Bidirectional Interaction of Hippocampal Ripples and Cortical Slow Waves Leads to Coordinated Spiking Activity During NREM Sleep

A proposed role for electrical coupling in the neocortical slow oscillation

We constructed a computational thalamocortical network model for study of the neocortical slow oscillation. It incorporated a number of neuronal types, both excitatory and inhibitory, each model neuron simulated as a multicompartment entity with numerous membrane conductances. As in previous experimental and modeling studies, simulated slow oscillations primarily depended on recurrently connected deep intrinsic bursting (IB) pyramidal cells, with NMDA receptors being critical as well as intrinsic membrane conductances (e.g. persistent Na+); and with repolarization to the Down state dependent on intrinsic (slow Ca2+-dependent K+) and synaptic (GABAB receptor mediated) conductances. Furthermore, however, we now can account for additional features of the slow oscillation: the frequent occurrence of spikelets, the presence of very fast ripple-like oscillations, and the transition to so-called fast runs (10 to ∼20 Hz bursty oscillations). These latter phenomena depended in our model on electrical coupling via gap junctions between pyramidal neurons. The importance of gap junctions is supported by previous experimental data on the ripple-blocking effect of halothane, as well as by data from the in vitro hippocampus.

Sleep Alterations and Cognitive Decline

Sleep disturbances and cognitive decline are intricately connected, and both are prevalent in aging populations and individuals with neurodegenerative disorders such as Alzheimer's disease (AD) and other dementias. Sleep is vital for cognitive functions including memory consolidation, executive function, and attention. Disruption in these processes is associated with cognitive decline, although causal evidence is mixed. This review delves into the bidirectional relationship between alterations in sleep and cognitive impairment, exploring key mechanisms such as amyloid-β accumulation, tau pathology, synaptic homeostasis, neurotransmitter dysregulation, oxidative stress, and vascular contributions. Evidence from both experimental research and population-based studies underscores the necessity of early interventions targeting sleep to mitigate risks of neurodegenerative diseases. A deeper understanding of the interplay between sleep and cognitive health may pave the way for innovative strategies to prevent or reduce cognitive decline through improved sleep management.

Topography of putative bi-directional interaction between hippocampal sharp-wave ripples and neocortical slow oscillations

Systems consolidation relies on coordination between hippocampal sharp-wave ripples (SWRs) and neocortical UP/DOWN states during sleep. However, whether this coupling exists across the neocortex and the mechanisms enabling it remains unknown. By combining electrophysiology in mouse hippocampus (HPC) and retrosplenial cortex (RSC) with wide-field imaging of the dorsal neocortex, we found spatially and temporally precise bi-directional hippocampo-neocortical interaction. HPC multi-unit activity and SWR probability were correlated with UP/DOWN states in the default mode network (DMN), with the highest modulation by the RSC in deep sleep. Further, some SWRs were preceded by the high rebound excitation accompanying DMN DOWN → UP transitions, whereas large-amplitude SWRs were often followed by DOWN states originating in the RSC. We explain these electrophysiological results with a model in which the HPC and RSC are weakly coupled excitable systems capable of bi-directional perturbation and suggest that the RSC may act as a gateway through which SWRs can perturb downstream cortical regions via cortico-cortical propagation of DOWN states.

Topography of putative bidirectional interaction between hippocampal sharp wave ripples and neocortical slow oscillations

Systems consolidation relies on coordination between hippocampal sharp-wave ripples (SWRs) and neocortical UP/DOWN states during sleep. However, whether this coupling exists across neocortex and the mechanisms enabling it remain unknown. By combining electrophysiology in mouse hippocampus (HPC) and retrosplenial cortex (RSC) with widefield imaging of dorsal neocortex, we found spatially and temporally precise bidirectional hippocampo-neocortical interaction. HPC multi-unit activity and SWR probability was correlated with UP/DOWN states in mouse default mode network, with highest modulation by RSC in deep sleep. Further, some SWRs were preceded by the high rebound excitation accompanying DMN DOWN→UP transitions, while large-amplitude SWRs were often followed by DOWN states originating in RSC. We explain these electrophysiological results with a model in which HPC and RSC are weakly coupled excitable systems capable of bi-directional perturbation and suggest RSC may act as a gateway through which SWRs can perturb downstream cortical regions via cortico-cortical propagation of DOWN states.

Coordinated NREM sleep oscillations among hippocampal subfields modulate synaptic plasticity in humans

The integration of hippocampal oscillations during non-rapid eye movement (NREM) sleep is crucial for memory consolidation. However, how cardinal sleep oscillations bind across various subfields of the human hippocampus to promote information transfer and synaptic plasticity remains unclear. Using human intracranial recordings from 25 epilepsy patients, we find that hippocampal subfields, including DG/CA3, CA1, and SUB, all exhibit significant delta and spindle power during NREM sleep. The DG/CA3 displays strong coupling between delta and ripple oscillations with all the other hippocampal subfields. In contrast, the regions of CA1 and SUB exhibit more precise coordination, characterized by event-level triple coupling between delta, spindle, and ripple oscillations. Furthermore, we demonstrate that the synaptic plasticity within the hippocampal circuit, as indexed by delta-wave slope, is linearly modulated by spindle power. In contrast, ripples act as a binary switch that triggers a sudden increase in delta-wave slope. Overall, these results suggest that different subfields of the hippocampus regulate one another through diverse layers of sleep oscillation synchronization, collectively facilitating information processing and synaptic plasticity during NREM sleep.

Feasibility of a Personal Neuromorphic Emulation

The representation of intelligence is achieved by patterns of connections among neurons in brains and machines. Brains grow continuously, such that their patterns of connections develop through activity-dependent specification, with the continuing ontogenesis of individual experience. The theory of active inference proposes that the developmental organization of sentient systems reflects general processes of informatic self-evidencing, through the minimization of free energy. We interpret this theory to imply that the mind may be described in information terms that are not dependent on a specific physical substrate. At a certain level of complexity, self-evidencing of living (self-organizing) information systems becomes hierarchical and reentrant, such that effective consciousness emerges as the consequence of a good regulator. We propose that these principles imply that an adequate reconstruction of the computational dynamics of an individual human brain/mind is possible with sufficient neuromorphic computational emulation.

Python/NEURON code for simulating biophysically realistic thalamocortical dynamics during sleep

Understanding the function of sleep and its associated neural rhythms is an important goal in neuroscience. While many theoretical models of neural dynamics during sleep exist, few include the effects of neuromodulators on sleep oscillations and describe transitions between sleep and wake states or different sleep stages. Here, we started with a C++-based thalamocortical network model that describes characteristic thalamic and cortical oscillations specific to sleep. This model, which includes a biophysically realistic description of intrinsic and synaptic channels, allows for testing the effects of different neuromodulators, intrinsic cell properties, and synaptic connectivity on neural dynamics during sleep. We present a complete reimplementation of this previously-published sleep model in the standardized NEURON/Python framework, making it more accessible to the wider scientific community.

Hippocampal replay sequence governed by spontaneous brain-wide dynamics

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

Single closed-loop acoustic stimulation targeting memory consolidation suppressed hippocampal ripple and thalamo-cortical spindle activity in mice

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

Progress on the hippocampal circuits and functions based on sharp wave ripples

Sharp wave ripples (SWRs) are high-frequency synchronization events generated by hippocampal neuronal circuits during various forms of learning and reactivated during memory consolidation and recall. There is mounting evidence that SWRs are essential for storing spatial and social memories in rodents and short-term episodic memories in humans. Sharp wave ripples originate mainly from the hippocampal CA3 and subiculum, and can be transmitted to modulate neuronal activity in cortical and subcortical regions for long-term memory consolidation and behavioral guidance. Different hippocampal subregions have distinct functions in learning and memory. For instance, the dorsal CA1 is critical for spatial navigation, episodic memory, and learning, while the ventral CA1 and dorsal CA2 may work cooperatively to store and consolidate social memories. Here, we summarize recent studies demonstrating that SWRs are essential for the consolidation of spatial, episodic, and social memories in various hippocampal-cortical pathways, and review evidence that SWR dysregulation contributes to cognitive impairments in neurodegenerative and neurodevelopmental diseases.

Augmenting hippocampal-prefrontal neuronal synchrony during sleep enhances memory consolidation in humans

Memory consolidation during sleep is thought to depend on the coordinated interplay between cortical slow waves, thalamocortical sleep spindles and hippocampal ripples, but direct evidence is lacking. Here, we implemented real-time closed-loop deep brain stimulation in human prefrontal cortex during sleep and tested its effects on sleep electrophysiology and on overnight consolidation of declarative memory. Synchronizing the stimulation to the active phases of endogenous slow waves in the medial temporal lobe (MTL) enhanced sleep spindles, boosted locking of brain-wide neural spiking activity to MTL slow waves, and improved coupling between MTL ripples and thalamocortical oscillations. Furthermore, synchronized stimulation enhanced the accuracy of recognition memory. By contrast, identical stimulation without this precise time-locking was not associated with, and sometimes even degraded, these electrophysiological and behavioral effects. Notably, individual changes in memory accuracy were highly correlated with electrophysiological effects. Our results indicate that hippocampo-thalamocortical synchronization during sleep causally supports human memory consolidation.

Multielectrode Cortical Stimulation Selectively Induces Unidirectional Wave Propagation of Excitatory Neuronal Activity in Biophysical Neural Model

Cortical stimulation is emerging as an experimental tool in basic research and a promising therapy for a range of neuropsychiatric conditions. As multielectrode arrays enter clinical practice, the possibility of using spatiotemporal patterns of electrical stimulation to induce desired physiological patterns has become theoretically possible, but in practice can only be implemented by trial-and-error because of a lack of predictive models. Experimental evidence increasingly establishes traveling waves as fundamental to cortical information-processing, but we lack an understanding of how to control wave properties despite rapidly improving technologies. This study uses a hybrid biophysical-anatomical and neural-computational model to predict and understand how a simple pattern of cortical surface stimulation could induce directional traveling waves via asymmetric activation of inhibitory interneurons. We found that pyramidal cells and basket cells are highly activated by the anodal electrode and minimally activated by the cathodal electrodes, while Martinotti cells are moderately activated by both electrodes but exhibit a slight preference for cathodal stimulation. Network model simulations found that this asymmetrical activation results in a traveling wave in superficial excitatory cells that propagates unidirectionally away from the electrode array. Our study reveals how asymmetric electrical stimulation can easily facilitate traveling waves by relying on two distinct types of inhibitory interneuron activity to shape and sustain the spatiotemporal dynamics of endogenous local circuit mechanisms.SIGNIFICANCE STATEMENT Electrical brain stimulation is becoming increasingly useful to probe the workings of brain and to treat a variety of neuropsychiatric disorders. However, stimulation is currently performed in a trial-and-error fashion as there are no methods to predict how different electrode arrangements and stimulation paradigms will affect brain functioning. In this study, we demonstrate a hybrid modeling approach, which makes experimentally testable predictions that bridge the gap between the microscale effects of multielectrode stimulation and the resultant circuit dynamics at the mesoscale. Our results show how custom stimulation paradigms can induce predictable, persistent changes in brain activity, which has the potential to restore normal brain function and become a powerful therapy for neurological and psychiatric conditions.

Sleep sharp wave ripple and its functions in memory and synaptic plasticity

Memory is one of the fundamental cognitive functions of brain. The formation and consolidation of memory depend on the hippocampus and sleep. Sharp wave ripple (SWR) is an electrophysiological event which is most frequently observed in the hippocampus during sleep. It represents a highly synchronized neuronal activity pattern which modulates numerous brain regions including the neocortex, subcortical areas, and the hippocampus itself. In this review, we discuss how SWRs link experiences to memories and what happens in the hippocampus and other brain regions during sleep by focusing on synaptic plasticity.

SPEED RESPONSE AFTER STRENGTH TRAINING IN CHINESE BOXING ATHLETES

ABSTRACT Introduction: Chinese boxing demands the maximum potential from its practitioners. The quality of executions of single or multiple punches depends on adequate strength training, and the quality of speed is a crucial factor for victory in Chinese boxing competitions. Objective: Explore the effects of strength training on the speed of punches in Chinese boxing athletes. Methods: Eighteen Chinese boxers were selected by random sampling. The competitors perform functional exercises directed at strength training for ten weeks. Several key points of sports aerobics were comprehensively evaluated. Mathematical and statistical methods were applied in processing the data before and after the intervention. Results: The speed quality of Chinese boxing practitioners after strength training was statistically beneficial (P<0.05). Only the particular qualities of strength did not show significant differences among the four unique qualities of strength analyzed; there were significant differences among the other three abilities (P<0.05). Conclusion: The introduction of functional strength training into the physical training of high-level Chinese boxing athletes can significantly improve the quality of movement speed. Functional strength exercises are a safe and efficient form of exercise. This training protocol provides a good reference to compensate for the shortfall in conventional strength exercises. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

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

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

Pathological changes of brain oscillations following ischemic stroke

Brain oscillations recorded in the extracellular space are among the most important aspects of neurophysiology data reflecting the activity and function of neurons in a population or a network. The signal strength and patterns of brain oscillations can be powerful biomarkers used for disease detection and prediction of the recovery of function. Electrophysiological signals can also serve as an index for many cutting-edge technologies aiming to interface between the nervous system and neuroprosthetic devices and to monitor the efficacy of boosting neural activity. In this review, we provided an overview of the basic knowledge regarding local field potential, electro- or magneto- encephalography signals, and their biological relevance, followed by a summary of the findings reported in various clinical and experimental stroke studies. We reviewed evidence of stroke-induced changes in hippocampal oscillations and disruption of communication between brain networks as potential mechanisms underlying post-stroke cognitive dysfunction. We also discussed the promise of brain stimulation in promoting post stroke functional recovery via restoring neural activity and enhancing brain plasticity.

The space-time profiles of sleep spindles and their coordination with slow oscillations on the electrode manifold

Sleep spindles are important for sleep quality and cognitive functions, with their coordination with slow oscillations (SOs) potentially organizing cross-region reactivation of memory traces. Here, we describe the organization of spindles on the electrode manifold and their relation to SOs. We analyzed the sleep night EEG of 34 subjects and detected spindles and SOs separately at each electrode. We compared spindle properties (frequency, duration, and amplitude) in slow wave sleep (SWS) and Stage 2 sleep (S2); and in spindles that coordinate with SOs or are uncoupled. We identified different topographical spindle types using clustering analysis that grouped together spindles co-detected across electrodes within a short delay (±300 ms). We then analyzed the properties of spindles of each type, and coordination to SOs. We found that SWS spindles are shorter than S2 spindles, and spindles at frontal electrodes have higher frequencies in S2 compared to SWS. Furthermore, S2 spindles closely following an SO (about 10% of all spindles) show faster frequency, shorter duration, and larger amplitude than uncoupled ones. Clustering identified Global, Local, Posterior, Frontal-Right and Left spindle types. At centro-parietal locations, Posterior spindles show faster frequencies compared to other types. Furthermore, the infrequent SO-spindle complexes are preferentially recruiting Global SO waves coupled with fast Posterior spindles. Our results suggest a non-uniform participation of spindles to complexes, especially evident in S2. This suggests the possibility that different mechanisms could initiate an SO-spindle complex compared to SOs and spindles separately. This has implications for understanding the role of SOs-spindle complexes in memory reactivation.

Human Spindle Variability

In humans, sleep spindles are 10- to 16-Hz oscillations lasting approximately 0.5-2 s. Spindles, along with cortical slow oscillations, may facilitate memory consolidation by enabling synaptic plasticity. Early recordings of spindles at the scalp found anterior channels had overall slower frequency than central-posterior channels. This robust, topographical finding led to dichotomizing spindles as "slow" versus "fast," modeled as two distinct spindle generators in frontal versus posterior cortex. Using a large dataset of intracranial stereoelectroencephalographic (sEEG) recordings from 20 patients (13 female, 7 male) and 365 bipolar recordings, we show that the difference in spindle frequency between frontal and parietal channels is comparable to the variability in spindle frequency within the course of individual spindles, across different spindles recorded by a given site, and across sites within a given region. Thus, fast and slow spindles only capture average differences that obscure a much larger underlying overlap in frequency. Furthermore, differences in mean frequency are only one of several ways that spindles differ. For example, compared with parietal, frontal spindles are smaller, tend to occur after parietal when both are engaged, and show a larger decrease in frequency within-spindles. However, frontal and parietal spindles are similar in being longer, less variable, and more widespread than occipital, temporal, and Rolandic spindles. These characteristics are accentuated in spindles which are highly phase-locked to posterior hippocampal spindles. We propose that rather than a strict parietal-fast/frontal-slow dichotomy, spindles differ continuously and quasi-independently in multiple dimensions, with variability due about equally to within-spindle, within-region, and between-region factors.SIGNIFICANCE STATEMENT Sleep spindles are 10- to 16-Hz neural oscillations generated by cortico-thalamic circuits that promote memory consolidation. Spindles are often dichotomized into slow-anterior and fast-posterior categories for cognitive and clinical studies. Here, we show that the anterior-posterior difference in spindle frequency is comparable to that observed between different cycles of individual spindles, between spindles from a given site, or from different sites within a region. Further, we show that spindles vary on other dimensions such as duration, amplitude, spread, primacy and consistency, and that these multiple dimensions vary continuously and largely independently across cortical regions. These findings suggest that multiple continuous variables rather than a strict frequency dichotomy may be more useful biomarkers for memory consolidation or psychiatric disorders.

Activity and Coupling to Hippocampal Oscillations of Median Raphe GABAergic Cells in Awake Mice

Ascending serotonergic/glutamatergic projection from the median raphe region (MRR) to the hippocampal formation regulates both encoding and consolidation of memory and the oscillations associated with them. The firing of various types of MRR neurons exhibits rhythmic modulation coupled to hippocampal oscillatory activity. A possible intermediary between rhythm-generating forebrain regions and entrained ascending modulation may be the GABAergic circuit in the MRR, known to be targeted by a diverse array of top-down inputs. However, the activity of inhibitory MRR neurons in an awake animal is still largely unexplored. In this study, we utilized whole cell patch-clamp, single cell, and multichannel extracellular recordings of GABAergic and non-GABAergic MRR neurons in awake, head-fixed mice. First, we have demonstrated that glutamatergic and serotonergic neurons receive both transient, phasic, and sustained tonic inhibition. Then, we observed substantial heterogeneity of GABAergic firing patterns but a marked modulation of activity by brain states and fine timescale coupling of spiking to theta and ripple oscillations. We also uncovered a correlation between the preferred theta phase and the direction of activity change during ripples, suggesting the segregation of inhibitory neurons into functional groups. Finally, we could detect complementary alteration of non-GABAergic neurons' ripple-coupled activity. Our findings support the assumption that the local inhibitory circuit in the MRR may synchronize ascending serotonergic/glutamatergic modulation with hippocampal activity on a subsecond timescale.

Single-neuron firing cascades underlie global spontaneous brain events

The resting brain consumes enormous energy and shows highly organized spontaneous activity. To investigate how this activity is manifest among single neurons, we analyzed spiking discharges of ∼10,000 isolated cells recorded from multiple cortical and subcortical regions of the mouse brain during immobile rest. We found that firing of a significant proportion (∼70%) of neurons conformed to a ubiquitous, temporally sequenced cascade of spiking that was synchronized with global events and elapsed over timescales of 5 to 10 s. Across the brain, two intermixed populations of neurons supported orthogonal cascades. The relative phases of these cascades determined, at each moment, the response magnitude evoked by an external visual stimulus. Furthermore, the spiking of individual neurons embedded in these cascades was time locked to physiological indicators of arousal, including local field potential power, pupil diameter, and hippocampal ripples. These findings demonstrate that the large-scale coordination of low-frequency spontaneous activity, which is commonly observed in brain imaging and linked to arousal, sensory processing, and memory, is underpinned by sequential, large-scale temporal cascades of neuronal spiking across the brain.

Replay in Deep Learning: Current Approaches and Missing Biological Elements

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

The essential role of hippocampo-cortical connections in temporal coordination of spindles and ripples

The predominant activity of slow wave sleep is cortical slow oscillations (SOs), thalamic spindles and hippocampal sharp wave ripples. While the precise temporal nesting of these rhythms was shown to be essential for memory consolidation, the coordination mechanism is poorly understood. Here we develop a minimal hippocampo-cortico-thalamic network that can explain the mechanism underlying the SO-spindle-ripple coupling indicating of the succession of regional neuronal interactions. Further we verify the model predictions experimentally in naturally sleeping rodents showing our simple model provides a quantitative match to several experimental observations including the nesting of ripples in the spindle troughs and larger duration but lower amplitude of the ripples co-occurring with spindles or SOs compared to the isolated ripples. The model also predicts that the coupling of ripples to SOs and spindles monotonically enhances by increasing the strength of hippocampo-cortical connections while it is stronger at intermediate values of the cortico-hippocampal projections.

NMDA receptors promote hippocampal sharp-wave ripples and the associated coactivity of CA1 pyramidal cells

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