable hypothesis how the natural structure of sleep stages provides an optimal environment to consolidate memories.

Shaping memory consolidation via targeted memory reactivation during sleep

Recent studies have shown that the reactivation of specific memories during sleep can be modulated using external stimulation. Specifically, it has been reported that matching a sensory stimulus (e.g., odor or sound cue) with target information (e.g., pairs of words, pictures, and motor sequences) during wakefulness, and then presenting the cue alone during sleep, facilitates memory of the target information. Thus, presenting learned cues while asleep may reactivate related declarative, procedural, and emotional material, and facilitate the neurophysiological processes underpinning memory consolidation in humans. This paradigm, which has been named targeted memory reactivation, has been successfully used to improve visuospatial and verbal memories, strengthen motor skills, modify implicit social biases, and enhance fear extinction. However, these studies also show that results depend on the type of memory investigated, the task employed, the sensory cue used, and the specific sleep stage of stimulation. Here, we present a review of how memory consolidation may be shaped using noninvasive sensory stimulation during sleep.

Circuit mechanisms of hippocampal reactivation during sleep

The hippocampus is important for memory and learning, being a brain site where initial memories are formed and where sharp wave - ripples (SWR) are found, which are responsible for mapping recent memories to long-term storage during sleep-related memory replay. While this conceptual schema is well established, specific intrinsic and network-level mechanisms driving spatio-temporal patterns of hippocampal activity during sleep, and specifically controlling off-line memory reactivation are unknown. In this study, we discuss a model of hippocampal CA1-CA3 network generating spontaneous characteristic SWR activity. Our study predicts the properties of CA3 input which are necessary for successful CA1 ripple generation and the role of synaptic interactions and intrinsic excitability in spike sequence replay during SWRs. Specifically, we found that excitatory synaptic connections promote reactivation in both CA3 and CA1, but the different dynamics of sharp waves in CA3 and ripples in CA1 result in a differential role for synaptic inhibition in modulating replay: promoting spike sequence specificity in CA3 but not in CA1 areas. Finally, we describe how awake learning of spatial trajectories leads to synaptic changes sufficient to drive hippocampal cells' reactivation during sleep, as required for sleep-related memory consolidation.

Towards a whole brain model of Perceptual Learning

A hallmark of modern Perceptual Learning (PL) is the extent to which learning is specific to the trained stimuli. Such specificity to orientation, spatial location and even eye of training has been used as psychophysical evidence of the neural basis of learning. This argument that specificity of PL implies regionalization of brain plasticity implicitly assumes that examination of a singular locus of PL is an appropriate approach to understand learning. However, recent research shows that learning effects once thought to be specific depend on subtleties of the training paradigm and that within even a simple training procedure there are multiple aspects of the task and stimuli that are learned simultaneously. Here, we suggest that learning on any task involves a broad network of brain regions undergoing changes in representations, read-out weights, decision rules, attention and feedback processes as well as oculomotor changes. However, importantly, the distribution of learning across the neural system depends upon the details of the training procedure and the characterstics of the individual being trained. We propose that to advance our understanding of PL, the field must move towards understanding how distributed brain processes jointly contribute to behavioral learning effects.

Sleep-Dependent Memory Consolidation and Incremental Sentence Comprehension: Computational Dependencies during Language Learning as Revealed by Neuronal Oscillations

We hypothesize a beneficial influence of sleep on the consolidation of the combinatorial mechanisms underlying incremental sentence comprehension. These predictions are grounded in recent work examining the effect of sleep on the consolidation of linguistic information, which demonstrate that sleep-dependent neurophysiological activity consolidates the meaning of novel words and simple grammatical rules. However, the sleep-dependent consolidation of sentence-level combinatorics has not been studied to date. Here, we propose that dissociable aspects of sleep neurophysiology consolidate two different types of combinatory mechanisms in human language: sequence-based (order-sensitive) and dependency-based (order-insensitive) combinatorics. The distinction between the two types of combinatorics is motivated both by cross-linguistic considerations and the neurobiological underpinnings of human language. Unifying this perspective with principles of sleep-dependent memory consolidation, we posit that a function of sleep is to optimize the consolidation of sequence-based knowledge (the when) and the establishment of semantic schemas of unordered items (the what) that underpin cross-linguistic variations in sentence comprehension. This hypothesis builds on the proposal that sleep is involved in the construction of predictive codes, a unified principle of brain function that supports incremental sentence comprehension. Finally, we discuss neurophysiological measures (EEG/MEG) that could be used to test these claims, such as the quantification of neuronal oscillations, which reflect basic mechanisms of information processing in the brain.

Sleep Benefits Memory for Semantic Category Structure While Preserving Exemplar-Specific Information

Semantic memory encompasses knowledge about both the properties that typify concepts (e.g. robins, like all birds, have wings) as well as the properties that individuate conceptually related items (e.g. robins, in particular, have red breasts). We investigate the impact of sleep on new semantic learning using a property inference task in which both kinds of information are initially acquired equally well. Participants learned about three categories of novel objects possessing some properties that were shared among category exemplars and others that were unique to an exemplar, with exposure frequency varying across categories. In Experiment 1, memory for shared properties improved and memory for unique properties was preserved across a night of sleep, while memory for both feature types declined over a day awake. In Experiment 2, memory for shared properties improved across a nap, but only for the lower-frequency category, suggesting a prioritization of weakly learned information early in a sleep period. The increase was significantly correlated with amount of REM, but was also observed in participants who did not enter REM, suggesting involvement of both REM and NREM sleep. The results provide the first evidence that sleep improves memory for the shared structure of object categories, while simultaneously preserving object-unique information.