Play it again: reactivation of waking experience and memory

Repetition dynamically and rapidly increases cortical, but not hippocampal, offline reactivation

No sooner is an experience over than its neural representation begins to be transformed through memory reactivation during offline periods. The lion's share of prior research has focused on understanding offline reactivation within the hippocampus. However, it is hypothesized that consolidation processes involve offline reactivation in cortical regions as well as coordinated reactivation in the hippocampus and cortex. Using fMRI, we presented novel and repeated paired associates to participants during encoding and measured offline memory reactivation for those events during an immediate post-encoding rest period. post-encoding reactivation frequency of repeated and once-presented events did not differ in the hippocampus. However, offline reactivation in widespread cortical regions and hippocampal-cortical coordinated reactivation were significantly enhanced for repeated events. These results provide evidence that repetition might facilitate the distribution of memory representations across cortical networks, a hallmark of systems-level consolidation. Interestingly, we found that offline reactivation frequency in both hippocampus and cortex explained variance in behavioral success on an immediate associative recognition test for the once-presented information, potentially indicating a role of offline reactivation in maintaining these novel, weaker, memories. Together, our findings highlight that endogenous offline reactivation can be robustly and significantly modulated by study repetition.

Sleep-dependent decorrelation of hippocampal spatial representations

Neuronal ensembles are crucial for episodic memory and spatial mapping. Sleep, particularly non-REM (NREM), is vital for memory consolidation, as it triggers plasticity mechanisms through brain oscillations that reactivate neuronal ensembles. Here, we assessed their role in consolidating hippocampal spatial representations during sleep. We recorded hippocampus activity in rats performing a spatial object-place recognition (OPR) memory task, during encoding and retrieval periods, separated by intervening sleep. Successful OPR retrieval correlated with NREM duration, during which cortical oscillations decreased in power and density as well as neuronal spiking, suggesting global downregulation of network excitability. However, neurons encoding specific spatial locations (i.e., place cells) or objects during OPR showed stronger synchrony with brain oscillations compared to non-encoding neurons, and the stability of spatial representations decreased proportionally with NREM duration. Our findings suggest that NREM sleep may promote flexible remapping in hippocampal ensembles, potentially aiding memory consolidation and adaptation to novel spatial contexts.

Hippocampal cholecystokinin-expressing interneurons regulate temporal coding and contextual learning

Cholecystokinin-expressing interneurons (CCKIs) are hypothesized to shape pyramidal cell-firing patterns and regulate network oscillations and related network state transitions. To directly probe their role in the CA1 region, we silenced their activity using optogenetic and chemogenetic tools in mice. Opto-tagged CCKIs revealed a heterogeneous population, and their optogenetic silencing triggered wide disinhibitory network changes affecting both pyramidal cells and other interneurons. CCKI silencing enhanced pyramidal cell burst firing and altered the temporal coding of place cells: theta phase precession was disrupted, whereas sequence reactivation was enhanced. Chemogenetic CCKI silencing did not alter the acquisition of spatial reference memories on the Morris water maze but enhanced the recall of contextual fear memories and enabled selective recall when similar environments were tested. This work suggests the key involvement of CCKIs in the control of place-cell temporal coding and the formation of contextual memories.

Spindle-locked ripples mediate memory reactivation during human NREM sleep

Memory consolidation relies in part on the reactivation of previous experiences during sleep. The precise interplay of sleep-related oscillations (slow oscillations, spindles and ripples) is thought to coordinate the information flow between relevant brain areas, with ripples mediating memory reactivation. However, in humans empirical evidence for a role of ripples in memory reactivation is lacking. Here, we investigated the relevance of sleep oscillations and specifically ripples for memory reactivation during human sleep using targeted memory reactivation. Intracranial electrophysiology in epilepsy patients and scalp EEG in healthy participants revealed that elevated levels of slow oscillation - spindle activity coincided with the read-out of experimentally induced memory reactivation. Importantly, spindle-locked ripples recorded intracranially from the medial temporal lobe were found to be correlated with the identification of memory reactivation during non-rapid eye movement sleep. Our findings establish ripples as key-oscillation for sleep-related memory reactivation in humans and emphasize the importance of the coordinated interplay of the cardinal sleep oscillations.

Does sleep benefit source memory? Investigating 12-h retention intervals with a multinomial modeling approach

For retention intervals of up to 12 h, the active systems consolidation hypothesis predicts that sleep compared to wakefulness strengthens the context binding of memories previously established during encoding. Sleep should thus improve source memory. By comparing retention intervals filled with natural night sleep versus daytime wakefulness, we tested this prediction in two online source-monitoring experiments using intentionally learned pictures as items and incidentally learned screen positions and frame colors as source dimensions. In Experiment 1, we examined source memory by varying the spatial position of pictures on the computer screen. Multinomial modeling analyses revealed a significant sleep benefit in source memory. In Experiment 2, we manipulated both the spatial position and the frame color of pictures orthogonally to investigate source memory for two different source dimensions at the same time, also allowing exploration of bound memory for both source dimensions. The sleep benefit on spatial source memory replicated. In contrast, no source memory sleep benefit was observed for either frame color or bound memory of both source dimensions, probably as a consequence of a floor effect in incidental encoding of color associations. In sum, the results of both experiments show that sleep within a 12-h retention interval improves source memory for spatial positions, supporting the prediction of the active systems consolidation hypothesis. However, additional research is required to clarify the impact of sleep on source memory for other context features and bound memories of multiple source dimensions.

Hippocampal sharp-wave ripples correlate with periods of naturally occurring self-generated thoughts in humans

Core features of human cognition highlight the importance of the capacity to focus on information distinct from events in the here and now, such as mind wandering. However, the brain mechanisms that underpin these self-generated states remain unclear. An emerging hypothesis is that self-generated states depend on the process of memory replay, which is linked to sharp-wave ripples (SWRs), which are transient high-frequency oscillations originating in the hippocampus. Local field potentials were recorded from the hippocampus of 10 patients with epilepsy for up to 15 days, and experience sampling was used to describe their association with ongoing thought patterns. The SWR rates were higher during extended periods of time when participants' ongoing thoughts were more vivid, less desirable, had more imaginable properties, and exhibited fewer correlations with an external task. These data suggest a role for SWR in the patterns of ongoing thoughts that humans experience in daily life.

Consolidation of emotional memory during waking rest depends on trait anxiety

A short period of eyes-closed waking rest improves long-term memory for recently learned information, including declarative, spatial, and procedural memory. However, the effect of rest on emotional memory consolidation remains unknown. This preregistered study aimed to establish whether post-encoding rest affects emotional memory and how anxiety levels might modulate this effect. Participants completed a modified version of the dot-probe attention task that involved reacting to and encoding word stimuli appearing underneath emotionally negative or neutral photos. We tested the effect of waking rest on memory for these words and pictures by manipulating the state that participants entered just after this task (rest vs. active wake). Trait anxiety levels were measured using the State-Trait Anxiety Inventory and examined as a covariate. Waking rest improved emotional memory consolidation for individuals high in trait anxiety. These results suggest that the beneficial effect of waking rest on memory extends into the emotional memory domain but depends on individual characteristics such as anxiety.

Awake targeted memory reactivation doesn't work

Memories are pliable and can be biased by post-encoding information. In targeted memory reactivation (TMR) studies, participants encode information then sleep, during which time sounds or scents that were previously associated with the encoded images are re-presented in an effort to trigger reactivation of the associated memory traces. Upon subsequent testing, memory for reactivated items is often enhanced. Is sleep essential for this process? The literature on awake TMR is small and findings are mixed. Here, we asked English-speaking adults to learn Japanese vocabulary words. During a subsequent active rest phase, participants played Tetris while sound cues associated with the vocabulary words were presented. Results showed that when memories were reactivated, they were either disrupted (Experiment 1) or unaffected (Experiments 2, 3). These findings indicate that awake TMR is not beneficial, and may actually impair subsequent memory. These findings have important implications for research on memory consolidation and reactivation.

Reduced overnight memory consolidation and associated alterations in sleep spindles and slow oscillations in early Alzheimer's disease

Spatial navigation critically underlies hippocampal-entorhinal circuit function that is early affected in Alzheimer's disease (AD). There is growing evidence that AD pathophysiology dynamically interacts with the sleep/wake cycle impairing hippocampal memory. To elucidate sleep-dependent consolidation in a cohort of symptomatic AD patients (n = 12, 71.25 ± 2.16 years), we tested hippocampal place learning by means of a virtual reality task and verbal memory by a word-pair association task before and after a night of sleep. Our results show an impaired overnight memory retention in AD compared with controls in the verbal task, together with a significant reduction of sleep spindle activity (i.e., lower amplitude of fast sleep spindles, p = 0.016) and increased duration of the slow oscillation (SO; p = 0.019). Higher spindle density, faster down-to-upstate transitions within SOs, and the time delay between SOs and nested spindles predicted better memory performance in healthy controls but not in AD patients. Our results show that mnemonic processing and memory consolidation in AD is slightly impaired as reflected by dysfunctional oscillatory dynamics and spindle-SO coupling during NonREM sleep. In this translational study based on experimental paradigms in animals and extending previous work in healthy aging and preclinical disease stages, our results in symptomatic AD further deepen the understanding of the memory decline within a bidirectional relationship of sleep and AD pathology.

Sensory cortical ensembles exhibit differential coupling to ripples in distinct hippocampal subregions

Cortical neurons activated during recent experiences often reactivate with dorsal hippocampal CA1 ripples during subsequent rest. Less is known about cortical interactions with intermediate hippocampal CA1, whose connectivity, functions, and ripple events differ from dorsal CA1. We identified three clusters of putative excitatory neurons in mouse visual cortex that are preferentially excited together with either dorsal or intermediate CA1 ripples or suppressed before both ripples. Neurons in each cluster were evenly distributed across primary and higher visual cortices and co-active even in the absence of ripples. These ensembles exhibited similar visual responses but different coupling to thalamus and pupil-indexed arousal. We observed a consistent activity sequence preceding and predicting ripples: (1) suppression of ripple-suppressed cortical neurons, (2) thalamic silence, and (3) activation of intermediate CA1-ripple-activated cortical neurons. We propose that coordinated dynamics of these ensembles relay visual experiences to distinct hippocampal subregions for incorporation into different cognitive maps.

Exploring Ripple Waves in the Human Brain

Ripples are brief (<150 ms) high-frequency oscillatory neural activities in the brain with a range of 140 to 200 Hz in rodents and 80 to 140 Hz in humans. Ripples are regarded as playing an essential role in several aspects of memory function, mainly in the hippocampus. This type of ripple generally occurs with sharp waves and is called a sharp-wave ripple (SPW-R). Extensive research of SPW-Rs in the rodent brain while actively awake has also linked the function of these SPW-Rs to navigation and decision making. Although many studies with rodents unveiled SPW-R function, research in humans on this subject is still sparse. Therefore, unveiling SPW-R function in the human hippocampus is warranted. A certain type of ripples may also be a biomarker of epilepsy. This type of ripple is called a pathological ripple (p-ripple). p-ripples have a wider range of frequency (80-500 Hz) than SPW-Rs, and the range of frequency is especially higher in brain regions that are intrinsically linked to epilepsy onset. Brain regions producing ripples are too small for scalp electrode recording, and intracranial recording is typically needed to detect ripples. In addition, SPW-Rs in the human hippocampus have been recorded from patients with epilepsy who may have p-ripples. Differentiating SPW-Rs and p-ripples is often not easy. We need to develop more sophisticated methods to record SPW-Rs to differentiate them from p-ripples. This paper reviews the general features and roles of ripple waves.

The developmental trajectory of object recognition robustness: Children are like small adults but unlike big deep neural networks

In laboratory object recognition tasks based on undistorted photographs, both adult humans and deep neural networks (DNNs) perform close to ceiling. Unlike adults', whose object recognition performance is robust against a wide range of image distortions, DNNs trained on standard ImageNet (1.3M images) perform poorly on distorted images. However, the last 2 years have seen impressive gains in DNN distortion robustness, predominantly achieved through ever-increasing large-scale datasets-orders of magnitude larger than ImageNet. Although this simple brute-force approach is very effective in achieving human-level robustness in DNNs, it raises the question of whether human robustness, too, is simply due to extensive experience with (distorted) visual input during childhood and beyond. Here we investigate this question by comparing the core object recognition performance of 146 children (aged 4-15 years) against adults and against DNNs. We find, first, that already 4- to 6-year-olds show remarkable robustness to image distortions and outperform DNNs trained on ImageNet. Second, we estimated the number of images children had been exposed to during their lifetime. Compared with various DNNs, children's high robustness requires relatively little data. Third, when recognizing objects, children-like adults but unlike DNNs-rely heavily on shape but not on texture cues. Together our results suggest that the remarkable robustness to distortions emerges early in the developmental trajectory of human object recognition and is unlikely the result of a mere accumulation of experience with distorted visual input. Even though current DNNs match human performance regarding robustness, they seem to rely on different and more data-hungry strategies to do so.

Memory and Consciousness-Usually in Tandem but Sometimes Apart

Episodic memory, the ability to remember specific events from one's personal past, has been the subject of research for several decades, with a particular emphasis on its relationship with consciousness. In the December 2022 issue of Cognitive and Behavioral Neurology , Budson, Richman, and Kensinger shed new light on this complex topic with a comprehensive exploration of consciousness. In this commentary, I present three propositions about the relationship between episodic memory and consciousness: (1) Episodic memory is usually associated with conscious retrieval; (2) it is possible to have consciousness without episodic memory; and (3) episodic memory can be accessed without conscious retrieval. Drawing from studies conducted with nonhuman animals, I provide evidence to support each of these propositions and discuss how they relate to the theory presented by Budson et al (2000). Although some of my propositions differ from their views, their work has been valuable in stimulating ongoing discussions to advance our understanding of memory.

L-DOPA increases slow-wave sleep duration and selectively modulates memory persistence in older adults

Introduction

Millions of people worldwide take medications such as L-DOPA that increase dopamine to treat Parkinson's disease. Yet, we do not fully understand how L-DOPA affects sleep and memory. Our earlier research in Parkinson's disease revealed that the timing of L-DOPA relative to sleep affects dopamine's impact on long-term memory. Dopamine projections between the midbrain and hippocampus potentially support memory processes during slow wave sleep. In this study, we aimed to test the hypothesis that L-DOPA enhances memory consolidation by modulating NREM sleep.

Methods

We conducted a double-blind, randomised, placebo-controlled crossover trial with healthy older adults (65-79 years, n = 35). Participants first learned a word list and were then administered long-acting L-DOPA (or placebo) before a full night of sleep. Before sleeping, a proportion of the words were re-exposed using a recognition test to strengthen memory. L-DOPA was active during sleep and the practice-recognition test, but not during initial learning.

Results

The single dose of L-DOPA increased total slow-wave sleep duration by approximately 11% compared to placebo, while also increasing spindle amplitudes around slow oscillation peaks and around 1-4 Hz NREM spectral power. However, behaviourally, L-DOPA worsened memory of words presented only once compared to re-exposed words. The coupling of spindles to slow oscillation peaks correlated with these differential effects on weaker and stronger memories. To gauge whether L-DOPA affects encoding or retrieval of information in addition to consolidation, we conducted a second experiment targeting L-DOPA only to initial encoding or retrieval and found no behavioural effects.

Discussion

Our results demonstrate that L-DOPA augments slow wave sleep in elderly, perhaps tuning coordinated network activity and impacting the selection of information for long-term storage. The pharmaceutical modification of slow-wave sleep and long-term memory may have clinical implications.

Clinical trial registration

Eudract number: 2015-002027-26; https://doi.org/10.1186/ISRCTN90897064, ISRCTN90897064.

Sensory cortical ensembles exhibit differential coupling to ripples in distinct hippocampal subregions

Cortical neurons activated during recent experiences often reactivate with dorsal hippocampal CA1 sharp-wave ripples (SWRs) during subsequent rest. Less is known about cortical interactions with intermediate hippocampal CA1, whose connectivity, functions, and SWRs differ from those of dorsal CA1. We identified three clusters of visual cortical excitatory neurons that are excited together with either dorsal or intermediate CA1 SWRs, or suppressed before both SWRs. Neurons in each cluster were distributed across primary and higher visual cortices and co-active even in the absence of SWRs. These ensembles exhibited similar visual responses but different coupling to thalamus and pupil-indexed arousal. We observed a consistent activity sequence: (i) suppression of SWR-suppressed cortical neurons, (ii) thalamic silence, and (iii) activation of the cortical ensemble preceding and predicting intermediate CA1 SWRs. We propose that the coordinated dynamics of these ensembles relay visual experiences to distinct hippocampal subregions for incorporation into different cognitive maps.

Deep reinforcement learning for data-efficient weakly supervised business process anomaly detection

The detection of anomalous behavior in business process data is a crucial task for preventing failures that may jeopardize the performance of any organization. Supervised learning techniques are impracticable because of the difficulties of gathering huge amounts of labeled business process anomaly data. For this reason, unsupervised learning techniques and semi-supervised learning approaches trained on entirely labeled normal data have dominated this domain for a long time. However, these methods do not work well because of the absence of prior knowledge of true anomalies. In this study, we propose a deep weakly supervised reinforcement learning-based approach to identify anomalies in business processes by leveraging limited labeled anomaly data. The proposed approach is intended to use a small collection of labeled anomalous data while exploring a huge set of unlabeled data to find new classes of anomalies that are outside the scope of the labeled anomalous data. We created a unique reward function that combined the supervisory signal supplied by a variational autoencoder trained on unlabeled data with the supervisory signal provided by the environment’s reward. To further reduce data deficiency, we introduced a sampling method to allow the effective exploration of the unlabeled data and to address the imbalanced data problem, which is a common problem in the anomaly detection field. This approach depends on the proximity between the data samples in the latent space of the variational autoencoder. Furthermore, to efficiently model the sequential nature of business process data and to handle the long-term dependences, we used a long short-term memory network combined with a self-attention mechanism to develop the agent of our reinforcement learning model. Multiple scenarios were used to test the proposed approach on real-world and synthetic datasets. The findings revealed that the proposed approach outperformed five competing approaches by efficiently using the few available anomalous examples.

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.

Recurrent networks endowed with structural priors explain suboptimal animal behavior

The strategies found by animals facing a new task are determined both by individual experience and by structural priors evolved to leverage the statistics of natural environments. Rats quickly learn to capitalize on the trial sequence correlations of two-alternative forced choice (2AFC) tasks after correct trials but consistently deviate from optimal behavior after error trials. To understand this outcome-dependent gating, we first show that recurrent neural networks (RNNs) trained in the same 2AFC task outperform rats as they can readily learn to use across-trial information both after correct and error trials. We hypothesize that, although RNNs can optimize their behavior in the 2AFC task without any a priori restrictions, rats' strategy is constrained by a structural prior adapted to a natural environment in which rewarded and non-rewarded actions provide largely asymmetric information. When pre-training RNNs in a more ecological task with more than two possible choices, networks develop a strategy by which they gate off the across-trial evidence after errors, mimicking rats' behavior. Population analyses show that the pre-trained networks form an accurate representation of the sequence statistics independently of the outcome in the previous trial. After error trials, gating is implemented by a change in the network dynamics that temporarily decouple the categorization of the stimulus from the across-trial accumulated evidence. Our results suggest that the rats' suboptimal behavior reflects the influence of a structural prior that reacts to errors by isolating the network decision dynamics from the context, ultimately constraining the performance in a 2AFC laboratory task.

Visual Plasticity in Adulthood: Perspectives from Hebbian and Homeostatic Plasticity

The visual system retains profound plastic potential in adulthood. In the current review, we summarize the evidence of preserved plasticity in the adult visual system during visual perceptual learning as well as both monocular and binocular visual deprivation. In each condition, we discuss how such evidence reflects two major cellular mechanisms of plasticity: Hebbian and homeostatic processes. We focus on how these two mechanisms work together to shape plasticity in the visual system. In addition, we discuss how these two mechanisms could be further revealed in future studies investigating cross-modal plasticity in the visual system.

Place cells are nonrandomly clustered by field location in CA1 hippocampus

A challenge in both modern and historic neuroscience has been achieving an understanding of neuron circuits, and determining the computational and organizational principles that underlie these circuits. Deeper understanding of the organization of brain circuits and cell types, including in the hippocampus, is required for advances in behavioral and cognitive neuroscience, as well as for understanding principles governing brain development and evolution. In this manuscript, we pioneer a new method to analyze the spatial clustering of active neurons in the hippocampus. We use calcium imaging and a rewarded navigation task to record from 100 s of place cells in the CA1 of freely moving rats. We then use statistical techniques developed for and in widespread use in geographic mapping studies, global Moran's I, and local Moran's I to demonstrate that cells that code for similar spatial locations tend to form small spatial clusters. We present evidence that this clustering is not the result of artifacts from calcium imaging, and show that these clusters are primarily formed by cells that have place fields around previously rewarded locations. We go on to show that, although cells with similar place fields tend to form clusters, there is no obvious topographic mapping of environmental location onto the hippocampus, such as seen in the visual cortex. Insights into hippocampal organization, as in this study, can elucidate mechanisms underlying motivational behaviors, spatial navigation, and memory formation.

To sleep or not to sleep - Effects on memory in normal aging and disease

Sleep behavior undergoes significant changes across the lifespan, and aging is associated with marked alterations in sleep amounts and quality. The primary sleep changes in healthy older adults include a shift in sleep timing, reduced slow-wave sleep, and impaired sleep maintenance. However, neurodegenerative and psychiatric disorders are more common among the elderly, which further worsen their sleep health. Irrespective of the cause, insufficient sleep adversely affects various bodily functions including energy metabolism, mood, and cognition. In this review, we will focus on the cognitive changes associated with inadequate sleep during normal aging and the underlying neural mechanisms.

Compensatory cognition in neurological diseases and aging: A review of animal and human studies

Specialized individual circuits in the brain are recruited for specific functions. Interestingly, multiple neural circuitries continuously compete with each other to acquire the specialized function. However, the dominant among them compete and become the central neural network for that particular function. For example, the hippocampal principal neural circuitries are the dominant networks among many which are involved in learning processes. But, in the event of damage to the principal circuitry, many times, less dominant networks compensate for the primary network. This review highlights the psychopathologies of functional loss and the aspects of functional recuperation in the absence of the hippocampus.

The expanded circuitry of hippocampal ripples and replay

The place cells and well-defined oscillatory population rhythms of the rodent hippocampus have served as a powerful model system in linking cells and circuits to memory function. While the initial three decades of place cell research primarily focused on the activity of neurons during exploration, the last twenty-five years have seen growing interest in the physiology of the hippocampus at rest. During slow-wave sleep and quiet wakefulness the hippocampus exhibits sharp-wave ripples (SWRs), short high-frequency, high-amplitude oscillations, that organize the reactivation or 'replay' of sequences of place cells, and interventions that disrupt SWRs impair learning. While the canonical model of SWRs generation have emphasized CA3 input to CA1 as the source of excitatory drive, recent work suggests there are multiple circuits, including the CA2 region, that can both influence, generate and organize SWRs, both from the oscillatory and information content perspectives in a task and state-dependent manner. This extended circuitry and its function must be considered for a true understanding of the role of the hippocampus in off-line processes such as planning and consolidation.

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'.

Artificial intelligence insights into hippocampal processing

Advances in artificial intelligence, machine learning, and deep neural networks have led to new discoveries in human and animal learning and intelligence. A recent artificial intelligence agent in the DeepMind family, muZero, can complete a variety of tasks with limited information about the world in which it is operating and with high uncertainty about features of current and future space. To perform, muZero uses only three functions that are general yet specific enough to allow learning across a variety of tasks without overgeneralization across different contexts. Similarly, humans and animals are able to learn and improve in complex environments while transferring learning from other contexts and without overgeneralizing. In particular, the mammalian extrahippocampal system (eHPCS) can guide spatial decision making while simultaneously encoding and processing spatial and contextual information. Like muZero, the eHPCS is also able to adjust contextual representations depending on the degree and significance of environmental changes and environmental cues. In this opinion, we will argue that the muZero functions parallel those of the hippocampal system. We will show that the different components of the muZero model provide a framework for thinking about generalizable learning in the eHPCS, and that the evaluation of how transitions in cell representations occur between similar and distinct contexts can be informed by advances in artificial intelligence agents such as muZero. We additionally explain how advances in AI agents will provide frameworks and predictions by which to investigate the expected link between state changes and neuronal firing. Specifically, we will discuss testable predictions about the eHPCS, including the functions of replay and remapping, informed by the mechanisms behind muZero learning. We conclude with additional ways in which agents such as muZero can aid in illuminating prospective questions about neural functioning, as well as how these agents may shed light on potential expected answers.

Hippocampal gamma and sharp wave/ripples mediate bidirectional interactions with cortical networks during sleep

Hippocampus-neocortex interactions during sleep are critical for memory processes: Hippocampally initiated replay contributes to memory consolidation in the neocortex and hippocampal sharp wave/ripples modulate cortical activity. Yet, the spatial and temporal patterns of this interaction are unknown. With voltage imaging, electrocorticography, and laminarly resolved hippocampal potentials, we characterized cortico-hippocampal signaling during anesthesia and nonrapid eye movement sleep. We observed neocortical activation transients, with statistics suggesting a quasi-critical regime, may be helpful for communication across remote brain areas. From activity transients, we identified, in a data-driven fashion, three functional networks. A network overlapping with the default mode network and centered on retrosplenial cortex was the most associated with hippocampal activity. Hippocampal slow gamma rhythms were strongly associated to neocortical transients, even more than ripples. In fact, neocortical activity predicted hippocampal slow gamma and followed ripples, suggesting that consolidation processes rely on bidirectional signaling between hippocampus and neocortex.

Sustained upregulation of widespread hippocampal-neocortical coupling following memory encoding

Systems consolidation of new experiences into lasting episodic memories involves hippocampal-neocortical interactions. Evidence of this process is already observed during early post-encoding rest periods, both as increased hippocampal coupling with task-relevant perceptual regions and reactivation of stimulus-specific patterns following intensive encoding tasks. We investigate the spatial and temporal characteristics of these hippocampally anchored post-encoding neocortical modulations. Eighty-nine adults participated in an experiment consisting of interleaved memory task- and resting-state periods. We observed increased post-encoding functional connectivity between hippocampus and individually localized neocortical regions responsive to stimuli encountered during memory encoding. Post-encoding modulations were manifested as a nearly system-wide upregulation in hippocampal coupling with all major functional networks. The configuration of these extensive modulations resembled hippocampal-neocortical interaction patterns estimated from active encoding operations, suggesting hippocampal post-encoding involvement exceeds perceptual aspects. Reinstatement of encoding patterns was not observed in resting-state scans collected 12 h later, nor when using other candidate seed regions. The similarity in hippocampal functional coupling between online memory encoding and offline post-encoding rest suggests reactivation in humans involves a spectrum of cognitive processes engaged during the experience of an event. There were no age effects, suggesting that upregulation of hippocampal-neocortical connectivity represents a general phenomenon seen across the adult lifespan.

Brief memory reactivations induce learning in the numeric domain

Learning of arithmetic facts such as the multiplication table requires time-consuming, repeated practice. In light of evidence indicating that reactivation of encoded memories can modulate learning and memory processes at the synaptic, system and behavioral levels, we asked whether brief memory reactivations can induce human learning in the numeric domain. Adult participants performed a number-fact retrieval task in which they learned arbitrary numeric facts. Following encoding and a baseline test, 3 passive, brief reactivation sessions of only 40 s each were conducted on separate days. Learning was evaluated in a retest session. Results showed reactivations induced learning, with improved performance at retest relative to baseline test. Furthermore, performance was superior compared to a control group performing test-retest sessions without reactivations, who showed significant memory deterioration. A standard practice group completed active-retrieval sessions on 3 separate days, and showed significant learning gains. Interestingly, while these gains were higher than those of the reactivations group, subjects showing reactivation-induced learning were characterized by superior efficiency relative to standard practice subjects, with higher rate of improvement per practice time. A follow-up long-term retention experiment showed that 30 days following initial practice, weekly brief reactivations reduced forgetting, with participants performing superior to controls undergoing the same initial practice without reactivations. Overall, the results demonstrate that brief passive reactivations induce efficient learning and reduce forgetting within a numerical context. Time-efficient practice in the numeric domain carries implications for enhancement of learning strategies in daily-life settings.

Replay, the default mode network and the cascaded memory systems model

The spontaneous replay of patterns of activity related to past experiences and memories is a striking feature of brain activity, as is the coherent activation of sets of brain areas - particularly those comprising the default mode network (DMN) - during rest. We propose that these two phenomena are strongly intertwined and that their potential functions overlap. In the 'cascaded memory systems model' that we outline here, we hypothesize that the DMN forms the backbone for the propagation of replay, mediating interactions between the hippocampus and the neocortex that enable the consolidation of new memories. The DMN may also independently ignite replay cascades, which support reactivation of older memories or high-level semantic representations. We suggest that transient cortical activations, inducing long-range correlations across the neocortex, are a key mechanism supporting a hierarchy of representations that progresses from simple percepts to semantic representations of causes and, finally, to whole episodes.

Precision dynamical mapping using topological data analysis reveals a hub-like transition state at rest

In the absence of external stimuli, neural activity continuously evolves from one configuration to another. Whether these transitions or explorations follow some underlying arrangement or lack a predictable ordered plan remains to be determined. Here, using fMRI data from highly sampled individuals (~5 hours of resting-state data per individual), we aimed to reveal the rules that govern transitions in brain activity at rest. Our Topological Data Analysis based Mapper approach characterized a highly visited transition state of the brain that acts as a switch between different neural configurations to organize the spontaneous brain activity. Further, while the transition state was characterized by a uniform representation of canonical resting-state networks (RSNs), the periphery of the landscape was dominated by a subject-specific combination of RSNs. Altogether, we revealed rules or principles that organize spontaneous brain activity using a precision dynamics approach.

Is the role of sleep in memory consolidation overrated?

Substantial empirical evidence suggests that sleep benefits the consolidation and reorganization of learned information. Consequently, the concept of "sleep-dependent memory consolidation" is now widely accepted by the scientific community, in addition to influencing public perceptions regarding the functions of sleep. There are, however, numerous studies that have presented findings inconsistent with the sleep-memory hypothesis. Here, we challenge the notion of "sleep-dependency" by summarizing evidence for effective memory consolidation independent of sleep. Plasticity mechanisms thought to mediate or facilitate consolidation during sleep (e.g., neuronal replay, reactivation, slow oscillations, neurochemical milieu) also operate during non-sleep states, particularly quiet wakefulness, thus allowing for the stabilization of new memories. We propose that it is not sleep per se, but the engagement of plasticity mechanisms, active during both sleep and (at least some) waking states, that constitutes the critical factor determining memory formation. Thus, rather than playing a "critical" role, sleep falls along a continuum of behavioral states that vary in their effectiveness to support memory consolidation at the neural and behavioral level.

Alterations of sleep oscillations in Alzheimer's disease: A potential role for GABAergic neurons in the cortex, hippocampus, and thalamus

Sleep abnormalities are widely reported in patients with Alzheimer's disease (AD) and are linked to cognitive impairments. Sleep abnormalities could be potential biomarkers to detect AD since they are often observed at the preclinical stage. Moreover, sleep could be a target for early intervention to prevent or slow AD progression. Thus, here we review changes in brain oscillations observed during sleep, their connection to AD pathophysiology and the role of specific brain circuits. Slow oscillations (0.1-1 Hz), sleep spindles (8-15 Hz) and their coupling during non-REM sleep are consistently reduced in studies of patients and in AD mouse models although the timing and magnitude of these alterations depends on the pathophysiological changes and the animal model studied. Changes in delta (1-4 Hz) activity are more variable. Animal studies suggest that hippocampal sharp-wave ripples (100-250 Hz) are also affected. Reductions in REM sleep amount and slower oscillations during REM are seen in patients but less consistently in animal models. Thus, changes in a variety of sleep oscillations could impact sleep-dependent memory consolidation or restorative functions of sleep. Recent mechanistic studies suggest that alterations in the activity of GABAergic neurons in the cortex, hippocampus and thalamic reticular nucleus mediate sleep oscillatory changes in AD and represent a potential target for intervention. Longitudinal studies of the timing of AD-related sleep abnormalities with respect to pathology and dysfunction of specific neural networks are needed to identify translationally relevant biomarkers and guide early intervention strategies to prevent or delay AD progression.

Social Learning of a Spatial Task by Observation Alone

Interactions between conspecifics are central to the acquisition of useful memories in the real world. Observational learning, i.e., learning a task by observing the success or failure of others, has been reported in many species, including rodents. However, previous work in rats with NMDA-receptor blockade has shown that even extensive observation of an unexplored space through a clear barrier is not sufficient to generate a stable hippocampal representation of that space. This raises the question of whether rats can learn a spatial task in a purely observed space from watching a conspecific, and if so, does this somehow stabilize their hippocampal representation? To address these questions, we designed an observational spatial task in a two-part environment that is nearly identical to that of the aforementioned electrophysiological study, in which an observer rat watches a demonstrator animal to learn the location of a hidden reward. Our results demonstrate that rats do not need to physically explore an environment to learn a reward location, provided a conspecific demonstrates where it is. We also show that the behavioral memory is not affected by NMDA receptor blockade, suggesting that the spatial representation underlying the behavior has been consolidated by observation alone.

Natural VTA activity during NREM sleep influences future exploratory behavior

During wakefulness, the VTA represents the valence of experiences and mediates affective response to the outside world. Recent work revealed that two major VTA populations – dopamine and GABA neurons – are highly active during REM sleep and less active during NREM sleep. Using long-term cell type and brain state-specific recordings, machine learning, and optogenetics, we examined the role that the sleep-activity of these neurons plays in subsequent awake behavior. We found that VTA activity during NREM (but not REM) sleep correlated with exploratory features of the next day’s behavior. Disrupting natural VTA activity during NREM (but not REM) sleep reduced future tendency to explore and increased preferences for familiarity and goal-directed actions, with no direct effect on learning or memory. Our data suggest that, during deep sleep, VTA neurons engage in offline processing, consolidating not memories but affective responses to remembered environments, shaping the way that animals respond to future experiences.

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Dopamine and GABA neurons in the VTA are active during NREM as well as REM sleep

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VTA activity during NREM-sleep — but not REM-sleep — is correlated with exploration the next day

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Inhibiting this activity during NREM-sleep — but not REM-sleep — reduces future exploration

Learning cortical representations through perturbed and adversarial dreaming

Humans and other animals learn to extract general concepts from sensory experience without extensive teaching. This ability is thought to be facilitated by offline states like sleep where previous experiences are systemically replayed. However, the characteristic creative nature of dreams suggests that learning semantic representations may go beyond merely replaying previous experiences. We support this hypothesis by implementing a cortical architecture inspired by generative adversarial networks (GANs). Learning in our model is organized across three different global brain states mimicking wakefulness, non-rapid eye movement (NREM), and REM sleep, optimizing different, but complementary, objective functions. We train the model on standard datasets of natural images and evaluate the quality of the learned representations. Our results suggest that generating new, virtual sensory inputs via adversarial dreaming during REM sleep is essential for extracting semantic concepts, while replaying episodic memories via perturbed dreaming during NREM sleep improves the robustness of latent representations. The model provides a new computational perspective on sleep states, memory replay, and dreams, and suggests a cortical implementation of GANs.

Tinnitus: at a crossroad between phantom perception and sleep

Sensory disconnection from the environment is a hallmark of sleep and is crucial for sleep maintenance. It remains unclear, however, whether internally generated percepts—phantom percepts—may overcome such disconnection and, in turn, how sleep and its effect on sensory processing and brain plasticity may affect the function of the specific neural networks underlying such phenomena. A major hurdle in addressing this relationship is the methodological difficulty to study sensory phantoms, due to their subjective nature and lack of control over the parameters or neural activity underlying that percept. Here, we explore the most prevalent phantom percept, subjective tinnitus—or tinnitus for short—as a model to investigate this. Tinnitus is the permanent perception of a sound with no identifiable corresponding acoustic source. This review offers a novel perspective on the functional interaction between brain activity across the sleep–wake cycle and tinnitus. We discuss characteristic features of brain activity during tinnitus in the awake and the sleeping brain and explore its effect on sleep functions and homeostasis. We ask whether local changes in cortical activity in tinnitus may overcome sensory disconnection and prevent the occurrence of global restorative sleep and, in turn, how accumulating sleep pressure may temporarily alleviate the persistence of a phantom sound. Beyond an acute interaction between sleep and neural activity, we discuss how the effects of sleep on brain plasticity may contribute to aberrant neural circuit activity and promote tinnitus consolidation. Tinnitus represents a unique window into understanding the role of sleep in sensory processing. Clarification of the underlying relationship may offer novel insights into therapeutic interventions in tinnitus management.

Photonic reinforcement learning based on optoelectronic reservoir computing

Reinforcement learning has been intensively investigated and developed in artificial intelligence in the absence of training data, such as autonomous driving vehicles, robot control, internet advertising, and elastic optical networks. However, the computational cost of reinforcement learning with deep neural networks is extremely high and reducing the learning cost is a challenging issue. We propose a photonic on-line implementation of reinforcement learning using optoelectronic delay-based reservoir computing, both experimentally and numerically. In the proposed scheme, we accelerate reinforcement learning at a rate of several megahertz because there is no required learning process for the internal connection weights in reservoir computing. We perform two benchmark tasks, CartPole-v0 and MountanCar-v0 tasks, to evaluate the proposed scheme. Our results represent the first hardware implementation of reinforcement learning based on photonic reservoir computing and pave the way for fast and efficient reinforcement learning as a novel photonic accelerator.

Breathing coordinates cortico-hippocampal dynamics in mice during offline states

Network dynamics have been proposed as a mechanistic substrate for the information transfer across cortical and hippocampal circuits. However, little is known about the mechanisms that synchronize and coordinate these processes across widespread brain regions during offline states. Here we address the hypothesis that breathing acts as an oscillatory pacemaker, persistently coupling distributed brain circuit dynamics. Using large-scale recordings from a number of cortical and subcortical brain regions in behaving mice, we uncover the presence of an intracerebral respiratory corollary discharge, that modulates neural activity across these circuits. During offline states, the respiratory modulation underlies the coupling of hippocampal sharp-wave ripples and cortical DOWN/UP state transitions, which mediates systems memory consolidation. These results highlight breathing, a perennial brain rhythm, as an oscillatory scaffold for the functional coordination of the limbic circuit that supports the segregation and integration of information flow across neuronal networks during offline states.

Sleep and homeostatic control of plasticity

Sleep homeostasis is a complex neurobiologic phenomenon involving a number of molecular pathways, neurotransmitter release, synaptic activity, and factors modulating neural networks. Sleep plasticity allows for homeostatic optimization of neural networks and the replay-based consolidation of specific circuits, especially important for cognition, behavior, and information processing. Furthermore, research is currently moving from an essentially brain-focused to a more comprehensive view involving other systems, such as the immune system, hormonal status, and metabolic pathways. When dysfunctional, these systems contribute to sleep loss and fragmentation as well as to sleep need. In this chapter, the implications of neural plasticity and sleep homeostasis for the diagnosis and treatment of some major sleep disorders, such as insomnia and sleep deprivation, obstructive sleep apnea syndrome, restless legs syndrome, REM sleep behavior disorder, and narcolepsy are discussed in detail with their therapeutical implications. This chapter highlights that sleep is necessary for the maintenance of an optimal brain function and is sensitive to both genetic background and environmental enrichment. Even in pathologic conditions, sleep acts as a resilient plastic state that consolidates prior information and prioritizes network activity for efficient brain functioning.

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.

Molecular Mechanisms of Memory Consolidation That Operate During Sleep

The role of sleep for brain function has been in the focus of interest for many years. It is now firmly established that sleep and the corresponding brain activity is of central importance for memory consolidation. Less clear are the underlying molecular mechanisms and their specific contribution to the formation of long-term memory. In this review, we summarize the current knowledge of such mechanisms and we discuss the several unknowns that hinder a deeper appreciation of how molecular mechanisms of memory consolidation during sleep impact synaptic function and engram formation.

CA2 inhibition reduces the precision of hippocampal assembly reactivation

The structured reactivation of hippocampal neuronal ensembles during fast synchronous oscillatory events, termed sharp-wave ripples (SWRs), has been suggested to play a crucial role in the storage and use of memory. Activity in both the CA2 and CA3 subregions can precede this population activity in CA1, and chronic inhibition of either region alters SWR oscillations. However, the precise contribution of CA2 to the oscillation, as well as to the reactivation of CA1 neurons within it, remains unclear. Here, we employ chemogenetics to transiently silence CA2 pyramidal cells in mice, and we observe that although SWRs still occur, the reactivation of CA1 pyramidal cell ensembles within the events lose both temporal and informational precision. These observations suggest that CA2 activity contributes to the fidelity of experience-dependent hippocampal replay.

Ripple band phase precession of place cell firing during replay

Neuronal “replay,” in which place cell firing during rest recapitulates recently experienced trajectories, is thought to mediate the transmission of information from hippocampus to neocortex, but the mechanism for this transmission is unknown. Here, we show that replay uses a phase code to represent spatial trajectories by the phase of firing relative to the 150- to 250-Hz “ripple” oscillations that accompany replay events. This phase code is analogous to the theta phase precession of place cell firing during navigation, in which place cells fire at progressively earlier phases of the 6- to 12-Hz theta oscillation as their place field is traversed, providing information about self-location that is additional to the rate code and a necessary precursor of replay. Thus, during replay, each ripple cycle contains a “forward sweep” of decoded locations along the recapitulated trajectory. Our results indicate a novel encoding of trajectory information during replay and implicates phase coding as a general mechanism by which the hippocampus transmits experienced and replayed sequential information to downstream targets.

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Place cells fire at successively earlier ripple band phases during replay

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Ripple band firing phase during replay encodes location within the place field

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This produces forward sweeps of place cell activity during each ripple cycle

Continuous attractors for dynamic memories

Episodic memory has a dynamic nature: when we recall past episodes, we retrieve not only their content, but also their temporal structure. The phenomenon of replay, in the hippocampus of mammals, offers a remarkable example of this temporal dynamics. However, most quantitative models of memory treat memories as static configurations, neglecting the temporal unfolding of the retrieval process. Here, we introduce a continuous attractor network model with a memory-dependent asymmetric component in the synaptic connectivity, which spontaneously breaks the equilibrium of the memory configurations and produces dynamic retrieval. The detailed analysis of the model with analytical calculations and numerical simulations shows that it can robustly retrieve multiple dynamical memories, and that this feature is largely independent of the details of its implementation. By calculating the storage capacity, we show that the dynamic component does not impair memory capacity, and can even enhance it in certain regimes.

When we recall a past experience, accessing what is known as an ‘episodic memory’, it usually does not appear as a still image or a snapshot of what occurred. Instead, our memories tend to be dynamic: we remember how a sequence of events unfolded, and when we do this, we often re-experience at least part of that same sequence. If the memory includes physical movement, the sequence combines space and time to remember a trajectory. For example, a mouse might remember how it went down a hole and found cheese there.

However, mathematical models of how past experiences are stored in our brains and retrieved when we remember them have so far focused on snapshot memories. ‘Attractor network models’ are one type of mathematical model that neuroscientists use to represent how neurons communicate with each other to store memories. These models can provide insights into how circuits of neurons, for example those in the hippocampus (a part of the brain crucial for memory), may have evolved to remember the past, but so far they have only focused on how single moments, rather than sequences of events, are represented by populations of neurons.

Spalla et al. found a way to extend these models, so they could analyse how networks of neurons can store and retrieve dynamic memories. These memories are represented in the brain as ‘continuous attractors’, which can be thought of as arrows that attract mental trajectories first to the arrow itself, and once on the arrow, to the arrowhead. Each recalled event elicits the next one on the arrow, as the mental trajectory advances towards the arrowhead. Spalla et al. determined that memory networks in the hippocampus of mammals can store large numbers of these ‘arrows’, up to the same amount of ‘snapshot’ memories predicted to be stored with similar models.

Spalla et al.’s results may allow researchers to better understand memory storage and recall, since they allow for the modelling of complex and realistic aspects of episodic memories. This could provide insights into processes such as why our minds wander, as well as having implications for the study of how neurons physically interact with each other to transmit information.

The Engram's Dark Horse: How Interneurons Regulate State-Dependent Memory Processing and Plasticity

Brain states such as arousal and sleep play critical roles in memory encoding, storage, and recall. Recent studies have highlighted the role of engram neurons-populations of neurons activated during learning-in subsequent memory consolidation and recall. These engram populations are generally assumed to be glutamatergic, and the vast majority of data regarding the function of engram neurons have focused on glutamatergic pyramidal or granule cell populations in either the hippocampus, amygdala, or neocortex. Recent data suggest that sleep and wake states differentially regulate the activity and temporal dynamics of engram neurons. Two potential mechanisms for this regulation are either via direct regulation of glutamatergic engram neuron excitability and firing, or via state-dependent effects on interneuron populations-which in turn modulate the activity of glutamatergic engram neurons. Here, we will discuss recent findings related to the roles of interneurons in state-regulated memory processes and synaptic plasticity, and the potential therapeutic implications of understanding these mechanisms.

Spiking Neural Networks and Hippocampal Function: A Web-Accessible Survey of Simulations, Modeling Methods, and Underlying Theories

Computational modeling has contributed to hippocampal research in a wide variety of ways and through a large diversity of approaches, reflecting the many advanced cognitive roles of this brain region. The intensively studied neuron type circuitry of the hippocampus is a particularly conducive substrate for spiking neural models. Here we present an online knowledge base of spiking neural network simulations of hippocampal functions. First, we overview theories involving the hippocampal formation in subjects such as spatial representation, learning, and memory. Then we describe an original literature mining process to organize published reports in various key aspects, including: (i) subject area (e.g., navigation, pattern completion, epilepsy); (ii) level of modeling detail (Hodgkin-Huxley, integrate-and-fire, etc.); and (iii) theoretical framework (attractor dynamics, oscillatory interference, self-organizing maps, and others). Moreover, every peer-reviewed publication is also annotated to indicate the specific neuron types represented in the network simulation, establishing a direct link with the Hippocampome.org portal. The web interface of the knowledge base enables dynamic content browsing and advanced searches, and consistently presents evidence supporting every annotation. Moreover, users are given access to several types of statistical reports about the collection, a selection of which is summarized in this paper. This open access resource thus provides an interactive platform to survey spiking neural network models of hippocampal functions, compare available computational methods, and foster ideas for suitable new directions of research.

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.