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Brain rhythms of sleep reflect neuronal activity underlying sleep‐associated memory consolidation. The modulation of brain rhythms, such as the sleep slow oscillation (SO), is used both to investigate neurophysiological mechanisms as well as to measure the impact of sleep on presumed functional correlates. Previously, closed‐loop acoustic stimulation in humans targeted to the SO Up‐state successfully enhanced the slow oscillation rhythm and phase‐dependent spindle activity, although effects on memory retention have varied. Here, we aim to disclose relations between stimulation‐induced hippocampo‐thalamo‐cortical activity and retention performance on a hippocampus‐dependent object‐place recognition task in mice by applying acoustic stimulation at four estimated SO phases compared to sham condition. Across the 3‐h retention interval at the beginning of the light phase closed‐loop stimulation failed to improve retention significantly over sham. However, retention during SO Up‐state stimulation was significantly higher than for another SO phase. At all SO phases, acoustic stimulation was accompanied by a sharp increase in ripple activity followed by about a second‐long suppression of hippocampal sharp wave ripple and longer maintained suppression of thalamo‐cortical spindle activity. Importantly, dynamics of SO‐coupled hippocampal ripple activity distinguished SOUp‐state stimulation. Non‐rapid eye movement (NREM) sleep was not impacted by stimulation, yet preREM sleep duration was effected. Results reveal the complex effect of stimulation on the brain dynamics and support the use of closed‐loop acoustic stimulation in mice to investigate the inter‐regional mechanisms underlying memory consolidation.

 

Sleep is able to contribute not only to memory consolidation, but also to post‐sleep learning. The notion exists that either synaptic downscaling or another process during sleep increase post‐sleep learning capacity. A correlation between augmentation of the sleep slow oscillation and hippocampal activation at encoding support the contribution of sleep to encoding of declarative memories. In the present study, the effect of closed‐loop acoustic stimulation during an afternoon nap on post‐sleep encoding of two verbal (word pairs, verbal learning and memory test) and non‐verbal (figural pairs) tasks and on electroencephalogram during sleep and learning were investigated in young healthy adults (N = 16). Closed‐loop acoustic stimulation enhanced slow oscillatory and spindle activity, but did not affect encoding at the group level. Subgroup analyses and comparisons with similar studies lead us to the tentative conclusion that further parameters such as time of day and subjects' cognitive ability influenced responses to closed‐loop acoustic stimulation.

 

Hippocampal sharp‐wave ripples (SWRs) support the reactivation of memory representations, relaying information to neocortex during “offline” and sleep‐dependent memory consolidation. While blockade of NMDA receptors (NMDAR) is known to affect both learning and subsequent consolidation, the specific contributions of NMDAR activation to SWR‐associated activity remain unclear. Here, we combine biophysical modeling with in vivo local field potential (LFP) and unit recording to quantify changes in SWR dynamics following inactivation of NMDAR. In a biophysical model of CA3‐CA1 SWR activity, we find that NMDAR removal leads to reduced SWR density, but spares SWR properties such as duration, cell recruitment and ripple frequency. These predictions are confirmed by experiments in which NMDAR‐mediated transmission in rats was inhibited using three different NMDAR antagonists, while recording dorsal CA1 LFP. In the model, loss of NMDAR‐mediated conductances also induced a reduction in the proportion of cell pairs that co‐activate significantly above chance across multiple events. Again, this prediction is corroborated by dorsal CA1 single‐unit recordings, where the NMDAR blocker ketamine disrupted correlated spiking during SWR. Our results are consistent with a framework in which NMDA receptors both promote activation of SWR events and organize SWR‐associated spiking content. This suggests that, while SWR are short‐lived events emerging in fast excitatory‐inhibitory networks, slower network components including NMDAR‐mediated currents contribute to ripple density and promote consistency in the spiking content across ripples, underpinning mechanisms for fine‐tuning of memory consolidation processes.

 

Temporal interactions between non-rapid eye movement (NREM) sleep rhythms especially the coupling between cortical slow oscillations (SO, ∼1 Hz) and thalamic spindles (∼12 Hz) have been proposed to contribute to multi-regional interactions crucial for memory processing and cognitive ability. We investigated relationships between NREM sleep depth, sleep spindles and SO-spindle coupling regarding memory ability and memory consolidation in healthy humans. Findings underscore the functional relevance of spindle dynamics (slow versus fast), SO-phase, and most importantly NREM sleep depth for cognitive processing. Cross-frequency coupling analyses demonstrated stronger precise temporal coordination of slow spindles to SO down-state in N2 for subjects with higher general memory ability. A GLM model underscored this relationship, and furthermore that fast spindle properties were predictive of overnight memory consolidation. Our results suggest cognitive fingerprints dependent on conjoint fine-tuned SO-spindle temporal coupling, spindle properties, and brain sleep state. 

Cortical slow oscillations (SOs) and thalamocortical sleep spindles are two prominent EEG rhythms of slow wave sleep. These EEG rhythms play an essential role in memory consolidation. In humans, sleep spindles are categorized into slow spindles (8–12 Hz) and fast spindles (12–16 Hz), with different properties. Slow spindles that couple with the up-to-down phase of the SO require more experimental and computational investigation to disclose their origin, functional relevance and most importantly their relation with SOs regarding memory consolidation. To examine slow spindles, we propose a biophysical thalamocortical model with two independent thalamic networks (one for slow and the other for fast spindles). Our modeling results show that fast spindles lead to faster cortical cell firing, and subsequently increase the amplitude of the cortical local field potential (LFP) during the SO down-to-up phase. Slow spindles also facilitate cortical cell firing, but the response is slower, thereby increasing the cortical LFP amplitude later, at the SO up-to-down phase of the SO cycle. Neither the SO rhythm nor the duration of the SO down state is affected by slow spindle activity. Furthermore, at a more hyperpolarized membrane potential level of fast thalamic subnetwork cells, the activity of fast spindles decreases, while the slow spindles activity increases. Together, our model results suggest that slow spindles may facilitate the initiation of the following SO cycle, without however affecting expression of the SO Up and Down states.

 

Abstract 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. 

Abstract Study Objectives Synchronization of neural activity within local networks and between brain regions is a major contributor to rhythmic field potentials such as the EEG. On the other hand, dynamic changes in microstructure and activity are reflected in the EEG, for instance slow oscillation (SO) slope can reflect synaptic strength. SO-spindle coupling is a measure for neural communication. It was previously associated with memory consolidation, but also shown to reveal strong interindividual differences. In studies, weak electric current stimulation has modulated brain rhythms and memory retention. Here, we investigate whether SO-spindle coupling and SO slope during baseline sleep are associated with (predictive of) stimulation efficacy on retention performance. Methods Twenty-five healthy subjects participated in three experimental sessions. Sleep-associated memory consolidation was measured in two sessions, in one anodal transcranial direct current stimulation oscillating at subjects individual SO frequency (so-tDCS) was applied during nocturnal sleep. The third session was without a learning task (baseline sleep). The dependence on SO-spindle coupling and SO-slope during baseline sleep of so-tDCS efficacy on retention performance were investigated. Results Stimulation efficacy on overnight retention of declarative memories was associated with nesting of slow spindles to SO trough in deep nonrapid eye movement baseline sleep. Steepness and direction of SO slope in baseline sleep were features indicative for stimulation efficacy. Conclusions Findings underscore a functional relevance of activity during the SO up-to-down state transition for memory consolidation and provide support for distinct consolidation mechanisms for types of declarative memories. 

Abstract Sleep is one of the most ubiquitous but also complex animal behaviors. It is regulated at the global, systems level scale by circadian and homeostatic processes. Across the 24-h day, distribution of sleep/wake activity differs between species, with global sleep states characterized by defined patterns of brain electric activity and electromyography. Sleep patterns have been most intensely investigated in mammalian species. The present review begins with a brief overview on current understandings on the regulation of sleep, and its interaction with aging. An overview on age-related variations in the sleep states and associated electrophysiology and oscillatory events in humans as well as in the most common laboratory rodents follows. We present findings observed in different studies and meta-analyses, indicating links to putative physiological changes in the aged brain. Concepts requiring a more integrative view on the role of circadian and homeostatic sleep regulatory mechanisms to explain aging in sleep are emerging.