Temporally structured replay of awake hippocampal ensemble activity during rapid eye movement sleep

Remapping revisited: how the hippocampus represents different spaces

The representation of distinct spaces by hippocampal place cells has been linked to changes in their place fields (the locations in the environment where the place cells discharge strongly), a phenomenon that has been termed 'remapping'. Remapping has been assumed to be accompanied by the reorganization of subsecond cofiring relationships among the place cells, potentially maximizing hippocampal information coding capacity. However, several observations challenge this standard view. For example, place cells exhibit mixed selectivity, encode non-positional variables, can have multiple place fields and exhibit unreliable discharge in fixed environments. Furthermore, recent evidence suggests that, when measured at subsecond timescales, the moment-to-moment cofiring of a pair of cells in one environment is remarkably similar in another environment, despite remapping. Here, I propose that remapping is a misnomer for the changes in place fields across environments and suggest instead that internally organized manifold representations of hippocampal activity are actively registered to different environments to enable navigation, promote memory and organize knowledge.

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.

Older adults at greater risk for Alzheimer's disease show stronger associations between sleep apnea severity in REM sleep and verbal memory

BACKGROUND

Obstructive sleep apnea (OSA) increases risk for cognitive decline and Alzheimer's disease (AD). While the underlying mechanisms remain unclear, hypoxemia during OSA has been implicated in cognitive impairment. OSA during rapid eye movement (REM) sleep is usually more severe than in non-rapid eye movement (NREM) sleep, but the relative effect of oxyhemoglobin desaturation during REM versus NREM sleep on memory is not completely characterized. Here, we examined the impact of OSA, as well as the moderating effects of AD risk factors, on verbal memory in a sample of middle-aged and older adults with heightened AD risk.

METHODS

Eighty-one adults (mean age:61.7 ± 6.0 years, 62% females, 32% apolipoprotein E ε4 allele (APOE4) carriers, and 70% with parental history of AD) underwent clinical polysomnography including assessment of OSA. OSA features were derived in total, NREM, and REM sleep. REM-NREM ratios of OSA features were also calculated. Verbal memory was assessed with the Rey Auditory Verbal Learning Test (RAVLT). Multiple regression models evaluated the relationships between OSA features and RAVLT scores while adjusting for sex, age, time between assessments, education years, body mass index (BMI), and APOE4 status or parental history of AD. The significant main effects of OSA features on RAVLT performance and the moderating effects of AD risk factors (i.e., sex, age, APOE4 status, and parental history of AD) were examined.

RESULTS

Apnea-hypopnea index (AHI), respiratory disturbance index (RDI), and oxyhemoglobin desaturation index (ODI) during REM sleep were negatively associated with RAVLT total learning and long-delay recall. Further, greater REM-NREM ratios of AHI, RDI, and ODI (i.e., more events in REM than NREM) were related to worse total learning and recall. We found specifically that the negative association between REM ODI and total learning was driven by adults 60 + years old. In addition, the negative relationships between REM-NREM ODI ratio and total learning, and REM-NREM RDI ratio and long-delay recall were driven by APOE4 carriers.

CONCLUSION

Greater OSA severity, particularly during REM sleep, negatively affects verbal memory, especially for people with greater AD risk. These findings underscore the potential importance of proactive screening and treatment of REM OSA even if overall AHI appears low.

Retuning of hippocampal representations during sleep

Hippocampal representations that underlie spatial memory undergo continuous refinement following formation1. Here, to track the spatial tuning of neurons dynamically during offline states, we used a new Bayesian learning approach based on the spike-triggered average decoded position in ensemble recordings from freely moving rats. Measuring these tunings, we found spatial representations within hippocampal sharp-wave ripples that were stable for hours during sleep and were strongly aligned with place fields initially observed during maze exploration. These representations were explained by a combination of factors that included preconfigured structure before maze exposure and representations that emerged during θ-oscillations and awake sharp-wave ripples while on the maze, revealing the contribution of these events in forming ensembles. Strikingly, the ripple representations during sleep predicted the future place fields of neurons during re-exposure to the maze, even when those fields deviated from previous place preferences. By contrast, we observed tunings with poor alignment to maze place fields during sleep and rest before maze exposure and in the later stages of sleep. In sum, the new decoding approach allowed us to infer and characterize the stability and retuning of place fields during offline periods, revealing the rapid emergence of representations following new exploration and the role of sleep in the representational dynamics of the hippocampus.

Mystery of the memory engram: History, current knowledge, and unanswered questions

The quest to understand the memory engram has intrigued humans for centuries. Recent technological advances, including genetic labelling, imaging, optogenetic and chemogenetic techniques, have propelled the field of memory research forward. These tools have enabled researchers to create and erase memory components. While these innovative techniques have yielded invaluable insights, they often focus on specific elements of the memory trace. Genetic labelling may rely on a particular immediate early gene as a marker of activity, optogenetics may activate or inhibit one specific type of neuron, and imaging may capture activity snapshots in a given brain region at specific times. Yet, memories are multifaceted, involving diverse arrays of neuronal subpopulations, circuits, and regions that work in concert to create, store, and retrieve information. Consideration of contributions of both excitatory and inhibitory neurons, micro and macro circuits across brain regions, the dynamic nature of active ensembles, and representational drift is crucial for a comprehensive understanding of the complex nature of memory.

Hippocampal replay sequence governed by spontaneous brain-wide dynamics

Neurons in the hippocampus exhibit spontaneous spiking activity during rest that appears to recapitulate previously experienced events. While this replay activity is frequently linked to memory consolidation and learning, the underlying mechanisms are not well understood. Recent large-scale neural recordings in mice have demonstrated that resting-state spontaneous activity is expressed as quasi-periodic cascades of spiking activity that pervade the forebrain, with each cascade engaging a high proportion of recorded neurons. Hippocampal ripples are known to be coordinated with cortical dynamics; however, less is known about the occurrence of replay activity relative to other brain-wide spontaneous events. Here we analyzed responses across the mouse brain to multiple viewings of natural movies, as well as subsequent patterns of neural activity during rest. We found that hippocampal neurons showed time-selectivity, with individual neurons responding consistently during particular moments of the movie. During rest, the population of time-selective hippocampal neurons showed both forward and time-reversed replay activity that matched the sequence observed in the movie. Importantly, these replay events were strongly time-locked to brain-wide spiking cascades, with forward and time-reversed replay activity associated with distinct cascade types. Thus, intrinsic hippocampal replay activity is temporally structured according to large-scale spontaneous physiology affecting areas throughout the forebrain. These findings shed light on the coordination between hippocampal and cortical circuits thought to be critical for memory consolidation.

Caffeine-induced protein kinase A activation restores cognitive deficits induced by sleep deprivation by regulating O-GlcNAc cycling in adult zebrafish

Sleep deprivation (SD) is widely acknowledged as a significant risk factor for cognitive impairment. In this study, intraperitoneal caffeine administration significantly ameliorated the learning and memory (L/M) deficits induced by SD and reduced aggressive behaviors in adult zebrafish. SD led to a reduction in protein kinase A (PKA) phosphorylation, phosphorylated-cAMP response element-binding protein (p-CREB), and c-Fos expression in zebrafish brain. Notably, these alterations were effectively reversed by caffeine. In addition, caffeine mitigated neuroinflammation induced by SD, as evident from suppression of the SD-mediated increase in glial fibrillary acidic protein (GFAP) and nuclear factor-κB (NF-κB) activation. Caffeine restored normal O-GlcNAcylation and O-GlcNAc transferase (OGT) levels while reversing the increased expression of O-GlcNAcase (OGA) in zebrafish brain after SD. Intriguingly, rolipram, a selective phosphodiesterase 4 (PDE4) inhibitor, effectively mitigated cognitive deficits, restored p-CREB and c-Fos levels, and attenuated the increase in GFAP in brain induced by SD. In addition, rolipram reversed the decrease in O-GlcNAcylation and OGT expression as well as elevation of OGA expression following SD. Treatment with H89, a PKA inhibitor, significantly impaired the L/M functions of zebrafish compared with the control group, inducing a decrease in O-GlcNAcylation and OGT expression and, conversely, an increase in OGA expression. The H89-induced changes in O-GlcNAc cycling and L/M dysfunction were effectively reversed by glucosamine treatment. H89 suppressed, whereas caffeine and rolipram promoted O-GlcNAc cycling in Neuro2a cells. Our collective findings underscore the interplay between PKA signaling and O-GlcNAc cycling in the regulation of cognitive function in the brain, offering potential therapeutic targets for cognitive deficits associated with SD.NEW & NOTEWORTHY Our observation highlights the intricate interplay between cAMP/PKA signaling and O-GlcNAc cycling, unveiling a novel mechanism that potentially governs the regulation of learning and memory functions. The dynamic interplay between these two pathways provides a novel and nuanced perspective on the molecular foundation of learning and memory regulation. These insights open avenues for the development of targeted interventions to treat conditions that impact cognitive function, including SD.

Auditory stimulation during REM sleep modulates REM electrophysiology and cognitive performance

REM sleep is critical for memory, emotion, and cognition. Manipulating brain activity during REM could improve our understanding of its function and benefits. Earlier studies have suggested that auditory stimulation in REM might modulate REM time and reduce rapid eye movement density. Building on this, we studied the cognitive effects and electroencephalographic responses related to such stimulation. We used acoustic stimulation locked to eye movements during REM and compared two overnight conditions (stimulation and no-stimulation). We evaluated the impact of this stimulation on REM sleep duration and electrophysiology, as well as two REM-sensitive memory tasks: visual discrimination and mirror tracing. Our results show that this auditory stimulation in REM decreases the rapid eye movements that characterize REM sleep and improves performance on the visual task but is detrimental to the mirror tracing task. We also observed increased beta-band activity and decreased theta-band activity following stimulation. Interestingly, these spectral changes were associated with changes in behavioural performance. These results show that acoustic stimulation can modulate REM sleep and suggest that different memory processes underpin its divergent impacts on cognitive performance.

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.

Volitional activation of remote place representations with a hippocampal brain-machine interface

The hippocampus is critical for recollecting and imagining experiences. This is believed to involve voluntarily drawing from hippocampal memory representations of people, events, and places, including maplike representations of familiar environments. However, whether representations in such "cognitive maps" can be volitionally accessed is unknown. We developed a brain-machine interface to test whether rats can do so by controlling their hippocampal activity in a flexible, goal-directed, and model-based manner. We found that rats can efficiently navigate or direct objects to arbitrary goal locations within a virtual reality arena solely by activating and sustaining appropriate hippocampal representations of remote places. This provides insight into the mechanisms underlying episodic memory recall, mental simulation and planning, and imagination and opens up possibilities for high-level neural prosthetics that use hippocampal representations.

Rapid eye movements associated with REM sleep is involved in consolidation of visuospatial learning in rats

Rapid eye movement (REM) sleep plays a significant role in visuospatial learning and memory consolidation; however, its mechanism of action is unknown. Rapid eye movements (REMs), a characteristic active feature of REM sleep, is a potential correlate of neural processing for visual memory consolidation. The superior colliculus (SC) plays a central role in oculomotor control and spatial localization of objects in the visual field. We proposed that local reversible inactivation of the SC during post-learning sessions might interfere with REMs and negatively impact REM sleep associated consolidation of the visuospatial learnt task. Under gaseous anesthesia, bilateral cannulae aiming SC and electrodes for recording electrophysiological signals to classify sleep-waking were implanted. Following standard protocol, all rats were subjected to Morris water maze (MWM) training for 5 consecutive days followed by probe trial. After MWM training, on all except the probe test days, the rat SC were bilaterally infused with either vehicle (control, Group 1), Lidocaine hydrochloride a local anesthetic (Lox 2%, Group 2), or muscimol (Mus, GABA agonist, Group 3) and sleep-wakefulness recorded after day 1, 4, and post-probe learning sessions. Post-learning, compared to vehicle, Mus treated group significantly decreased REMs, phasic REM sleep, percent time spent in REM sleep and REM sleep frequency/hr. Also, during probe test, the escape latency was significantly increased, and the percentage time spent in the platform quadrant were significantly decreased in both, Mus and Lox 2% treated rats, while the number of platform location crossings was decreased in Mus treated group. The results showed that Lox 2% and Mus into SC reduced consolidation of visuospatial learning. The findings support our contention that SC mediated activation of REMs exerts a positive influence in processing and consolidation of visual learning during REM sleep. The findings explain the role of REMs during REM sleep in visual memory consolidation.

The role of the hippocampus in the consolidation of emotional memories during sleep

Episodic memory relies on the hippocampus, a heterogeneous brain region with distinct functions. Spatial representations in the dorsal hippocampus (dHPC) are crucial for contextual memory, while the ventral hippocampus (vHPC) is more involved in emotional processing. Here, we review the literature in rodents highlighting the anatomical and functional properties of the hippocampus along its dorsoventral axis that underlie its role in contextual and emotional memory encoding, consolidation, and retrieval. We propose that the coordination between the dorsal and vHPC through theta oscillations during rapid eye movement (REM) sleep, and through sharp-wave ripples during non-REM (NREM) sleep, might facilitate the transfer of contextual information for integration with valence-related processing in other structures of the network. Further investigation into the physiology of the vHPC and its connections with other brain areas is needed to deepen the current understanding of emotional memory consolidation during sleep.

Aversive experience drives offline ensemble reactivation to link memories across days

Memories are encoded in neural ensembles during learning and stabilized by post-learning reactivation. Integrating recent experiences into existing memories ensures that memories contain the most recently available information, but how the brain accomplishes this critical process remains unknown. Here we show that in mice, a strong aversive experience drives the offline ensemble reactivation of not only the recent aversive memory but also a neutral memory formed two days prior, linking the fear from the recent aversive memory to the previous neutral memory. We find that fear specifically links retrospectively, but not prospectively, to neutral memories across days. Consistent with prior studies, we find reactivation of the recent aversive memory ensemble during the offline period following learning. However, a strong aversive experience also increases co-reactivation of the aversive and neutral memory ensembles during the offline period. Finally, the expression of fear in the neutral context is associated with reactivation of the shared ensemble between the aversive and neutral memories. Taken together, these results demonstrate that strong aversive experience can drive retrospective memory-linking through the offline co-reactivation of recent memory ensembles with memory ensembles formed days prior, providing a neural mechanism by which memories can be integrated across days.

Eye movements during phasic versus tonic rapid eye movement sleep are biomarkers of dissociable electroencephalogram processes for the consolidation of novel problem-solving skills

The hallmark eye movement (EM) bursts that occur during rapid eye movement (REM) sleep are markers of consolidation for procedural memory involving novel cognitive strategies and problem-solving skills. Examination of the brain activity associated with EMs during REM sleep might elucidate the processes involved in memory consolidation, and may uncover the functional significance of REM sleep and EMs themselves. Participants performed a REM-dependent, novel procedural problem-solving task (i.e. the Tower of Hanoi; ToH) before and after intervals of either overnight sleep (n = 20) or a daytime 8-hour wake period (n = 20). In addition, event-related spectral perturbation of the electroencephalogram (EEG) time-locked to EMs occurring either in bursts (i.e. phasic REM), or in isolation (i.e. tonic REM), were compared to sleep on a non-learning control night. ToH improvement was greater following sleep compared to wakefulness. During sleep, prefrontal theta (~2-8 Hz) and central-parietal-occipital sensorimotor rhythm (SMR) activity (~8-16 Hz) time-locked to EMs, were greater on the ToH night versus control night, and during phasic REM sleep, were both positively correlated with overnight memory improvements. Furthermore, SMR power during tonic REM increased significantly from the control night to ToH night, but was relatively stable from night to night during phasic REM. These results suggest that EMs are markers of learning-related increases in theta and SMR during phasic and tonic REM sleep. Phasic and tonic REM sleep may be functionally distinct in terms of their contribution to procedural memory consolidation.

Extracting electromyographic signals from multi-channel LFPs using independent component analysis without direct muscular recording

Electromyography (EMG) has been commonly used for the precise identification of animal behavior. However, it is often not recorded together with in vivo electrophysiology due to the need for additional surgeries and setups and the high risk of mechanical wire disconnection. While independent component analysis (ICA) has been used to reduce noise from field potential data, there has been no attempt to proactively use the removed "noise," of which EMG signals are thought to be one of the major sources. Here, we demonstrate that EMG signals can be reconstructed without direct EMG recording using the "noise" ICA component from local field potentials. The extracted component is highly correlated with directly measured EMG, termed IC-EMG. IC-EMG is useful for measuring an animal's sleep/wake, freezing response, and non-rapid eye movement (NREM)/REM sleep states consistently with actual EMG. Our method has advantages in precise and long-term behavioral measurement in wide-ranging in vivo electrophysiology experiments.

Targeted memory reactivation in human REM sleep elicits detectable reactivation

It is now well established that memories can reactivate during non-rapid eye movement (non-REM) sleep, but the question of whether equivalent reactivation can be detected in rapid eye movement (REM) sleep is hotly debated. To examine this, we used a technique called targeted memory reactivation (TMR) in which sounds are paired with learned material in wake, and then re-presented in subsequent sleep, in this case REM, to trigger reactivation. We then used machine learning classifiers to identify reactivation of task-related motor imagery from wake in REM sleep. Interestingly, the strength of measured reactivation positively predicted overnight performance improvement. These findings provide the first evidence for memory reactivation in human REM sleep after TMR that is directly related to brain activity during wakeful task performance.

Identifying properties of pattern completion neurons in a computational model of the visual cortex

Neural ensembles are found throughout the brain and are believed to underlie diverse cognitive functions including memory and perception. Methods to activate ensembles precisely, reliably, and quickly are needed to further study the ensembles' role in cognitive processes. Previous work has found that ensembles in layer 2/3 of the visual cortex (V1) exhibited pattern completion properties: ensembles containing tens of neurons were activated by stimulation of just two neurons. However, methods that identify pattern completion neurons are underdeveloped. In this study, we optimized the selection of pattern completion neurons in simulated ensembles. We developed a computational model that replicated the connectivity patterns and electrophysiological properties of layer 2/3 of mouse V1. We identified ensembles of excitatory model neurons using K-means clustering. We then stimulated pairs of neurons in identified ensembles while tracking the activity of the entire ensemble. Our analysis of ensemble activity quantified a neuron pair's power to activate an ensemble using a novel metric called pattern completion capability (PCC) based on the mean pre-stimulation voltage across the ensemble. We found that PCC was directly correlated with multiple graph theory parameters, such as degree and closeness centrality. To improve selection of pattern completion neurons in vivo, we computed a novel latency metric that was correlated with PCC and could potentially be estimated from modern physiological recordings. Lastly, we found that stimulation of five neurons could reliably activate ensembles. These findings can help researchers identify pattern completion neurons to stimulate in vivo during behavioral studies to control ensemble activation.

Rule Abstraction Is Facilitated by Auditory Cuing in REM Sleep

Sleep facilitates abstraction, but the exact mechanisms underpinning this are unknown. Here, we aimed to determine whether triggering reactivation in sleep could facilitate this process. We paired abstraction problems with sounds, then replayed these during either slow-wave sleep (SWS) or rapid eye movement (REM) sleep to trigger memory reactivation in 27 human participants (19 female). This revealed performance improvements on abstraction problems that were cued in REM, but not problems cued in SWS. Interestingly, the cue-related improvement was not significant until a follow-up retest 1 week after the manipulation, suggesting that REM may initiate a sequence of plasticity events that requires more time to be implemented. Furthermore, memory-linked trigger sounds evoked distinct neural responses in REM, but not SWS. Overall, our findings suggest that targeted memory reactivation in REM can facilitate visual rule abstraction, although this effect takes time to unfold.SIGNIFICANCE STATEMENT The ability to abstract rules from a corpus of experiences is a building block of human reasoning. Sleep is known to facilitate rule abstraction, but it remains unclear whether we can manipulate this process actively and which stage of sleep is most important. Targeted memory reactivation (TMR) is a technique that uses re-exposure to learning-related sensory cues during sleep to enhance memory consolidation. Here, we show that TMR, when applied during REM sleep, can facilitate the complex recombining of information needed for rule abstraction. Furthermore, we show that this qualitative REM-related benefit emerges over the course of a week after learning, suggesting that memory integration may require a slower form of plasticity.

On temporal scale-free non-periodic stimulation and its mechanisms as an infinite improbability drive of the brain's functional connectogram

Rationalized development of electrical stimulation (ES) therapy is of paramount importance. Not only it will foster new techniques and technologies with increased levels of safety, efficacy, and efficiency, but it will also facilitate the translation from basic research to clinical practice. For such endeavor, design of new technologies must dialogue with state-of-the-art neuroscientific knowledge. By its turn, neuroscience is transitioning-a movement started a couple of decades earlier-into adopting a new conceptual framework for brain architecture, in which time and thus temporal patterns plays a central role in the neuronal representation of sampled data from the world. This article discusses how neuroscience has evolved to understand the importance of brain rhythms in the overall functional architecture of the nervous system and, consequently, that neuromodulation research should embrace this new conceptual framework. Based on such support, we revisit the literature on standard (fixed-frequency pulsatile stimuli) and mostly non-standard patterns of ES to put forward our own rationale on how temporally complex stimulation schemes may impact neuromodulation strategies. We then proceed to present a low frequency, on average (thus low energy), scale-free temporally randomized ES pattern for the treatment of experimental epilepsy, devised by our group and termed NPS (Non-periodic Stimulation). The approach has been shown to have robust anticonvulsant effects in different animal models of acute and chronic seizures (displaying dysfunctional hyperexcitable tissue), while also preserving neural function. In our understanding, accumulated mechanistic evidence suggests such a beneficial mechanism of action may be due to the natural-like characteristic of a scale-free temporal pattern that may robustly compete with aberrant epileptiform activity for the recruitment of neural circuits. Delivering temporally patterned or random stimuli within specific phases of the underlying oscillations (i.e., those involved in the communication within and across brain regions) could both potentiate and disrupt the formation of neuronal assemblies with random probability. The usage of infinite improbability drive here is obviously a reference to the "The Hitchhiker's Guide to the Galaxy" comedy science fiction classic, written by Douglas Adams. The parallel is that dynamically driving brain functional connectogram, through neuromodulation, in a manner that would not favor any specific neuronal assembly and/or circuit, could re-stabilize a system that is transitioning to fall under the control of a single attractor. We conclude by discussing future avenues of investigation and their potentially disruptive impact on neurotechnology, with a particular interest in NPS implications in neural plasticity, motor rehabilitation, and its potential for clinical translation.

Sleep-A brain-state serving systems memory consolidation

Although long-term memory consolidation is supported by sleep, it is unclear how it differs from that during wakefulness. Our review, focusing on recent advances in the field, identifies the repeated replay of neuronal firing patterns as a basic mechanism triggering consolidation during sleep and wakefulness. During sleep, memory replay occurs during slow-wave sleep (SWS) in hippocampal assemblies together with ripples, thalamic spindles, neocortical slow oscillations, and noradrenergic activity. Here, hippocampal replay likely favors the transformation of hippocampus-dependent episodic memory into schema-like neocortical memory. REM sleep following SWS might balance local synaptic rescaling accompanying memory transformation with a sleep-dependent homeostatic process of global synaptic renormalization. Sleep-dependent memory transformation is intensified during early development despite the immaturity of the hippocampus. Overall, beyond its greater efficacy, sleep consolidation differs from wake consolidation mainly in that it is supported, rather than impaired, by spontaneous hippocampal replay activity possibly gating memory formation in neocortex.

Differential replay of reward and punishment paths predicts approach and avoidance

Neural replay is implicated in planning, where states relevant to a task goal are rapidly reactivated in sequence. It remains unclear whether, during planning, replay relates to an actual prospective choice. Here, using magnetoencephalography (MEG), we studied replay in human participants while they planned to either approach or avoid an uncertain environment containing paths leading to reward or punishment. We find evidence for forward sequential replay during planning, with rapid state-to-state transitions from 20 to 90 ms. Replay of rewarding paths was boosted, relative to aversive paths, before a decision to avoid and attenuated before a decision to approach. A trial-by-trial bias toward replaying prospective punishing paths predicted irrational decisions to approach riskier environments, an effect more pronounced in participants with higher trait anxiety. The findings indicate a coupling of replay with planned behavior, where replay prioritizes an online representation of a worst-case scenario for approaching or avoiding.

Memory reactivations during sleep: a neural basis of dream experiences?

Newly encoded memory traces are spontaneously reactivated during sleep. Since their discovery in the 1990s, these memory reactivations have been discussed as a potential neural basis for dream experiences. New results from animal and human research, as well as from the rapidly growing field of sleep and dream engineering, provide essential insights into this question, and reveal both strong parallels and disparities between the two phenomena. We suggest that, although memory reactivations may contribute to subjective experiences across different states of consciousness, they are not likely to be the primary neural basis of dreaming. We identify important limitations in current research paradigms and suggest novel strategies to address this question empirically.

Topographic-dynamic reorganisation model of dreams (TRoD) - A spatiotemporal approach

Dreams are one of the most bizarre and least understood states of consciousness. Bridging the gap between brain and phenomenology of (un)conscious experience, we propose the Topographic-dynamic Re-organization model of Dreams (TRoD). Topographically, dreams are characterized by a shift towards increased activity and connectivity in the default-mode network (DMN) while they are reduced in the central executive network, including the dorsolateral prefrontal cortex (except in lucid dreaming). This topographic re-organization is accompanied by dynamic changes; a shift towards slower frequencies and longer timescales. This puts dreams dynamically in an intermediate position between awake state and NREM 2/SWS sleep. TRoD proposes that the shift towards DMN and slower frequencies leads to an abnormal spatiotemporal framing of input processing including both internally- and externally-generated inputs (from body and environment). In dreams, a shift away from temporal segregation to temporal integration of inputs results in the often bizarre and highly self-centric mental contents as well as hallucinatory-like states. We conclude that topography and temporal dynamics are core features of the TroD, which may provide the connection of neural and mental activity, e.g., brain and experience during dreams as their "common currency".

How our understanding of memory replay evolves

Memory reactivations and replay, widely reported in the hippocampus and cortex across species, have been implicated in memory consolidation, planning, and spatial and skill learning. Technological advances in electrophysiology, calcium imaging, and human neuroimaging techniques have enabled neuroscientists to measure large-scale neural activity with increasing spatiotemporal resolution and have provided opportunities for developing robust analytic methods to identify memory replay. In this article, we first review a large body of historically important and representative memory replay studies from the animal and human literature. We then discuss our current understanding of memory replay functions in learning, planning, and memory consolidation and further discuss the progress in computational modeling that has contributed to these improvements. Next, we review past and present analytic methods for replay analyses and discuss their limitations and challenges. Finally, looking ahead, we discuss some promising analytic methods for detecting nonstereotypical, behaviorally nondecodable structures from large-scale neural recordings. We argue that seamless integration of multisite recordings, real-time replay decoding, and closed-loop manipulation experiments will be essential for delineating the role of memory replay in a wide range of cognitive and motor functions.

Replay and compositional computation

Replay in the brain has been viewed as rehearsal or, more recently, as sampling from a transition model. Here, we propose a new hypothesis: that replay is able to implement a form of compositional computation where entities are assembled into relationally bound structures to derive qualitatively new knowledge. This idea builds on recent advances in neuroscience, which indicate that the hippocampus flexibly binds objects to generalizable roles and that replay strings these role-bound objects into compound statements. We suggest experiments to test our hypothesis, and we end by noting the implications for AI systems which lack the human ability to radically generalize past experience to solve new problems.

Focal epilepsy impacts rapid eye movement sleep microstructure

STUDY OBJECTIVES

Whereas there is plenty of evidence on the influence of epileptic activity on non-rapid eye movement (NREM) sleep macro- and micro-structure, data on the impact of epilepsy on rapid eye movement (REM) sleep remains sparse. Using high-density electroencephalography (HD-EEG), we assessed global and focal disturbances of sawtooth waves (STW) as cortically generated sleep oscillations of REM sleep in patients with focal epilepsy.

METHODS

Twenty-two patients with drug-resistant focal epilepsy (13 females; mean age, 32.6 ± 10.7 years; 12 temporal lobe epilepsy) and 12 healthy controls (3 females; 24.0 ± 3.2 years) underwent combined overnight HD-EEG and polysomnography. STW rate, duration, frequency, power, spatial extent, IED rates and sleep homeostatic properties were analyzed.

RESULTS

STW rate and duration were reduced in patients with focal epilepsy compared to healthy controls (rate: 0.64/min ± 0.46 vs. 1.12/min ± 0.41, p = .005, d = -0.98; duration: 3.60 s ± 0.76 vs. 4.57 ± 1.00, p = .003, d = -1.01). Not surprisingly given the fronto-central maximum of STW, the reductions were driven by extratemporal lobe epilepsy patients (rate: 0.45/min ± 0.31 vs. 1.12/min ± 0.41, p = .0004, d = -1.35; duration: 3.49 s ± 0.92 vs. 4.57 ± 1.00, p = .017, d = -0.99) and were more pronounced in the first vs. the last sleep cycle (rate first cycle patients vs. controls: 0.60/min ± 0.49 vs. 1.10/min ± 0.55, p = .016, d = -0.90, rate last cycle patients vs. controls: 0.67/min ± 0.51 vs. 0.99/min ± 0.49, p = .11, d = -0.62; duration first cycle patients vs. controls: 3.60s ± 0.76 vs. 4.57 ± 1.00, p = .003, d = -1.01, duration last cycle patients vs. controls: 3.66s ± 0.84 vs. 4.51 ± 1.26, p = .039, d = -0.80). There was no regional decrease of STWs in the region with the epileptic focus vs. the contralateral side (all p > .05).

CONCLUSION

Patients with focal epilepsy and in particular extratemporal lobe epilepsy show a global reduction of STW activity in REM sleep. This may suggest that epilepsy impacts cortically generated sleep oscillations even in REM sleep when epileptic activity is low.

Neural ensembles in navigation: From single cells to population codes

The brain can represent behaviorally relevant information through the firing of individual neurons as well as the coordinated firing of ensembles of neurons. Neurons in the hippocampus and associated cortical regions participate in a variety of types of ensembles to support navigation. These ensemble types include single cell codes, population codes, time-compressed sequences, behavioral sequences, and engrams. We present the physiological basis and behavioral relevance of ensemble firing. We discuss how these traditional definitions of ensembles can constrain or expand potential analyses due to the underlying assumptions and abstractions made. We highlight how coding can change at the ensemble level while underlying single cell codes remain intact. Finally, we present how ensemble definitions could be broadened to better understand the full complexity of the brain.

Modeling the function of episodic memory in spatial learning

Episodic memory has been studied extensively in the past few decades, but so far little is understood about how it drives future behavior. Here we propose that episodic memory can facilitate learning in two fundamentally different modes: retrieval and replay, which is the reinstatement of hippocampal activity patterns during later sleep or awake quiescence. We study their properties by comparing three learning paradigms using computational modeling based on visually-driven reinforcement learning. Firstly, episodic memories are retrieved to learn from single experiences (one-shot learning); secondly, episodic memories are replayed to facilitate learning of statistical regularities (replay learning); and, thirdly, learning occurs online as experiences arise with no access to memories of past experiences (online learning). We found that episodic memory benefits spatial learning in a broad range of conditions, but the performance difference is meaningful only when the task is sufficiently complex and the number of learning trials is limited. Furthermore, the two modes of accessing episodic memory affect spatial learning differently. One-shot learning is typically faster than replay learning, but the latter may reach a better asymptotic performance. In the end, we also investigated the benefits of sequential replay and found that replaying stochastic sequences results in faster learning as compared to random replay when the number of replays is limited. Understanding how episodic memory drives future behavior is an important step toward elucidating the nature of episodic memory.

Offline neuronal activity and synaptic plasticity during sleep and memory consolidation

After initial formation during learning, memories are further processed in the brain during subsequent days for long-term consolidation, with sleep playing a key role in this process. Studies have shown that neuronal activity patterns during the awake period are repeated in the hippocampus during sleep, which may coordinate brain-wide reactivation leading to memory consolidation. Consistently, perturbation of this activity blocks the formation of long-term memory. This 'replay' of activity during sleep likely triggers plastic changes in synaptic transmission, a cellular substrate of memory, in multiple brain regions, which likely plays a critical role in long-term memory. Two forms of synaptic plasticity, potentiation and depression of synaptic transmission, are induced in parallel during sleep and is termed "offline synaptic plasticity", as opposed to the "online synaptic plasticity" that occurs immediately following a memory event.

Functional roles of REM sleep

Rapid eye movement (REM) sleep is an enigmatic and intriguing sleep state. REM sleep differs from non-REM sleep by its characteristic brain activity and from wakefulness by a reduced anti-gravity muscle tone. In addition to these key traits, diverse physiological phenomena appear across the whole body during REM sleep. However, it remains unclear whether these phenomena are the causes or the consequences of REM sleep. Experimental approaches using humans and animal models have gradually revealed the functional roles of REM sleep. Extensive efforts have been made to interpret the characteristic brain activity in the context of memory functions. Numerous physical and psychological functions of REM sleep have also been proposed. Moreover, REM sleep has been implicated in aspects of brain development. Here, we review the variety of functional roles of REM sleep, mainly as revealed by animal models. In addition, we discuss controversies regarding the functional roles of REM sleep.

Imagination as a fundamental function of the hippocampus

Imagination is a biological function that is vital to human experience and advanced cognition. Despite this importance, it remains unknown how imagination is realized in the brain. Substantial research focusing on the hippocampus, a brain structure traditionally linked to memory, indicates that firing patterns in spatially tuned neurons can represent previous and upcoming paths in space. This work has generally been interpreted under standard views that the hippocampus implements cognitive abilities primarily related to actual experience, whether in the past (e.g. recollection, consolidation), present (e.g. spatial mapping) or future (e.g. planning). However, relatively recent findings in rodents identify robust patterns of hippocampal firing corresponding to a variety of alternatives to actual experience, in many cases without overt reference to the past, present or future. Given these findings, and others on hippocampal contributions to human imagination, we suggest that a fundamental function of the hippocampus is to generate a wealth of hypothetical experiences and thoughts. Under this view, traditional accounts of hippocampal function in episodic memory and spatial navigation can be understood as particular applications of a more general system for imagination. This view also suggests that the hippocampus contributes to a wider range of cognitive abilities than previously thought. This article is part of the theme issue 'Thinking about possibilities: mechanisms, ontogeny, functions and phylogeny'.

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.

Disruption of hippocampal neuronal circuit function depends upon behavioral state in the APP/PS1 mouse model of Alzheimer's disease

The Alzheimer's disease-associated peptide amyloid-beta (Aβ) has been associated with neuronal hyperactivity under anesthesia, but clinical trials of anticonvulsants or neural system suppressors have, so far, failed to improve symptoms in AD. Using simultaneous hippocampal calcium imaging and electrophysiology in freely moving mice expressing human Aβ, here we show that Aβ aggregates perturbed neural systems in a state-dependent fashion, driving neuronal hyperactivity in exploratory behavior and slow wave sleep (SWS), yet suppressing activity in quiet wakefulness (QW) and REM sleep. In exploratory behavior and REM sleep, Aβ impaired hippocampal theta-gamma phase-amplitude coupling and altered neuronal synchronization with theta. In SWS, Aβ reduced cortical slow oscillation (SO) power, the coordination of hippocampal sharp wave-ripples with both the SO and thalamocortical spindles, and the coordination of calcium transients with the sharp wave-ripple. Physostigmine improved Aβ-associated hyperactivity in exploratory behavior and hypoactivity in QW and expanded the range of gamma that coupled with theta phase, but exacerbated hypoactivity in exploratory behavior. Together, these findings show that the effects of Aβ alone on hippocampal circuit function are profoundly state dependent and suggest a reformulation of therapeutic strategies aimed at Aβ induced hyperexcitability.

The Role of Intra-Amygdaloid Neurotensin and Dopamine Interaction in Spatial Learning and Memory

Neurotransmitter and neuromodulator neurotensin (NT) has been proved to facilitate spatial and passive avoidance learning after microinjected into the rat central nucleus of amygdala (CeA). These previous studies of our laboratory also revealed that neurotensin-1 receptor (NTS1) is involved in the mentioned actions of NT. Extensive literature confirms the interaction between neurotensinergic and dopaminergic systems, and our research group also suppose that the mesolimbic dopaminergic system (MLDS) is involved in the spatial learning and memory-facilitating effect of NT in the CeA. In the present work, NT and dopamine (DA) interaction has been examined in the Morris water maze and passive avoidance tests. Rats received 100 ng NT, 5 µg dopamine D2 receptor antagonist sulpiride in itself, sulpiride as a pretreatment before NT or vehicle solution into the CeA. NT microinjection significantly decreased target-finding latency in the Morris water maze test and significantly increased entrance latency in the passive avoidance test, as was expected based on our previous findings. The DA D2 receptor antagonist pretreatment was able to inhibit both effects of NT. The results confirm the facilitatory effect of NT on spatial learning and memory and let us conclude that these actions can be exerted via the DA D2 receptors.

Context-independent expression of spatial code in hippocampus

The hippocampus plays a crucial role in the formation and retrieval of spatial memory across mammals and episodic memory in humans. Episodic and spatial memories can be retrieved irrespective of the subject's awake behavioral state and independently of its actual spatial context. However, the nature of hippocampal network activity during such out-context retrieval has not been described so far. Theoretically, context-independent spatial memory retrieval suggests a shift of the hippocampal spatial representations from coding the current spatial context to coding the remembered environment. In this study we show in rats that the CA3 neuronal population can switch spontaneously across representations and transiently activate another stored familiar spatial pattern without direct external sensory cuing. This phenomenon qualitatively differs from the well-described sharp wave-related pattern reactivations during immobility. Here, it occurs under the theta oscillatory state during active exploration and reflects the preceding experience of sudden environmental change. The respective out-context coding spikes appeared later in the theta cycle than the in-context ones. Finally, the experience also induced the emergence of population vectors with a co-expression of both codes segregated into different phases of the theta cycle.

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.

Theta dominates cross-frequency coupling in hippocampal-medial entorhinal circuit during awake-behavior in rats

Hippocampal theta and gamma rhythms are hypothesized to play a role in the physiology of higher cognition. Prior research has reported that an offset in theta cycles between the entorhinal cortex, CA3, and CA1 regions promotes independence of population activity across the hippocampus. In line with this idea, it has recently been observed that CA1 pyramidal cells can establish and maintain coordinated place cell activity intrinsically, with minimal reliance on afferent input. Counter to these observations is the contemporary hypothesis that CA1 neuron activity is driven by a gamma oscillation arising from the medial entorhinal cortex (MEC) that relays information by providing precisely timed synchrony between MEC and CA1. Reinvestigating this in rats during appetitive track running, we found that theta is the dominant frequency of cross-frequency coupling between the MEC and hippocampus, with hippocampal gamma largely independent of entorhinal gamma.

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Theta, theta harmonic, and gamma power increase with running speed in the HPC and MEC

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Intra-regionally, theta-theta harmonic and theta-gamma coupling increases with speed

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Cross-regionally, theta is the dominant frequency of coupling between HPC and MEC

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Marginal gamma coupling can be explained by local gamma modulated by coherent theta

Continuous learning of spiking networks trained with local rules

Artificial neural networks (ANNs) experience catastrophic forgetting (CF) during sequential learning. In contrast, the brain can learn continuously without any signs of catastrophic forgetting. Spiking neural networks (SNNs) are the next generation of ANNs with many features borrowed from biological neural networks. Thus, SNNs potentially promise better resilience to CF. In this paper, we study the susceptibility of SNNs to CF and test several biologically inspired methods for mitigating catastrophic forgetting. SNNs are trained with biologically plausible local training rules based on spike-timing-dependent plasticity (STDP). Local training prohibits the direct use of CF prevention methods based on gradients of a global loss function. We developed and tested the method to determine the importance of synapses (weights) based on stochastic Langevin dynamics without the need for the gradients. Several other methods of catastrophic forgetting prevention adapted from analog neural networks were tested as well. The experiments were performed on freely available datasets in the SpykeTorch environment.

A Brain-Inspired Model of Hippocampal Spatial Cognition Based on a Memory-Replay Mechanism

Since the hippocampus plays an important role in memory and spatial cognition, the study of spatial computation models inspired by the hippocampus has attracted much attention. This study relies mainly on reward signals for learning environments and planning paths. As reward signals in a complex or large-scale environment attenuate sharply, the spatial cognition and path planning performance of such models will decrease clearly as a result. Aiming to solve this problem, we present a brain-inspired mechanism, a Memory-Replay Mechanism, that is inspired by the reactivation function of place cells in the hippocampus. We classify the path memory according to the reward information and find the overlapping place cells in different categories of path memory to segment and reconstruct the memory to form a “virtual path”, replaying the memory by associating the reward information. We conducted a series of navigation experiments in a simple environment called a Morris water maze (MWM) and in a complex environment, and we compared our model with a reinforcement learning model and other brain-inspired models. The experimental results show that under the same conditions, our model has a higher rate of environmental exploration and more stable signal transmission, and the average reward obtained under stable conditions was 14.12% higher than RL with random-experience replay. Our model also shows good performance in complex maze environments where signals are easily attenuated. Moreover, the performance of our model at bifurcations is consistent with neurophysiological studies.

Sequence learning, prediction, and replay in networks of spiking neurons

Sequence learning, prediction and replay have been proposed to constitute the universal computations performed by the neocortex. The Hierarchical Temporal Memory (HTM) algorithm realizes these forms of computation. It learns sequences in an unsupervised and continuous manner using local learning rules, permits a context specific prediction of future sequence elements, and generates mismatch signals in case the predictions are not met. While the HTM algorithm accounts for a number of biological features such as topographic receptive fields, nonlinear dendritic processing, and sparse connectivity, it is based on abstract discrete-time neuron and synapse dynamics, as well as on plasticity mechanisms that can only partly be related to known biological mechanisms. Here, we devise a continuous-time implementation of the temporal-memory (TM) component of the HTM algorithm, which is based on a recurrent network of spiking neurons with biophysically interpretable variables and parameters. The model learns high-order sequences by means of a structural Hebbian synaptic plasticity mechanism supplemented with a rate-based homeostatic control. In combination with nonlinear dendritic input integration and local inhibitory feedback, this type of plasticity leads to the dynamic self-organization of narrow sequence-specific subnetworks. These subnetworks provide the substrate for a faithful propagation of sparse, synchronous activity, and, thereby, for a robust, context specific prediction of future sequence elements as well as for the autonomous replay of previously learned sequences. By strengthening the link to biology, our implementation facilitates the evaluation of the TM hypothesis based on experimentally accessible quantities. The continuous-time implementation of the TM algorithm permits, in particular, an investigation of the role of sequence timing for sequence learning, prediction and replay. We demonstrate this aspect by studying the effect of the sequence speed on the sequence learning performance and on the speed of autonomous sequence replay.

Essentially all data processed by mammals and many other living organisms is sequential. This holds true for all types of sensory input data as well as motor output activity. Being able to form memories of such sequential data, to predict future sequence elements, and to replay learned sequences is a necessary prerequisite for survival. It has been hypothesized that sequence learning, prediction and replay constitute the fundamental computations performed by the neocortex. The Hierarchical Temporal Memory (HTM) constitutes an abstract powerful algorithm implementing this form of computation and has been proposed to serve as a model of neocortical processing. In this study, we are reformulating this algorithm in terms of known biological ingredients and mechanisms to foster the verifiability of the HTM hypothesis based on electrophysiological and behavioral data. The proposed model learns continuously in an unsupervised manner by biologically plausible, local plasticity mechanisms, and successfully predicts and replays complex sequences. Apart from establishing contact to biology, the study sheds light on the mechanisms determining at what speed we can process sequences and provides an explanation of fast sequence replay observed in the hippocampus and in the neocortex.

Roles of sleep-related cardiovascular autonomic functions in voluntary-exercise-induced alleviation of hypertension in spontaneously hypertensive rats

Autonomic dysfunction and sleep problems are closely associated with hypertension and predict cardiovascular morbidity and mortality. Animal studies and clinical observations have identified exercise as an important factor in preventing and treating hypertension. However, the roles of autonomic function and sleep in the antihypertensive mechanisms of exercise are still not fully understood. This study aimed to clarify the physiological mechanisms associated with autonomic function and sleep through wheel exercise. Male spontaneously hypertensive rats (SHRs) were grouped into a wheel-exercised group and a sedentary group (controls). Electroencephalogram, electromyogram, electrocardiogram, and mean arterial pressure (MAP) were recorded simultaneously for 24 h once a week over 11 weeks. Wheel exercise was initiated in the SHRs at 12 weeks old and continued for another eight weeks. A significant suppression in the age-related elevation of MAP was noted in the SHRs undergoing wheel exercise. The reduction in MAP was correlated with increased parasympathetic activity and baroreflex sensitivity and decreased sympathetic activity, mainly during quiet sleep. Exercise increased the paradoxical sleep time and theta power (associated with cognitive function) but not the delta power (an indicator of sleep depth) or the attenuation of circadian rhythm flattening (characterized by increased wakefulness and less sleep during the light period and the opposite during the dark period). Furthermore, the exercise-induced changes in autonomic function occurred before those in sleep patterns, which were dependent on each other. In conclusion, wheel exercise can modulate sleep-related cardiovascular dysfunction and the flattening of circadian rhythm, preventing the progression of hypertension, which reduces the incidence of cardiovascular diseases.

Examining the relationship between working memory consolidation and long-term consolidation

An emerging area of research is focused on the relationship between working memory and long-term memory and the likely overlap between these processes. Of particular interest is how some information first maintained in working memory is retained for longer periods and eventually preserved in long-term memory. The process of stabilizing transient memory representations for lasting retention is referred to as consolidation in both the working memory and long-term memory literature, although these have historically been viewed as independent constructs. The present review aims to investigate the relationship between working memory consolidation and long-term memory consolidation, which both have rich, but distinct, histories. This review provides an overview of the proposed models and neural mechanisms of both types of consolidation, as well as clinical findings related to consolidation and potential approaches for the manipulation of consolidation. Finally, two hypotheses are proposed to explain the relationship between working memory consolidation and long-term memory consolidation.

Neuronal Ensembles Organize Activity to Generate Contextual Memory

Contextual learning is a critical component of episodic memory and important for living in any environment. Context can be described as the attributes of a location that are not the location itself. This includes a variety of non-spatial information that can be derived from sensory systems (sounds, smells, lighting, etc.) and internal state. In this review, we first address the behavioral underpinnings of contextual memory and the development of context memory theory, with a particular focus on the contextual fear conditioning paradigm as a means of assessing contextual learning and the underlying processes contributing to it. We then present the various neural centers that play roles in contextual learning. We continue with a discussion of the current knowledge of the neural circuitry and physiological processes that underlie contextual representations in the Entorhinal cortex-Hippocampal (EC-HPC) circuit, as the most well studied contributor to contextual memory, focusing on the role of ensemble activity as a representation of context with a description of remapping, and pattern separation and completion in the processing of contextual information. We then discuss other critical regions involved in contextual memory formation and retrieval. We finally consider the engram assembly as an indicator of stored contextual memories and discuss its potential contribution to contextual memory.

Age-Related Unstructured Spike Patterns and Molecular Localization in Drosophila Circadian Neurons

Aging decreases sleep quality by disrupting the molecular machinery that regulates the circadian rhythm. However, we do not fully understand the mechanism that underlies this process. In Drosophila, sleep quality is regulated by precisely timed patterns of spontaneous firing activity in posterior DN1 (DN1p) circadian clock neurons. How aging affects the physiological function of DN1p neurons is unknown. In this study, we found that aging altered functional parameters related to neural excitability and disrupted patterned spike sequences in DN1p neurons during nighttime. We also characterized age-associated changes in intrinsic membrane properties related to spike frequency adaptations and synaptic properties, which may account for the unstructured spike patterns in aged DN1p neurons. Because Slowpoke binding protein (SLOB) and the Na⁺/K⁺ ATPase β subunit (NaKβ) regulate clock-dependent spiking patterns in circadian networks, we compared the subcellular organization of these factors between young and aged DN1p neurons. Young DN1p neurons showed circadian cycling of HA-tagged SLOB and myc-tagged NaKβ targeting the plasma membrane, whereas aged DN1p neurons showed significantly disrupted subcellular localization patterns of both factors. The distribution of SLOB and NaKβ signals also showed greater variability in young vs. aged DN1p neurons, suggesting aging leads to a loss of actively formed heterogeneity for these factors. These findings showed that aging disrupts precisely structured molecular patterns that regulate structured neural activity in the circadian network, leading to age-associated declines in sleep quality. Thus, it is possible to speculate that a recovery of unstructured neural activity in aging clock neurons could help to rescue age-related poor sleep quality.

Decoding cognition from spontaneous neural activity

In human neuroscience, studies of cognition are rarely grounded in non-task-evoked, 'spontaneous' neural activity. Indeed, studies of spontaneous activity tend to focus predominantly on intrinsic neural patterns (for example, resting-state networks). Taking a 'representation-rich' approach bridges the gap between cognition and resting-state communities: this approach relies on decoding task-related representations from spontaneous neural activity, allowing quantification of the representational content and rich dynamics of such activity. For example, if we know the neural representation of an episodic memory, we can decode its subsequent replay during rest. We argue that such an approach advances cognitive research beyond a focus on immediate task demand and provides insight into the functional relevance of the intrinsic neural pattern (for example, the default mode network). This in turn enables a greater integration between human and animal neuroscience, facilitating experimental testing of theoretical accounts of intrinsic activity, and opening new avenues of research in psychiatry.

Spatial goal coding in the hippocampal formation

The mammalian hippocampal formation contains several distinct populations of neurons involved in representing self-position and orientation. These neurons, which include place, grid, head direction, and boundary-vector cells, are thought to collectively instantiate cognitive maps supporting flexible navigation. However, to flexibly navigate, it is necessary to also maintain internal representations of goal locations, such that goal-directed routes can be planned and executed. Although it has remained unclear how the mammalian brain represents goal locations, multiple neural candidates have recently been uncovered during different phases of navigation. For example, during planning, sequential activation of spatial cells may enable simulation of future routes toward the goal. During travel, modulation of spatial cells by the prospective route, or by distance and direction to the goal, may allow maintenance of route and goal-location information, supporting navigation on an ongoing basis. As the goal is approached, an increased activation of spatial cells may enable the goal location to become distinctly represented within cognitive maps, aiding goal localization. Lastly, after arrival at the goal, sequential activation of spatial cells may represent the just-taken route, enabling route learning and evaluation. Here, we review and synthesize these and other evidence for goal coding in mammalian brains, relate the experimental findings to predictions from computational models, and discuss outstanding questions and future challenges.

Clinical correlates of drug-related dreams in opioid use disorder

BACKGROUND AND OBJECTIVES

Drug-related dreams are commonly reported by individuals in treatment for substance use disorders, which may be distressing. Existing evidence suggests that dream recollection may be influenced by clinically relevant phenomena, such as opioid use and withdrawal, general sleep disturbance, affective symptoms, and chronic pain. However, very few studies have explored drug-related dreams among individuals who screened positive for opioid use disorder (OUD).

METHODS

Adults recruited from Amazon Mechanical Turk (MTurk) who screened positive for OUD (N = 154) completed a questionnaire about drug-related dreams, as well as measures assessing sleep, opioid use history, stress, anxiety, and chronic pain. χ² analyses, one-way analysis of variance, and bivariate correlations, correcting for the false discovery rate, were used as appropriate to explore correlates of (1) recollecting a drug-related dream, and (2) experiencing post-dream craving and distress.

RESULTS

Individuals who recollected a past-week drug-related dream were more likely to report other recent sleep disturbances, including poorer sleep quality, greater insomnia symptoms, and a higher risk for sleep apnea. Post-dream craving and distress were both associated with greater insomnia symptoms, poor sleep hygiene behaviors, and greater anxiety symptoms. Individuals who had ever experienced a drug-related dream (recently, or in their lifetime) were more likely to report a history of severe withdrawal, overdose, and intravenous opioid use.

CONCLUSION AND SCIENTIFIC SIGNIFICANCE

Drug-related dreams were common among individuals in the present sample and were related to other clinically relevant phenomena. Interventions that treat co-occurring OUD, pain, sleep symptoms, and affective symptoms may improve overall well-being in this population.

Breaking Down a Rhythm: Dissecting the Mechanisms Underlying Task-Related Neural Oscillations

A century worth of research has linked multiple cognitive, perceptual and behavioral states to various brain oscillations. However, the mechanistic roles and circuit underpinnings of these oscillations remain an area of active study. In this review, we argue that the advent of optogenetic and related systems neuroscience techniques has shifted the field from correlational to causal observations regarding the role of oscillations in brain function. As a result, studying brain rhythms associated with behavior can provide insight at different levels, such as decoding task-relevant information, mapping relevant circuits or determining key proteins involved in rhythmicity. We summarize recent advances in this field, highlighting the methods that are being used for this purpose, and discussing their relative strengths and limitations. We conclude with promising future approaches that will help unravel the functional role of brain rhythms in orchestrating the repertoire of complex behavior.