Sharp-wave Ripples Research Articles

Neuroplasticity Meets Artificial Intelligence: A Hippocampus-Inspired Approach to the Stability–Plasticity Dilemma

The stability–plasticity dilemma remains a critical challenge in developing artificial intelligence (AI) systems capable of continuous learning. This perspective paper presents a novel approach by drawing inspiration from the mammalian hippocampus–cortex system. We elucidate how this biological system’s ability to balance rapid learning with long-term memory retention can inspire novel AI architectures. Our analysis focuses on key mechanisms, including complementary learning systems and memory consolidation, with emphasis on recent discoveries about sharp-wave ripples and barrages of action potentials. We propose innovative AI designs incorporating dual learning rates, offline consolidation, and dynamic plasticity modulation. This interdisciplinary approach offers a framework for more adaptive AI systems while providing insights into biological learning. We present testable predictions and discuss potential implementations and implications of these biologically inspired principles. By bridging neuroscience and AI, our perspective aims to catalyze advancements in both fields, potentially revolutionizing AI capabilities while deepening our understanding of neural processes.

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.

Hippocampal recording with a soft microelectrode array in a cranial window imaging scheme: a validation study

The hippocampus has a crucial role in the formation, consolidation and recall of memories as well as in navigation related processes. These functions are in the focus of neuroscience and different disciplines have contributed to this research field for decades. Two-photon imaging in awake animals is a valuable new aspect for these observations, especially when it is supported by electrophysiology. In this study, we applied high speed two-photon hippocampal imaging through a chronically implanted, soft, transparent microelectrode (STM) device incorporated into a cranial window chamber in awake mice. We monitored the impedance of the recording sites over the course of the experiments to observe long-term changes in recording quality. The large-scale ipsilateral local field potential (LFP) recordings from the dorsal hippocampus provided reliable sharp wave-ripples (SPW-Rs), multi-unit activity (MUA) and single-unit activity (SUA) for up to two months. Calcium imaging of GCaMP6f. labeled cells from the CA1 pyramidal layer under the transparent device was possible even after six months in thy1-GCaMP6f. transgenic mice. We investigated the immune response with GFAP staining after the end of the long-term experiments. Based on our results, this dedicated transparent electrode device proved to be suitable for simultaneous two-photon imaging and large-scale electrophysiological measurements in chronic experiments in mice.

Food intake enhances hippocampal sharp wave-ripples.

Effective regulation of energy metabolism is critical for survival. Metabolic control involves various nuclei within the hypothalamus, which receive information about the body's energy state and coordinate appropriate responses to maintain homeostasis, such as thermogenesis, pancreatic insulin secretion, and food-seeking behaviors. It has recently been found that the hippocampus, a brain region traditionally associated with memory and spatial navigation, is also involved in metabolic regulation. Specifically, hippocampal sharp wave ripples (SWRs), which are high-frequency neural oscillations supporting memory consolidation and foraging decisions, have been shown to influence peripheral glucose metabolism. However, whether SWRs are enhanced by recent feeding-when the need for glucose metabolism increases, and if so, whether feeding-dependent modulation of SWRs is communicated to other brain regions involved in metabolic regulation, remains unknown. To address these gaps, we recorded SWRs from the dorsal CA1 region of the hippocampus of mice during sleep sessions before and after consumption of meals of varying caloric values. We found that SWRs occurring during sleep are significantly enhanced following food intake, with the magnitude of enhancement being dependent on the caloric content of the meal. This pattern occurred under both food-deprived and ad libitum feeding conditions. Moreover, we demonstrate that neuronal populations in the lateral hypothalamus, which regulates food intake, exhibit robust SWR-triggered increase in activity. These findings identify the satiety state as a factor modulating SWRs and suggest that hippocampal-lateral hypothalamic communication is a potential mechanism by which SWRs could modulate peripheral metabolism and food intake.

Social odors drive hippocampal CA2 place cell responses to social stimuli.

Hippocampal region CA2 is essential for social memory processing. Interaction with social stimuli induces changes in CA2 place cell firing during active exploration and sharp wave-ripples during rest following a social interaction. However, it is unknown whether these changes in firing patterns are caused by integration of multimodal social stimuli or by a specific sensory modality associated with a social interaction. Rodents rely heavily on chemosensory cues in the form of olfactory signals for social recognition processes. To determine the extent to which social olfactory signals contribute to CA2 place cell responses to social stimuli, we recorded CA2 place cells in rats freely exploring environments containing stimuli that included or lacked olfactory content. We found that CA2 place cell firing patterns significantly changed only when social odors were prominent. Also, place cells that increased their firing in the presence of social odors alone preferentially increased their firing during subsequent sharp wave-ripples. Our results suggest that social olfactory cues are essential for changing CA2 place cell firing patterns during and after social interactions. These results support prior work suggesting CA2 performs social functions and shed light on processes underlying CA2 responses to social stimuli.

Alzheimer's pathology disrupts flexible place cell coding and hippocampal-prefrontal neural dynamics during risky foraging decisions in mice.

This study investigates the neural activity patterns associated with impaired decision-making in Alzheimer's disease (AD) by examining the effects of amyloid pathology on prefrontal-hippocampal circuit dynamics in 5XFAD mice, a model system known for its pronounced early amyloid pathology. Using ecologically relevant "approach food-avoid predator" paradigms, we revealed that 5XFAD mice display impaired decision strategies in risky foraging tasks, characterized by rigid hippocampal place fields and diminished sharp-wave ripple (SWR) frequencies, accompanied by disrupted prefrontal-hippocampal connectivity. These neural deficits align with behavioral inflexibility in dynamic threat scenarios. Our findings indicate that disrupted SWR dynamics and corticolimbic connectivity contribute to decision-making deficits in AD, emphasizing the potential of targeting these specific neural mechanisms to ameliorate cognitive impairments. This research contributes significantly to our understanding of AD's impact on cognition, providing insights that could lead to more targeted therapeutic strategies.

Disrupted Hippocampal Theta-Gamma Coupling and Spike-Field Coherence Following Experimental Traumatic Brain Injury.

Traumatic brain injury (TBI) often results in persistent learning and memory deficits, likely due to disrupted hippocampal circuitry underlying these processes. Precise temporal control of hippocampal neuronal activity is important for memory encoding and retrieval and is supported by oscillations that dynamically organize single unit firing. Using high-density laminar electrophysiology, we discovered a loss of oscillatory power across CA1 lamina, with a profound, layer-specific reduction in theta-gamma phase amplitude coupling in injured rats. Interneurons from injured animals were less strongly entrained to theta and gamma oscillations, suggesting a mechanism for the loss of coupling, while pyramidal cells were entrained to a later phase of theta. During quiet immobility, we report decreased ripple amplitudes from injured animals during sharp-wave ripple events. These results reveal deficits in information encoding and retrieval schemes essential to cognition that likely underlie TBI-associated learning and memory impairments, and elucidate potential targets for future neuromodulation therapies.

Inhibitory plasticity supports replay generalization in the hippocampus.

Memory consolidation assimilates recent experiences into long-term memory. This process requires the replay of learned sequences, although the content of these sequences remains controversial. Recent work has shown that the statistics of replay deviate from those of experience: stimuli that are experientially salient may be either recruited or suppressed from sharp-wave ripples. In this study, we found that this phenomenon can be explained parsimoniously and biologically plausibly by a Hebbian spike-time-dependent plasticity rule at inhibitory synapses. Using models at three levels of abstraction-leaky integrate-and-fire, biophysically detailed and abstract binary-we show that this rule enables efficient generalization, and we make specific predictions about the consequences of intact and perturbed inhibitory dynamics for network dynamics and cognition. Finally, we use optogenetics to artificially implant non-generalizable representations into the network in awake behaving mice, and we find that these representations also accumulate inhibition during sharp-wave ripples, experimentally validating a major prediction of our model. Our work outlines a potential direct link between the synaptic and cognitive levels of memory consolidation, with implications for both normal learning and neurological disease.

Offline hippocampal reactivation during dentate spikes supports flexible memory.

Stabilizing new memories requires coordinated neuronal spiking activity during sleep. Hippocampal sharp-wave ripples (SWRs) in the cornu ammonis (CA) region and dentate spikes (DSs) in the dentate gyrus (DG) are prime candidate network events for supporting this offline process. SWRs have been studied extensively, but the contribution of DSs remains unclear. By combining triple-ensemble (DG-CA3-CA1) recordings and closed-loop optogenetics in mice, we show that, like SWRs, DSs synchronize spiking across DG and CA principal cells to reactivate population-level patterns of neuronal coactivity expressed during prior waking experience. Notably, the population coactivity structure in DSs is more diverse and higher dimensional than that seen during SWRs. Importantly, suppressing DG granule cell spiking selectively during DSs impairs subsequent flexible memory performance during multi-object recognition tasks and associated hippocampal patterns of neuronal coactivity. We conclude that DSs constitute a second offline network event central to hippocampal population dynamics serving memory-guided behavior.

Haploinsufficiency of intraflagellar transport protein 172 causes autism-like behavioral phenotypes in mice through BDNF

IntroductionPrimary cilia are hair-like solitary organelles growing on most mammalian cells that play fundamental roles in embryonic patterning and organogenesis. Defective cilia often cause a suite of inherited diseases called ciliopathies with multifaceted manifestations. Intraflagellar transport (IFT), a bidirectional protein trafficking along the cilium, actively facilitates the formation and absorption of primary cilia. IFT172 is the largest component of the IFT-B complex, and its roles in Bardet-Biedl Syndrome (BBS) have been appreciated with unclear mechanisms. ObjectivesWe performed a battery of behavioral tests with Ift172 haploinsufficiency (Ift172+/-) and WT littermates. We use RNA sequencing to identify the genes and signaling pathways that are differentially expressed and enriched in the hippocampus of Ift172+/- mice. Using AAV-mediated sparse labeling, electron microscopic examination, patch clamp and local field potential recording, western blot, luciferase reporter assay, chromatin immunoprecipitation, and neuropharmacological approach, we investigated the underlying mechanisms for the aberrant phenotypes presented by Ift172+/- mice. ResultsIft172+/- mice displayed excessive self-grooming, elevated anxiety, and impaired cognition. RNA sequencing revealed enrichment of differentially expressed genes in pathways relevant to axonogenesis and synaptic plasticity, which were further confirmed by less spine density and synaptic number. Ift172+/- mice demonstrated fewer parvalbumin-expressing neurons, decreased inhibitory synaptic transmission, augmented theta oscillation, and sharp-wave ripples in the CA1 region. Moreover, Ift172 haploinsufficiency caused less BDNF production and less activated BDNF-TrkB signaling pathway through transcription factor Gli3. Application of 7,8-Dihydroxyflavone, a potent small molecular TrkB agonist, fully restored BDNF-TrkB signaling activity and abnormal behavioral phenotypes presented by Ift172+/- mice. With luciferase and chip assays, we provided further evidence that Gli3 may physically interact with BDNF promoter I and regulate BDNF expression. ConclusionsOur data suggest that Ift172 per se drives neurotrophic effects and, when defective, could cause neurodevelopmental disorders reminiscent of autism-like disorders.

Rat anterior cingulate neurons responsive to rule or strategy changes are modulated by the hippocampal theta rhythm and sharp-wave ripples.

To better understand neural processing during adaptive learning of stimulus-response-reward contingencies, we recorded synchrony of neuronal activity in anterior cingulate cortex (ACC) and hippocampal rhythms in male rats acquiring and switching between spatial and visual discrimination tasks in a Y-maze. ACC population activity as well as single unit activity shifted shortly after task rule changes or just before the rats adopted different task strategies. Hippocampal theta oscillations (associated with memory encoding) modulated an elevated proportion of rule-change responsive neurons (70%), but other neurons that were correlated with strategy-change, strategy value and reward-rate were not. However, hippocampal sharp wave-ripples modulated significantly higher proportions of rule-change, strategy-change and reward-rate responsive cells during post-session sleep but not pre-session sleep. This suggests an underestimated mechanism for hippocampal mismatch and contextual signals to facilitate ACC to detect contingency changes for cognitive flexibility, a function that is attenuated after it is damaged.

A hippocampal circuit mechanism to balance memory reactivation during sleep.

Memory consolidation involves the synchronous reactivation of hippocampal cells active during recent experience in sleep sharp-wave ripples (SWRs). How this increase in firing rates and synchrony after learning is counterbalanced to preserve network stability is not understood. We discovered a network event generated by an intrahippocampal circuit formed by a subset of CA2 pyramidal cells to cholecystokinin-expressing (CCK+) basket cells, which fire a barrage of action potentials ("BARR") during non-rapid eye movement sleep. CA1 neurons and assemblies that increased their activity during learning were reactivated during SWRs but inhibited during BARRs. The initial increase in reactivation during SWRs returned to baseline through sleep. This trend was abolished by silencing CCK+ basket cells during BARRs, resulting in higher synchrony of CA1 assemblies and impaired memory consolidation.

Selective reactivation of value- and place-dependent information during sharp-wave ripples in the intermediate and dorsal hippocampus.

Reactivating place cells during sharp-wave ripples in the hippocampus is important for memory consolidation. However, whether hippocampal reactivation is affected by the values of events experienced by the animal is largely unknown. Here, we investigated whether place cells in the dorsal (dHP) and intermediate hippocampus (iHP) of rats are differentially reactivated depending on the value associated with a place during the learning of places associated with higher-value rewards in a T-maze. Place cells in the iHP representing the high-value location were reactivated significantly more frequently than those representing the low-value location, characteristics not observed in the dHP. In contrast, the activities of place cells in the dHP coding the routes leading to high-value locations were replayed more than those in the iHP. Our findings suggest that value-based differential reactivation patterns along the septotemporal axis of the hippocampus may play essential roles in optimizing goal-directed spatial learning for maximal reward.

Neural coding of space by time.

The intertwining of space and time poses a significant scientific challenge, transcending disciplines from philosophy and physics to neuroscience. Deciphering neural coding, marked by its inherent spatial and temporal dimensions, has proven to be a complex task. In this paper, we present insights into temporal and spatial modes of neural coding and their intricate interplay, drawn from neuroscientific findings. We illustrate the conversion of a purely spatial input into the temporal form of a singular spike train, demonstrating storage, transmission to remote locations, and recall through spike bursts corresponding to Sharp Wave Ripples. Moreover, the converted temporal representation can be transformed back into a spatiotemporal pattern. The principles of the transformation process are illustrated using a simple feed-forward spiking neural network. The frequencies and phases of Subthreshold Membrane potential Oscillations play a pivotal role in this framework. The model offers insights into information multiplexing and phenomena such as stretching or compressing time of spike patterns.

Functional architecture of intracellular oscillations in hippocampal dendrites

Fast electrical signaling in dendrites is central to neural computations that support adaptive behaviors. Conventional techniques lack temporal and spatial resolution and the ability to track underlying membrane potential dynamics present across the complex three-dimensional dendritic arbor in vivo. Here, we perform fast two-photon imaging of dendritic and somatic membrane potential dynamics in single pyramidal cells in the CA1 region of the mouse hippocampus during awake behavior. We study the dynamics of subthreshold membrane potential and suprathreshold dendritic events throughout the dendritic arbor in vivo by combining voltage imaging with simultaneous local field potential recording, post hoc morphological reconstruction, and a spatial navigation task. We systematically quantify the modulation of local event rates by locomotion in distinct dendritic regions, report an advancing gradient of dendritic theta phase along the basal-tuft axis, and describe a predominant hyperpolarization of the dendritic arbor during sharp-wave ripples. Finally, we find that spatial tuning of dendritic representations dynamically reorganizes following place field formation. Our data reveal how the organization of electrical signaling in dendrites maps onto the anatomy of the dendritic tree across behavior, oscillatory network, and functional cell states.

Entorhinal cortex represents task-relevant remote locations independent of CA1.

Neurons can collectively represent the current sensory experience while an animal is exploring its environment or remote experiences while the animal is immobile. These remote representations can reflect learned associations and be required for learning. Neurons in the medial entorhinal cortex (MEC) reflect the animal's current location during movement, but little is known about what MEC neurons collectively represent during immobility. Here, we recorded thousands of neurons in superficial MEC and dorsal CA1 as mice learned to associate two pairs of rewarded locations. We found that during immobility, the MEC neural population frequently represented positions far from the animal's location, which we defined as 'non-local coding'. Cells with spatial firing fields at remote locations drove non-local coding, even as cells representing the current position remained active. While MEC non-local coding has been reported during sharp-wave ripples in downstream CA1, we observed non-local coding more often outside of ripples. In fact, CA1 activity was less coordinated with MEC during non-local coding. We further observed that non-local coding was pertinent to the task, as MEC preferentially represented remote task-relevant locations at appropriate times, while rarely representing task-irrelevant locations. Together, this work raises the possibility that MEC non-local coding could strengthen associations between locations independently from CA1.

Extended Poisson Gaussian-Process Latent Variable Model for Unsupervised Neural Decoding.

Dimension reduction on neural activity paves a way for unsupervised neural decoding by dissociating the measurement of internal neural pattern reactivation from the measurement of external variable tuning. With assumptions only on the smoothness of latent dynamics and of internal tuning curves, the Poisson gaussian-process latent variable model (P-GPLVM; Wu etal., 2017) is a powerful tool to discover the low-dimensional latent structure for high-dimensional spike trains. However, when given novel neural data, the original model lacks a method to infer their latent trajectories in the learned latent space, limiting its ability for estimating the neural reactivation. Here, we extend the P-GPLVM to enable the latent variable inference of new data constrained by previously learned smoothness and mapping information. We also describe a principled approach for the constrained latent variable inference for temporally compressed patterns of activity, such as those found in population burst events during hippocampal sharp-wave ripples, as well as metrics for assessing the validity of neural pattern reactivation and inferring the encoded experience. Applying these approaches to hippocampal ensemble recordings during active maze exploration, we replicate the result that P-GPLVM learns a latent space encoding the animal's position. We further demonstrate that this latent space can differentiate one maze context from another. By inferring the latent variables of new neural data during running, certain neural patterns are observed to reactivate, in accordance with the similarity of experiences encoded by its nearby neural trajectories in the training data manifold. Finally, reactivation of neural patterns can be estimated for neural activity during population burst events as well, allowing the identification for replay events of versatile behaviors and more general experiences. Thus, our extension of the P-GPLVM framework for unsupervised analysis of neural activity can be used to answer critical questions related to scientific discovery.

A nonoscillatory, millisecond-scale embedding of brain state provides insight into behavior.

The most robust and reliable signatures of brain states are enriched in rhythms between 0.1 and 20 Hz. Here we address the possibility that the fundamental unit of brain state could be at the scale of milliseconds and micrometers. By analyzing high-resolution neural activity recorded in ten mouse brain regions over 24 h, we reveal that brain states are reliably identifiable (embedded) in fast, nonoscillatory activity. Sleep and wake states could be classified from 100 to 101 ms of neuronal activity sampled from 100 µm of brain tissue. In contrast to canonical rhythms, this embedding persists above 1,000 Hz. This high-frequency embedding is robust to substates, sharp-wave ripples and cortical on/off states. Individual regions intermittently switched states independently of the rest of the brain, and such brief state discontinuities coincided with brief behavioral discontinuities. Our results suggest that the fundamental unit of state in the brain is consistent with the spatial and temporal scale of neuronal computation.

Neurofeedback training can modulate task-relevant memory replay rate in rats.

Hippocampal replay - the time-compressed, sequential reactivation of ensembles of neurons related to past experience - is a key neural mechanism of memory consolidation. Replay typically coincides with a characteristic pattern of local field potential activity, the sharp-wave ripple (SWR). Reduced SWR rates are associated with cognitive impairment in multiple models of neurodegenerative disease, suggesting that a clinically viable intervention to promote SWRs and replay would prove beneficial. We therefore developed a neurofeedback paradigm for rat subjects in which SWR detection triggered rapid positive feedback in the context of a memory-dependent task. This training protocol increased the prevalence of task-relevant replay during the targeted neurofeedback period by changing the temporal dynamics of SWR occurrence. This increase was also associated with neural and behavioral forms of compensation after the targeted period. These findings reveal short-timescale regulation of SWR generation and demonstrate that neurofeedback is an effective strategy for modulating hippocampal replay.

Neurofeedback training can modulate task-relevant memory replay rate in rats

Hippocampal replay – the time-compressed, sequential reactivation of ensembles of neurons related to past experience – is a key neural mechanism of memory consolidation. Replay typically coincides with a characteristic pattern of local field potential activity, the sharp-wave ripple (SWR). Reduced SWR rates are associated with cognitive impairment in multiple models of neurodegenerative disease, suggesting that a clinically viable intervention to promote SWRs and replay would prove beneficial. We therefore developed a neurofeedback paradigm for rat subjects in which SWR detection triggered rapid positive feedback in the context of a memory-dependent task. This training protocol increased the prevalence of task-relevant replay during the targeted neurofeedback period by changing the temporal dynamics of SWR occurrence. This increase was also associated with neural and behavioral forms of compensation after the targeted period. These findings reveal short-timescale regulation of SWR generation and demonstrate that neurofeedback is an effective strategy for modulating hippocampal replay.