Deciphering Neural Codes of Memory during Sleep

A theory of hippocampal function: New developments

We develop further here the only quantitative theory of the storage of information in the hippocampal episodic memory system and its recall back to the neocortex. The theory is upgraded to account for a revolution in understanding of spatial representations in the primate, including human, hippocampus, that go beyond the place where the individual is located, to the location being viewed in a scene. This is fundamental to much primate episodic memory and navigation: functions supported in humans by pathways that build 'where' spatial view representations by feature combinations in a ventromedial visual cortical stream, separate from those for 'what' object and face information to the inferior temporal visual cortex, and for reward information from the orbitofrontal cortex. Key new computational developments include the capacity of the CA3 attractor network for storing whole charts of space; how the correlations inherent in self-organizing continuous spatial representations impact the storage capacity; how the CA3 network can combine continuous spatial and discrete object and reward representations; the roles of the rewards that reach the hippocampus in the later consolidation into long-term memory in part via cholinergic pathways from the orbitofrontal cortex; and new ways of analysing neocortical information storage using Potts networks.

Episodic long-term memory formation during slow-wave sleep

We are unresponsive during slow-wave sleep but continue monitoring external events for survival. Our brain wakens us when danger is imminent. If events are non-threatening, our brain might store them for later consideration to improve decision-making. To test this hypothesis, we examined whether novel vocabulary consisting of simultaneously played pseudowords and translation words are encoded/stored during sleep, and which neural-electrical events facilitate encoding/storage. An algorithm for brain-state-dependent stimulation selectively targeted word pairs to slow-wave peaks or troughs. Retrieval tests were given 12 and 36 hr later. These tests required decisions regarding the semantic category of previously sleep-played pseudowords. The sleep-played vocabulary influenced awake decision-making 36 hr later, if targeted to troughs. The words' linguistic processing raised neural complexity. The words' semantic-associative encoding was supported by increased theta power during the ensuing peak. Fast-spindle power ramped up during a second peak likely aiding consolidation. Hence, new vocabulary played during slow-wave sleep was stored and influenced decision-making days later.

Protosequences in human cortical organoids model intrinsic states in the developing cortex

Neuronal firing sequences are thought to be the basic building blocks of neural coding and information broadcasting within the brain. However, when sequences emerge during neurodevelopment remains unknown. We demonstrate that structured firing sequences are present in spontaneous activity of human brain organoids and ex vivo neonatal brain slices from the murine somatosensory cortex. We observed a balance between temporally rigid and flexible firing patterns that are emergent phenomena in human brain organoids and early postnatal murine somatosensory cortex, but not in primary dissociated cortical cultures. Our findings suggest that temporal sequences do not arise in an experience-dependent manner, but are rather constrained by an innate preconfigured architecture established during neurogenesis. These findings highlight the potential for brain organoids to further explore how exogenous inputs can be used to refine neuronal circuits and enable new studies into the genetic mechanisms that govern assembly of functional circuitry during early human brain development.

What risk factors for Developmental Language Disorder can tell us about the neurobiological mechanisms of language development

Language is a complex multidimensional cognitive system that is connected to many neurocognitive capacities. The development of language is therefore strongly intertwined with the development of these capacities and their neurobiological substrates. Consequently, language problems, for example those of children with Developmental Language Disorder (DLD), are explained by a variety of etiological pathways and each of these pathways will be associated with specific risk factors. In this review, we attempt to link previously described factors that may interfere with language development to putative underlying neurobiological mechanisms of language development, hoping to uncover openings for future therapeutical approaches or interventions that can help children to optimally develop their language skills.

Item-specific neural representations during human sleep support long-term memory

Understanding how individual memories are reactivated during sleep is essential in theorizing memory consolidation. Here, we employed the targeted memory reactivation (TMR) paradigm to unobtrusively replaying auditory memory cues during human participants' slow-wave sleep (SWS). Using representational similarity analysis (RSA) on cue-elicited electroencephalogram (EEG), we found temporally segregated and functionally distinct item-specific neural representations: the early post-cue EEG activity (within 0 to 2,000 ms) contained comparable item-specific representations for memory cues and control cues, signifying effective processing of auditory cues. Critically, the later EEG activity (2,500 to 2,960 ms) showed greater item-specific representations for post-sleep remembered items than for forgotten and control cues, indicating memory reprocessing. Moreover, these later item-specific neural representations were supported by concurrently increased spindles, particularly for items that had not been tested prior to sleep. These findings elucidated how external memory cues triggered item-specific neural representations during SWS and how such representations were linked to successful long-term memory. These results will benefit future research aiming to perturb specific memory episodes during sleep.

Melatonin alleviates depression-like behaviors and cognitive dysfunction in mice by regulating the circadian rhythm of AQP4 polarization

Depression is a common chronic psychiatric illness, which is resistant to medical treatments. While melatonin may alleviate certain depression symptoms, evidence for its efficacy against core symptoms is lacking. Here, we tested a mechanism whereby melatonin rescues the behavioral outcomes of the chronic unpredictable mild stress (CUMS) mouse model of depression. CUMS mice showed depressive behaviors to tail suspension, open field behavior, and sucrose preference test, and cognitive dysfunction in the Morris water maze. Impairments in these measures were relieved by melatonin treatment. Moreover, CUMS mice had impaired glymphatic function across the sleep-wake cycle due to the astrocytic loss and disturbance of circadian regulation of the polarized expression of aquaporin-4 (AQP4) water channels in perivascular astrocytes. EEG results in CUMS mice showed a reduced total sleep time and non-rapid eye movement (NREM) sleep, due to sleep fragmentation in the light phase. CUMS mice lost the normal rhythmic expressions of circadian proteins Per2, Cry2, Bmal1, Clock, and Per1. However, the melatonin treatment restored glymphatic system function and the polarization of AQP4, while improving sleep structure, and rectifying the abnormal expression of Per2, Bmal1, Clock, and Per1 in CUMS mice. Interestingly, Per2 expression correlated negatively with the polarization of AQP4. Further studies demonstrated that Per2 directed the location of AQP4 expression via interactions with the α-dystrobrevin (Dtna) subunit of AQP4 in primary cultured astrocytes. In conclusion, we report a new mechanism whereby melatonin improves depression outcomes by regulating the expression of the circadian protein Per2, maintaining the circadian rhythm of astrocytic AQP4 polarization, and restoring glymphatic function.

Hippocampal memory reactivation during sleep is correlated with specific cortical states of the retrosplenial and prefrontal cortices

Episodic memories are thought to be stabilized through the coordination of cortico-hippocampal activity during sleep. However, the timing and mechanism of this coordination remain unknown. To investigate this, we studied the relationship between hippocampal reactivation and slow-wave sleep up and down states of the retrosplenial cortex (RTC) and prefrontal cortex (PFC). We found that hippocampal reactivations are strongly correlated with specific cortical states. Reactivation occurred during sustained cortical Up states or during the transition from up to down state. Interestingly, the most prevalent interaction with memory reactivation in the hippocampus occurred during sustained up states of the PFC and RTC, while hippocampal reactivation and cortical up-to-down state transition in the RTC showed the strongest coordination. Reactivation usually occurred within 150-200 msec of a cortical Up state onset, indicating that a buildup of excitation during cortical Up state activity influences the probability of memory reactivation in CA1. Conversely, CA1 reactivation occurred 30-50 msec before the onset of a cortical down state, suggesting that memory reactivation affects down state initiation in the RTC and PFC, but the effect in the RTC was more robust. Our findings provide evidence that supports and highlights the complexity of bidirectional communication between cortical regions and the hippocampus during sleep.

Regulation of dendritic spines in the amygdala following sleep deprivation

The amygdala is a hub of emotional circuits involved in the regulation of cognitive and emotional behaviors and its critically involved in emotional reactivity, stress regulation, and fear memory. Growing evidence suggests that the amygdala plays a key role in the consolidation of emotional memories during sleep. Neuroimaging studies demonstrated that the amygdala is selectively and highly activated during rapid eye movement sleep (REM) and sleep deprivation induces emotional instability and dysregulation of the emotional learning process. Regulation of dendritic spines during sleep represents a morphological correlate of memory consolidation. Several studies indicate that dendritic spines are remodeled during sleep, with evidence for broad synaptic downscaling and selective synaptic upscaling in several cortical areas and the hippocampus. Currently, there is a lack of information regarding the regulation of dendritic spines in the amygdala during sleep. In the present work, we investigated the effect of 5 h of sleep deprivation on dendritic spines in the mouse amygdala. Our data demonstrate that sleep deprivation results in differential dendritic spine changes depending on both the amygdala subregions and the morphological subtypes of dendritic spines. We observed decreased density of mushroom spines in the basolateral amygdala of sleep deprived mice, together with increased neck length and decreased surface area and volume. In contrast, we observed greater densities of stubby spines in sleep deprived mice in the central amygdala, indicating that downscaling selectively occurs in this spine type. Greater neck diameters for thin spines in the lateral and basolateral nuclei of sleep deprived mice, and decreases in surface area and volume for mushroom spines in the basolateral amygdala compared to increases in the cental amygdala provide further support for spine type-selective synaptic downscaling in these areas during sleep. Our findings suggest that sleep promotes synaptic upscaling of mushroom spines in the basolateral amygdala, and downscaling of selective spine types in the lateral and central amygdala. In addition, we observed decreased density of phosphorylated cofilin immunoreactive and growth hormone immunoreactive cells in the amygdala of sleep deprived mice, providing further support for upscaling of dendritic spines during sleep. Overall, our findings point to region-and spine type-specific changes in dendritic spines during sleep in the amygdala, which may contribute to consolidation of emotional memories during sleep.

Decoding behavior from global cerebrovascular activity using neural networks

Functional Ultrasound (fUS) provides spatial and temporal frames of the vascular activity in the brain with high resolution and sensitivity in behaving animals. The large amount of resulting data is underused at present due to the lack of appropriate tools to visualize and interpret such signals. Here we show that neural networks can be trained to leverage the richness of information available in fUS datasets to reliably determine behavior, even from a single fUS 2D image after appropriate training. We illustrate the potential of this method with two examples: determining if a rat is moving or static and decoding the animal's sleep/wake state in a neutral environment. We further demonstrate that our method can be transferred to new recordings, possibly in other animals, without additional training, thereby paving the way for real-time decoding of brain activity based on fUS data. Finally, the learned weights of the network in the latent space were analyzed to extract the relative importance of input data to classify behavior, making this a powerful tool for neuroscientific research.

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.

Freezing revisited: coordinated autonomic and central optimization of threat coping

Animals have sophisticated mechanisms for coping with danger. Freezing is a unique state that, upon threat detection, allows evidence to be gathered, response possibilities to be previsioned and preparations to be made for worst-case fight or flight. We propose that - rather than reflecting a passive fear state - the particular somatic and cognitive characteristics of freezing help to conceal overt responses, while optimizing sensory processing and action preparation. Critical for these functions are the neurotransmitters noradrenaline and acetylcholine, which modulate neural information processing and also control the sympathetic and parasympathetic branches of the autonomic nervous system. However, the interactions between autonomic systems and the brain during freezing, and the way in which they jointly coordinate responses, remain incompletely explored. We review the joint actions of these systems and offer a novel computational framework to describe their temporally harmonized integration. This reconceptualization of freezing has implications for its role in decision-making under threat and for psychopathology.

Uncovering spatial representations from spatiotemporal patterns of rodent hippocampal field potentials

Spatiotemporal patterns of large-scale spiking and field potentials of the rodent hippocampus encode spatial representations during maze runs, immobility, and sleep. Here, we show that multisite hippocampal field potential amplitude at ultra-high-frequency band (FPAuhf), a generalized form of multiunit activity, provides not only a fast and reliable reconstruction of the rodent's position when awake, but also a readout of replay content during sharp-wave ripples. This FPAuhf feature may serve as a robust real-time decoding strategy from large-scale recordings in closed-loop experiments. Furthermore, we develop unsupervised learning approaches to extract low-dimensional spatiotemporal FPAuhf features during run and ripple periods and to infer latent dynamical structures from lower-rank FPAuhf features. We also develop an optical flow-based method to identify propagating spatiotemporal LFP patterns from multisite array recordings, which can be used as a decoding application. Finally, we develop a prospective decoding strategy to predict an animal's future decision in goal-directed navigation.

Stepwise synaptic plasticity events drive the early phase of memory consolidation

[Figure: see text].

Stability of ripple events during task engagement in human hippocampus

High-frequency activity bursts in the hippocampus, known as ripples, are thought to support memory consolidation during “offline” states, such as sleep. Recently, human hippocampal ripples have been observed during “online” episodic memory tasks. It remains unclear whether similar ripple activity occurs during other cognitive states, including different types of episodic memory. However, identifying genuine ripple events in the human hippocampus is challenging. To address these questions, spectro-temporal ripple identification was applied to human hippocampal recordings across a variety of cognitive tasks. Overall, ripple attributes were stable across tasks of visual perception and associative memory, with mean rates lower than offline states of rest and sleep. In contrast, while more complex visual attention tasks did not modulate ripple attributes, rates were enhanced for more complex autobiographical memory conditions. Therefore, hippocampal ripples reliably occur across cognitive states but are specifically enhanced during offline states and complex memory processes, consistent with a role in consolidation.

Hippocampal ripples are high-frequency activity bursts proposed to support “offline” memory consolidation. Chen et al. identify that human hippocampal ripples occur with stable properties across tasks of visual perception and associative memory but are enhanced for autobiographical memory retrieval and non-REM sleep, supporting their “online” role in establishing and strengthening memory traces.

Circadian control of brain glymphatic and lymphatic fluid flow

The glymphatic system is a network of perivascular spaces that promotes movement of cerebrospinal fluid (CSF) into the brain and clearance of metabolic waste. This fluid transport system is supported by the water channel aquaporin-4 (AQP4) localized to vascular endfeet of astrocytes. The glymphatic system is more effective during sleep, but whether sleep timing promotes glymphatic function remains unknown. We here show glymphatic influx and clearance exhibit endogenous, circadian rhythms peaking during the mid-rest phase of mice. Drainage of CSF from the cisterna magna to the lymph nodes exhibits daily variation opposite to glymphatic influx, suggesting distribution of CSF throughout the animal depends on time-of-day. The perivascular polarization of AQP4 is highest during the rest phase and loss of AQP4 eliminates the day-night difference in both glymphatic influx and drainage to the lymph nodes. We conclude that CSF distribution is under circadian control and that AQP4 supports this rhythm.

Efficient Position Decoding Methods Based on Fluorescence Calcium Imaging in the Mouse Hippocampus

Large-scale fluorescence calcium imaging methods have become widely adopted for studies of long-term hippocampal and cortical neuronal dynamics. Pyramidal neurons of the rodent hippocampus show spatial tuning in freely foraging or head-fixed navigation tasks. Development of efficient neural decoding methods for reconstructing the animal's position in real or virtual environments can provide a fast readout of spatial representations in closed-loop neuroscience experiments. Here, we develop an efficient strategy to extract features from fluorescence calcium imaging traces and further decode the animal's position. We validate our spike inference-free decoding methods in multiple in vivo calcium imaging recordings of the mouse hippocampus based on both supervised and unsupervised decoding analyses. We systematically investigate the decoding performance of our proposed methods with respect to the number of neurons, imaging frame rate, and signal-to-noise ratio. Our proposed supervised decoding analysis is ultrafast and robust, and thereby appealing for real-time position decoding applications based on calcium imaging.

Drosophila as a Model to Study the Relationship Between Sleep, Plasticity, and Memory

Humans spend nearly a third of their life sleeping, yet, despite decades of research the function of sleep still remains a mystery. Sleep has been linked with various biological systems and functions, including metabolism, immunity, the cardiovascular system, and cognitive functions. Importantly, sleep appears to be present throughout the animal kingdom suggesting that it must provide an evolutionary advantage. Among the many possible functions of sleep, the relationship between sleep, and cognition has received a lot of support. We have all experienced the negative cognitive effects associated with a night of sleep deprivation. These can include increased emotional reactivity, poor judgment, deficit in attention, impairment in learning and memory, and obviously increase in daytime sleepiness. Furthermore, many neurological diseases like Alzheimer’s disease often have a sleep disorder component. In some cases, the sleep disorder can exacerbate the progression of the neurological disease. Thus, it is clear that sleep plays an important role for many brain functions. In particular, sleep has been shown to play a positive role in the consolidation of long-term memory while sleep deprivation negatively impacts learning and memory. Importantly, sleep is a behavior that is adapted to an individual’s need and influenced by many external and internal stimuli. In addition to being an adaptive behavior, sleep can also modulate plasticity in the brain at the level of synaptic connections between neurons and neuronal plasticity influences sleep. Understanding how sleep is modulated by internal and external stimuli and how sleep can modulate memory and plasticity is a key question in neuroscience. In order to address this question, several animal models have been developed. Among them, the fruit fly Drosophila melanogaster with its unparalleled genetics has proved to be extremely valuable. In addition to sleep, Drosophila has been shown to be an excellent model to study many complex behaviors, including learning, and memory. This review describes our current knowledge of the relationship between sleep, plasticity, and memory using the fly model.

Up and Down States and Memory Consolidation Across Somatosensory, Entorhinal, and Hippocampal Cortices

In the course of a day, brain states fluctuate, from conscious awake information-acquiring states to sleep states, during which previously acquired information is further processed and stored as memories. One hypothesis is that memories are consolidated and stored during "offline" states such as sleep, a process thought to involve transfer of information from the hippocampus to other cortical areas. Up and Down states (UDS), patterns of activity that occur under anesthesia and sleep states, are likely to play a role in this process, although the nature of this role remains unclear. Here we review what is currently known about these mechanisms in three anatomically distinct but interconnected cortical areas: somatosensory cortex, entorhinal cortex, and the hippocampus. In doing so, we consider the role of this activity in the coordination of "replay" during sleep states, particularly during hippocampal sharp-wave ripples. We conclude that understanding the generation and propagation of UDS may provide key insights into the cortico-hippocampal dialogue linking archi- and neocortical areas during memory formation.

Altered sleep architecture, rapid eye movement sleep, and neural oscillation in a mouse model of human chromosome 16p11.2 microdeletion

Sleep abnormalities are common among children with neurodevelopmental disorders. The human chr16p11.2 microdeletion is associated with a range of neurological and neurobehavioral abnormalities. Previous studies of a mouse model of human chr16p11.2 microdeletion (chr16p11.2df/+) have demonstrated pathophysiological changes at the synapses in the hippocampus and striatum; however, the impact of this genetic abnormality on system level brain functions, such as sleep and neural oscillation, has not been adequately investigated. Here, we show that chr16p11.2df/+ mice have altered sleep architecture, with increased wake time and reduced time in rapid eye movement (REM) and non-REM (NREM) sleep. Importantly, several measurements of REM sleep are significantly changed in deletion mice. The REM bout number and the bout number ratio of REM to NREM are decreased in mutant mice, suggesting a deficit in REM-NREM transition. The average REM bout duration is shorter in mutant mice, indicating a defect in REM maintenance. In addition, whole-cell patch clamp recording of the ventrolateral periaqueductal gray (vlPAG)-projecting gamma-aminobutyric acid (GABA)ergic neurons in the lateral paragigantocellular nucleus of ventral medulla of mutant mice reveal that these neurons, which are important for NREM-REM transition and REM maintenance, have hyperpolarized resting membrane potential and increased membrane resistance. These changes in intrinsic membrane properties suggest that these projection-specific neurons of mutant mice are less excitable, and thereby may play a role in deficient NREM-REM transition and REM maintenance. Furthermore, mutant mice exhibit changes in neural oscillation involving multiple frequency classes in several vigilance states. The most significant alterations occur in the theta frequency during wake and REM sleep.

Neural circuits linking sleep and addiction: Animal models to understand why select individuals are more vulnerable to substance use disorders after sleep deprivation

Individuals differ widely in their drug-craving behaviors. One reason for these differences involves sleep. Sleep disturbances lead to an increased risk of substance use disorders and relapse in only some individuals. While animal studies have examined the impact of sleep on reward circuitry, few have addressed the role of individual differences in the effects of altered sleep. There does, however, exist a rodent model of individual differences in reward-seeking behavior: the sign/goal-tracker model of Pavlovian conditioned approach. In this model, only some rats show the key behavioral traits associated with addiction, including impulsivity and poor attentional control, making this an ideal model system to examine individually distinct sleep-reward interactions. Here, we describe how the limbic neural circuits responsible for individual differences in incentive motivation overlap with those involved in sleep-wake regulation, and how this model can elucidate the common underlying mechanisms. Consideration of individual differences in preclinical models would improve our understanding of how sleep interacts with motivational systems, and why sleep deprivation contributes to addiction in only select individuals.

Unsupervised Detection of Cell-Assembly Sequences by Similarity-Based Clustering

Neurons which fire in a fixed temporal pattern (i.e., "cell assemblies") are hypothesized to be a fundamental unit of neural information processing. Several methods are available for the detection of cell assemblies without a time structure. However, the systematic detection of cell assemblies with time structure has been challenging, especially in large datasets, due to the lack of efficient methods for handling the time structure. Here, we show a method to detect a variety of cell-assembly activity patterns, recurring in noisy neural population activities at multiple timescales. The key innovation is the use of a computer science method to comparing strings ("edit similarity"), to group spikes into assemblies. We validated the method using artificial data and experimental data, which were previously recorded from the hippocampus of male Long-Evans rats and the prefrontal cortex of male Brown Norway/Fisher hybrid rats. From the hippocampus, we could simultaneously extract place-cell sequences occurring on different timescales during navigation and awake replay. From the prefrontal cortex, we could discover multiple spike sequences of neurons encoding different segments of a goal-directed task. Unlike conventional event-driven statistical approaches, our method detects cell assemblies without creating event-locked averages. Thus, the method offers a novel analytical tool for deciphering the neural code during arbitrary behavioral and mental processes.

Dynamic Network Activation of Hypothalamic MCH Neurons in REM Sleep and Exploratory Behavior

Most brain neurons are active in waking, but hypothalamic neurons that synthesize the neuropeptide melanin-concentrating hormone (MCH) are claimed to be active only during sleep, particularly rapid eye movement (REM) sleep. Here we use deep-brain imaging to identify changes in fluorescence of the genetically encoded calcium (Ca²⁺) indicator GCaMP6 in individual hypothalamic neurons that contain MCH. An in vitro electrophysiology study determined a strong relationship between depolarization and Ca²⁺ fluorescence in MCH neurons. In 10 freely behaving MCH-cre mice (male and female), the highest fluorescence occurred in all recorded neurons (n = 106) in REM sleep relative to quiet waking or non-REM sleep. Unexpectedly, 70% of the MCH neurons had strong fluorescence activity when the mice explored novel objects. Spatial and temporal mapping of the change in fluorescence between pairs of MCH neurons revealed dynamic activation of MCH neurons during REM sleep and activation of a subset of the same neurons during exploratory behavior. Functional network activity maps will facilitate comparisons of not only single-neuron activity, but also network responses in different conditions and disease.SIGNIFICANCE STATEMENT Functional activity maps identify brain circuits responding to specific behaviors, including rapid eye movement sleep (REM sleep), a sleep phase when the brain is as active as in waking. To provide the first activity map of individual neurons during REM sleep, we use deep-brain calcium imaging in unrestrained mice to map the activity of hypothalamic melanin-concentrating hormone (MCH) neurons. MCH neurons were found to be synchronously active during REM sleep, and also during the exploration of novel objects. Spatial mapping revealed dynamic network activation during REM sleep and activation of a subset of the neurons during exploratory behavior. Functional activity maps at the cellular level in specific behaviors, including sleep, are needed to establish a brain connectome.

How Memory Replay in Sleep Boosts Creative Problem-Solving

Creative thought relies on the reorganisation of existing knowledge. Sleep is known to be important for creative thinking, but there is a debate about which sleep stage is most relevant, and why. We address this issue by proposing that rapid eye movement sleep, or 'REM', and non-REM sleep facilitate creativity in different ways. Memory replay mechanisms in non-REM can abstract rules from corpuses of learned information, while replay in REM may promote novel associations. We propose that the iterative interleaving of REM and non-REM across a night boosts the formation of complex knowledge frameworks, and allows these frameworks to be restructured, thus facilitating creative thought. We outline a hypothetical computational model which will allow explicit testing of these hypotheses.

Efficient Decoding of Multi-Dimensional Signals From Population Spiking Activity Using a Gaussian Mixture Particle Filter

New recording technologies and the potential for closed-loop experiments have led to an increasing demand for computationally efficient and accurate algorithms to decode population spiking activity in multi-dimensional spaces. Exact point process filters can accurately decode low-dimensional signals, but are computationally intractable for high-dimensional signals. Approximate Gaussian filters are computationally efficient, but are inaccurate when the signals have complex distributions and nonlinear dynamics. Even particle filter methods tend to become inefficient and inaccurate when the filter distribution has multiple peaks. Here, we develop a new point process filter algorithm that combines the computational efficiency of approximate Gaussian methods with a numerical accuracy that exceeds standard particle filters. We use a mixture of Gaussian model for the posterior at each time step, allowing for an analytic solution to the computationally expensive filter integration step. During non-spike intervals, the filter needs only to update the mean, covariance, and mixture weight of each component. At spike times, a sampling procedure is used to update the filtering distribution and find the number of Gaussian mixture components necessary to maintain an accurate approximation. We illustrate the application of this algorithm to the problem of decoding a rat's position and velocity in a maze from hippocampal place cell data using both 2-D and 4-D decoders.

Dynamics of motor cortical activity during naturalistic feeding behavior

OBJECTIVE

The orofacial primary motor cortex (MIo) plays a critical role in controlling tongue and jaw movements during oral motor functions, such as chewing, swallowing and speech. However, the neural mechanisms of MIo during naturalistic feeding are still poorly understood. There is a strong need for a systematic study of motor cortical dynamics during feeding behavior.

APPROACH

To investigate the neural dynamics and variability of MIo neuronal activity during naturalistic feeding, we used chronically implanted micro-electrode arrays to simultaneously recorded ensembles of neuronal activity in the MIo of two monkeys (Macaca mulatta) while eating various types of food. We developed a Bayesian nonparametric latent variable model to reveal latent structures of neuronal population activity of the MIo and identify the complex mapping between MIo ensemble spike activity and high-dimensional kinematics.

MAIN RESULTS

Rhythmic neuronal firing patterns and oscillatory dynamics are evident in single-unit activity. At the population level, we uncovered the neural dynamics of rhythmic chewing, and quantified the neural variability at multiple timescales (complete feeding sequences, chewing sequence stages, chewing gape cycle phases) across food types. Our approach accommodates time-warping of chewing sequences and automatic model selection, and maps the latent states to chewing behaviors at fine timescales.

SIGNIFICANCE

Our work shows that neural representations of MIo ensembles display spatiotemporal patterns in chewing gape cycles at different chew sequence stages, and these patterns vary in a stage-dependent manner. Unsupervised learning and decoding analysis may reveal the link between complex MIo spatiotemporal patterns and chewing kinematics.

Local hippocampal fast gamma rhythms precede brain-wide hyperemic patterns during spontaneous rodent REM sleep

Rapid eye movement sleep (REMS) is a peculiar brain state combining the behavioral components of sleep and the electrophysiological profiles of wake. After decades of research our understanding of REMS still is precluded by the difficulty to observe its spontaneous dynamics and the lack of multimodal recording approaches to build comprehensive datasets. We used functional ultrasound (fUS) imaging concurrently with extracellular recordings of local field potentials (LFP) to reveal brain-wide spatiotemporal hemodynamics of single REMS episodes. We demonstrate for the first time the close association between global hyperemic events – largely outmatching wake levels in most brain regions – and local hippocampal theta (6–10 Hz) and fast gamma (80–110 Hz) events in the CA1 region. In particular, the power of fast gamma oscillations strongly correlated with the amplitude of subsequent vascular events. Our findings challenge our current understanding of neurovascular coupling and question the evolutionary benefit of such energy-demanding patterns in REMS function.

Neural activity during REM sleep is similar to the waking state. Here, the authors measure blood volume with neurofunctional ultrasound imaging together with hippocampal neural activity during REM sleep and report that fast gamma oscillations are coupled to a brain-wide upregulation of vascular flow.

Real-Time Readout of Large-Scale Unsorted Neural Ensemble Place Codes

Uncovering spatial representations from large-scale ensemble spike activity in specific brain circuits provides valuable feedback in closed-loop experiments. We develop a graphics processing unit (GPU)-powered population-decoding system for ultrafast reconstruction of spatial positions from rodents' unsorted spatiotemporal spiking patterns, during run behavior or sleep. In comparison with an optimized quad-core central processing unit (CPU) implementation, our approach achieves an ∼20- to 50-fold increase in speed in eight tested rat hippocampal, cortical, and thalamic ensemble recordings, with real-time decoding speed (approximately fraction of a millisecond per spike) and scalability up to thousands of channels. By accommodating parallel shuffling in real time (computation time <15 ms), our approach enables assessment of the statistical significance of online-decoded "memory replay" candidates during quiet wakefulness or sleep. This open-source software toolkit supports the decoding of spatial correlates or content-triggered experimental manipulation in closed-loop neuroscience experiments.

Hippocampal replays under the scrutiny of reinforcement learning models

Multiple in vivo studies have shown that place cells from the hippocampus replay previously experienced trajectories. These replays are commonly considered to mainly reflect memory consolidation processes. Some data, however, have highlighted a functional link between replays and reinforcement learning (RL). This theory, extensively used in machine learning, has introduced efficient algorithms and can explain various behavioral and physiological measures from different brain regions. RL algorithms could constitute a mechanistic description of replays and explain how replays can reduce the number of iterations required to explore the environment during learning. We review the main findings concerning the different hippocampal replay types and the possible associated RL models (either model-based, model-free, or hybrid model types). We conclude by tying these frameworks together. We illustrate the link between data and RL through a series of model simulations. This review, at the frontier between informatics and biology, paves the way for future work on replays.

Local sleep

The historic sleep regulatory paradigm invokes "top-down" imposition of sleep on the brain by sleep regulatory circuits. While remaining conceptually useful, many sleep phenomena are difficult to explain using that paradigm, including, unilateral sleep, sleep-walking, and poor performance after sleep deprivation. Further, all animals sleep after non-lethal brain lesions, regardless of whether the lesion includes sleep regulatory circuits, suggesting that sleep is a fundamental property of small viable neuronal/glial networks. That small areas of the brain can exhibit non-rapid eye movement sleep-like states is summarized. Further, sleep-like states in neuronal/glial cultures are described. The local sleep states, whether in vivo or in vitro, share electrophysiological properties and molecular regulatory components with whole animal sleep and exhibit sleep homeostasis. The molecular regulatory components of sleep are also involved in plasticity and inflammation. Like sleep, these processes, are initiated by local cell-activity dependent events, yet have at higher levels of tissue organization whole body functions. While there are large literatures dealing with local initiation and regulation of plasticity and inflammation, the literature surrounding local sleep is in its infancy and clinical applications of the local sleep concept are absent. Regardless, the local use-dependent sleep paradigm can advise and advance future research and clinical applications.

Theta Phase-Coordinated Memory Reactivation Reoccurs in a Slow-Oscillatory Rhythm during NREM Sleep

It has been proposed that sleep's contribution to memory consolidation is to reactivate prior encoded information. To elucidate the neural mechanisms carrying reactivation-related mnemonic information, we investigated whether content-specific memory signatures associated with memory reactivation during wakefulness reoccur during subsequent sleep. We show that theta oscillations orchestrate the reactivation of memories during both wakefulness and sleep. Reactivation patterns during sleep autonomously re-emerged at a rate of ∼1 Hz, indicating a coordination by slow oscillatory activity.

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

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

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

Cortical circuit activity underlying sleep slow oscillations and spindles

Slow oscillations and sleep spindles are hallmarks of the EEG during slow-wave sleep (SWS). Both oscillatory events, especially when co-occurring in the constellation of spindles nesting in the slow oscillation upstate, are considered to support memory formation and underlying synaptic plasticity. The regulatory mechanisms of this function at the circuit level are poorly understood. Here, using two-photon imaging in mice, we relate EEG-recorded slow oscillations and spindles to calcium signals recorded from the soma of cortical putative pyramidal-like (Pyr) cells and neighboring parvalbumin-positive interneurons (PV-Ins) or somatostatin-positive interneurons (SOM-Ins). Pyr calcium activity was increased more than threefold when spindles co-occurred with slow oscillation upstates compared with slow oscillations or spindles occurring in isolation. Independent of whether or not a spindle was nested in the slow oscillation upstate, the slow oscillation downstate was preceded by enhanced calcium signal in SOM-Ins that vanished during the upstate, whereas spindles were associated with strongly increased PV-In calcium activity. Additional wide-field calcium imaging of Pyr cells confirmed the enhanced calcium activity and its widespread topography associated with spindles nested in slow oscillation upstates. In conclusion, when spindles are nested in slow oscillation upstates, maximum Pyr activity appears to concur with strong perisomatic inhibition of Pyr cells via PV-Ins and low dendritic inhibition via SOM-Ins (i.e., conditions that might optimize synaptic plasticity within local cortical circuits).

Methods for Assessment of Memory Reactivation

It has been suggested that reactivation of previously acquired experiences or stored information in declarative memories in the hippocampus and neocortex contributes to memory consolidation and learning. Understanding memory consolidation depends crucially on the development of robust statistical methods for assessing memory reactivation. To date, several statistical methods have seen established for assessing memory reactivation based on bursts of ensemble neural spike activity during offline states. Using population-decoding methods, we propose a new statistical metric, the weighted distance correlation, to assess hippocampal memory reactivation (i.e., spatial memory replay) during quiet wakefulness and slow-wave sleep. The new metric can be combined with an unsupervised population decoding analysis, which is invariant to latent state labeling and allows us to detect statistical dependency beyond linearity in memory traces. We validate the new metric using two rat hippocampal recordings in spatial navigation tasks. Our proposed analysis framework may have a broader impact on assessing memory reactivations in other brain regions under different behavioral tasks.

The dynamic nature of fear engrams in the basolateral amygdala

Great progress has been made in our understanding of how so-called memory engrams in the brain enable the storage and retrieval of memories. This has led to the realization that across the lifetime of an animal, the spatial and temporal properties of a memory engram are not fixed, but instead are subjected to dynamic modifications that can be both dependent and independent on additional experiences. The dynamic nature of engrams is especially relevant in the case of fear memories, whose contributions to an animal's evolutionary fitness depend on a delicate balance of stability and flexibility. Though fear memories have the potential to last a lifetime, their expression also needs to be properly tuned to prevent maladaptive behavior, such as seen in patients with post-traumatic stress disorder. To achieve this balance, fear engrams are subjected to complex spatiotemporal dynamics, making them informative examples of the "dynamic engram". In this review, we discuss the current understanding of the dynamic nature of fear engrams in the basolateral amygdala, a brain region that plays a central role in fear memory encoding and expression. We propose that this understanding can be further advanced by studying how fast dynamics, such as oscillatory circuit activity, support the storage and retrieval of fear engrams that can be stable over long time intervals.

Uncovering temporal structure in hippocampal output patterns

Place cell activity of hippocampal pyramidal cells has been described as the cognitive substrate of spatial memory. Replay is observed during hippocampal sharp-wave-ripple-associated population burst events (PBEs) and is critical for consolidation and recall-guided behaviors. PBE activity has historically been analyzed as a phenomenon subordinate to the place code. Here, we use hidden Markov models to study PBEs observed in rats during exploration of both linear mazes and open fields. We demonstrate that estimated models are consistent with a spatial map of the environment, and can even decode animals’ positions during behavior. Moreover, we demonstrate the model can be used to identify hippocampal replay without recourse to the place code, using only PBE model congruence. These results suggest that downstream regions may rely on PBEs to provide a substrate for memory. Additionally, by forming models independent of animal behavior, we lay the groundwork for studies of non-spatial memory.

High-performance, inexpensive setup for simultaneous multisite recording of electrophysiological signals and mesoscale voltage imaging in the mouse cortex

Simultaneous recording of optical and electrophysiological signals from multiple cortical areas may provide crucial information to expand our understanding of cortical function. However, the insertion of multiple electrodes into the brain may compromise optical imaging by both restricting the field of view and interfering with the approaches used to stabilize the specimen. Existing methods that combine electrophysiological recording and optical imaging in vivo implement either multiple surface electrodes, silicon probes, or a single electrode for deeper recordings. To address such limitation, we built a microelectrode array (hyperdrive, patent US5928143 A) compatible with wide-field imaging that allows insertion of up to 12 probes into a large brain area (8 mm diameter). The hyperdrive is comprised of a circle of individual microdrives where probes are positioned at an angle leaving a large brain area unobstructed for wide-field imaging. Multiple tetrodes and voltage-sensitive dye imaging were used for acute simultaneous registration of spontaneous and evoked cortical activity in anesthetized mice. The electrophysiological signals were used to extract local field potential (LFP) traces, multiunit, and single-unit spiking activity. To demonstrate our approach, we compared LFP and VSD signals over multiple regions of the cortex and analyzed the relationship between single-unit and global cortical population activities. The study of the interactions between cortical activity at local and global scales, such as the one presented in this work, can help to expand our knowledge of brain function.

NLGN1 and NLGN2 in the prefrontal cortex: their role in memory consolidation and strengthening

The prefrontal cortex (PFC) is critical for memory formation, but the underlying molecular mechanisms are poorly understood. Clinical and animal model studies have shown that changes in PFC excitation and inhibition are important for cognitive functions as well as related disorders. Here, we discuss recent findings revealing the roles of the excitatory and inhibitory synaptic proteins neuroligin 1 (NLGN1) and NLGN2 in the PFC in memory formation and modulation of memory strength. We propose that shifts in NLGN1 and NLGN2 expression in specific excitatory and inhibitory neuronal subpopulations in response to experience regulate the dynamic processes of memory consolidation and strengthening. Because excitatory/inhibitory imbalances accompany neuropsychiatric disorders in which strength and flexibility of representations play important roles, understanding these mechanisms may suggest novel therapies.