A hippocampal circuit mechanism to balance memory reactivation during sleep

Unraveling Multiregional Neural Patterns during Consciousness Transition Using Flexible Microelectrode Arrays Integrated with Neuropixels Chips

Consciousness transitions, including awakening from anesthesia or falling asleep, involve complex neural dynamics across multiple brain regions. Understanding these transitions requires simultaneous and stable monitoring of large-scale neural activity in freely moving animals. Here, a flexible microelectrode array system (FlexiPixels probe) is demonstrated that integrates a multishank flexible microelectrode array with Neuropixels chips. This lightweight FlexiPixels probe enables stable and long-term neural signal recording across multiple brain regions in freely moving rats and tracking of neuronal activities during consciousness transitions from anesthesia to wakefulness and subsequent sleep states. Distinct state-dependent firing patterns emerge across different brain regions and neuronal types. CA1 neurons show similar activity during wakefulness and sleep, while DG neurons exhibit unique anesthesia sensitivity. These findings demonstrate FlexiPixels' capabilities for stable multiregion neural recording in freely moving animals and potential to unravel region-specific signatures in consciousness studies.

The role of electroencephalography in epilepsy research-From seizures to interictal activity and comorbidities

Electroencephalography (EEG) has been instrumental in epilepsy research for the past century, both for basic and translational studies. Its contributions have advanced our understanding of epilepsy, shedding light on the pathophysiology and functional organization of epileptic networks, and the mechanisms underlying seizures. Here we re-examine the historical significance, ongoing relevance, and future trajectories of EEG in epilepsy research. We describe traditional approaches to record brain electrical activity and discuss novel cutting-edge, large-scale techniques using micro-electrode arrays. Contemporary EEG studies explore brain potentials beyond the traditional Berger frequencies to uncover underexplored mechanisms operating at ultra-slow and high frequencies, which have proven valuable in understanding the principles of ictogenesis, epileptogenesis, and endogenous epileptogenicity. Integrating EEG with modern techniques such as optogenetics, chemogenetics, and imaging provides a more comprehensive understanding of epilepsy. EEG has become an integral element in a powerful suite of tools for capturing epileptic network dynamics across various temporal and spatial scales, ranging from rapid pathological synchronization to the long-term processes of epileptogenesis or seizure cycles. Advancements in EEG recording techniques parallel the application of sophisticated mathematical analyses and algorithms, significantly augmenting the information yield of EEG recordings. Beyond seizures and interictal activity, EEG has been instrumental in elucidating the mechanisms underlying epilepsy-related cognitive deficits and other comorbidities. Although EEG remains a cornerstone in epilepsy research, persistent challenges such as limited spatial resolution, artifacts, and the difficulty of long-term recording highlight the ongoing need for refinement. Despite these challenges, EEG continues to be a fundamental research tool, playing a central role in unraveling disease mechanisms and drug discovery.

Integrating endocannabinoid signaling, CCK interneurons, and hippocampal circuit dynamics in behaving animals

The brain's endocannabinoid signaling system modulates a diverse range of physiological phenomena and is also involved in various psychiatric and neurological disorders. The basic components of the molecular machinery underlying endocannabinoid-mediated synaptic signaling have been known for decades. However, limitations associated with the short-lived nature of endocannabinoid lipid signals had made it challenging to determine the spatiotemporal specificity and dynamics of endocannabinoid signaling in vivo. Here, we discuss how novel technologies have recently enabled unprecedented insights into endocannabinoid signaling taking place at specific synapses in behaving animals. In this review, we primarily focus on cannabinoid-sensitive inhibition in the hippocampus in relation to place cell properties to illustrate the potential of these novel methodologies. In addition, we highlight implications of these approaches and insights for the unraveling of cannabinoid regulation of synapses in vivo in other brain circuits in both health and disease.

A cortical-hippocampal communication undergoes rebalancing after new learning

The brain's ability to consolidate a wide range of memories while maintaining their distinctiveness across experiences remains poorly understood. Sharp-wave ripples, neural oscillations that occur predominantly within CA1 of the hippocampus during immobility and sleep, have been shown to play a critical role in the consolidation process. More recently, evidence has uncovered functional heterogeneity of pyramidal neurons within distinct sublayers of CA1 that display unique properties during ripples, potentially contributing to memory specificity. Despite this, it remains unclear exactly how ripples shift the activity of CA1 neuronal populations to accommodate the consolidation of specific memories and how sublayer differences manifest. Here, we studied interactions between the anterior cingulate cortex (ACC) and CA1 neurons during ripples and discovered a reorganization of their communication following learning. Notably, this reorganization appeared specifically for CA1 superficial (CA1sup) sublayer neurons. Utilizing a generalized linear model decoder, we demonstrate the pre-existence of ACC-to-CA1sup communication, which is suppressed during new learning and subsequent sleep suggesting that ACC activity may reallocate the contribution of CA1sup neurons during memory acquisition and consolidation. Further supporting this notion, we found that optogenetic stimulations of the ACC preferentially suppressed CA1sup interneurons while activating a unique subset of CA1 interneurons. Overall, these findings highlight a possible role of the ACC in rebalancing CA1 neuronal populations' contribution to ripple contents surrounding learning.

Sleep links hippocampal propensity for epileptiform activity to its viscerosensory inputs

The development of a seizure relies on two factors. One is the existence of an overexcitable neuronal network and the other is a trigger that switches normal activity of that network into a paroxysmal state. While mechanisms of local overexcitation have been the focus of many studies, the process of triggering remains poorly understood. We suggest that, apart from the known exteroceptive sources of reflex epilepsy such as visual, auditory or olfactory signals, there is a range of interoceptive triggers, which are relevant for seizure development in Temporal Lobe Epilepsy (TLE). The hypothesis proposed here aims to explain the prevalence of epileptic activity in sleep and in drowsiness states and to provide a detailed mechanism of seizures triggered by interoceptive signals.

Sleep microstructure organizes memory replay

Recently acquired memories are reactivated in the hippocampus during sleep, an initial step for their consolidation1-3. This process is concomitant with the hippocampal reactivation of previous memories4-6, posing the problem of how to prevent interference between older and recent, initially labile, memory traces. Theoretical work has suggested that consolidating multiple memories while minimizing interference can be achieved by randomly interleaving their reactivation7-10. An alternative is that a temporal microstructure of sleep can promote the reactivation of different types of memories during specific substates. Here, to test these two hypotheses, we developed a method to simultaneously record large hippocampal ensembles and monitor sleep dynamics through pupillometry in naturally sleeping mice. Oscillatory pupil fluctuations revealed a previously unknown microstructure of non-REM sleep-associated memory processes. We found that memory replay of recent experiences dominated in sharp-wave ripples during contracted pupil substates of non-REM sleep, whereas replay of previous memories preferentially occurred during dilated pupil substates. Selective closed-loop disruption of sharp-wave ripples during contracted pupil non-REM sleep impaired the recall of recent memories, whereas the same manipulation during dilated pupil substates had no behavioural effect. Stronger extrinsic excitatory inputs characterized the contracted pupil substate, whereas higher recruitment of local inhibition was prominent during dilated pupil substates. Thus, the microstructure of non-REM sleep organizes memory replay, with previous versus new memories being temporally segregated in different substates and supported by local and input-driven mechanisms, respectively. Our results suggest that the brain can multiplex distinct cognitive processes during sleep to facilitate continuous learning without interference.

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.

A BARRage of firing while asleep

Memory reactivation requires counterbalancing to consolidate memories.