Biology subjects

Guillaumin, M. C. C.

Publications and source records attributed to Guillaumin, M. C. C..

10 recordsLinked to original sources

bioRxiv2026

Generative replay across hippocampal-neocortical circuits

Flexible behaviour requires predictions that extend beyond direct experience, yet the neural mechanisms that support this process remain unclear. A candidate mechanism occurs during hippocampal sharp-wave ripples (SWRs), in which generative replay combines representations for discrete past events into novel activity patterns. However, it remains unknown whether comparable computations occur in neocortex. Using simultaneous electrophysiology and calcium imaging across hippocampus and neocortex, we record neural activity in freely moving mice during an inference task and in subsequent sleep. We show that neuronal sequences in primary visual cortex (V1) encode relationships that were never directly experienced, providing evidence for generative replay outside the hippocampus. Generative replay in V1 is predicted by preceding hippocampal activity, suggesting that hippocampal activity provides a teaching signal to reorganise neocortical representations during sleep. These findings identify a hippocampal-neocortical mechanism for generative replay which may support the construction of a hierarchical generative model to enable flexible behaviour.

neuroscience↗

bioRxiv2023

Deficient synaptic neurotransmission results in a persistent sleep-like cortical activity across vigilance states in mice

Growing evidence suggests that brain activity during sleep, as well as sleep regulation, are tightly linked with synaptic function and network excitability at the local and global levels. We previously reported that a mutation in synaptobrevin 2 (Vamp2) in restless (rlss) mice results in a marked increase of wakefulness and suppression of sleep, in particular REM sleep (REMS) as well as increased consolidation of sleep and wakefulness. In the current study, using finer-scale in vivo electrophysiology recordings, we report that spontaneous cortical activity in rlss mice during NREM sleep (NREMS) is characterised by an occurrence of abnormally prolonged periods of complete neuronal silence (OFF-periods), often lasting several seconds, similar to the burst suppression pattern typically seen under deep anaesthesia. Increased incidence of prolonged network OFF-periods was not specific to NREMS, but also present in REMS and wake in rlss mice. Slow-wave activity (SWA) was generally increased in rlss mice, while higher frequencies including theta-frequency activity were decreased, further resulting in diminished differences between vigilance states. The relative increase in SWA after sleep deprivation was attenuated in rlss mice, suggesting either that rlss mice experience persistently elevated sleep pressure, or, alternatively, that the intrusion of sleep-like patterns of activity into awake state diminishes the accumulation of sleep drive. We propose that deficit in global synaptic neurotransmitter release leads to state inertia, reflected in an abnormal propensity of brain networks to enter and remain in a persistent default state resembling coma or deep anaesthesia.

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bioRxiv2022

Disentangling the role of NAc D1 and D2 cells in hedonic eating

Overeating is driven by both the hedonic component ( liking) of food, and the motivation ( wanting) to eat it. The nucleus accumbens (NAc) is a key brain center implicated in these processes, but how distinct NAc cell populations encode liking and wanting to shape overconsumption remains unclear. Here, we probed the roles of NAc D1 and D2 cells in these processes using cell-specific recording and control in diverse behavioral paradigms that disentangle reward traits of liking and wanting related to food choice and overeating. NAc D2 cells encoded experience-dependent development of liking, while NAc D1 cells encoded innate liking during the first food taste. Optogenetic control confirmed causal links of D1 and D2 cells to these aspects of liking. In relation to wanting, D1 and D2 cells encoded and promoted distinct aspects of food approach: D1 cells interpreted food cues while D2 cells also sustained food-visit-length that facilitates consumption. Finally, at the level of food choice, D1, but not D2, cell activity was sufficient to switch food preference, programming subsequent long-lasting overconsumption. By revealing complementary roles of D1 and D2 cells in consumption, these findings assign neural bases to liking and wanting in a unifying framework of D1 and D2 cell activity.

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bioRxiv2022

Detection of neuronal OFF periods as low amplitude neural activity segments

During non-rapid eye movement sleep (NREM), synchronised neuronal activity is reflected in a specific neural oscillation observed in neocortical electrophysiological signals: a low frequency component characterised by depth-positive/surface-negative potentials known as slow waves, corresponding to alternating periods of high (ON period) and low (OFF period) spiking activity. Often overlooked in favour of slow waves, there is an interest in understanding how neuronal silencing during OFF periods leads to the generation of slow waves and whether this relationship changes between cortical layers. The foremost issue in detecting population OFF periods is the absence of a formal, widely adopted definition. Here, we grouped segments of high frequency neural activity containing spikes, recorded from the neocortex, on the basis of amplitude and asked whether the population of low amplitude (LA) segments displayed the expected characteristics of OFF periods. We corroborate previous studies showing that LA segments in neural activity signals are a uniquely identifiable structure with distinct characteristics from the surrounding signal that identify them as OFF periods including NREM sleep predominance and association with a local field potential (LFP) slow wave. In addition, we attribute new characteristics to these segments not previously associated with OFF periods: vigilance-state-dependent duration and duration-dependent homeostatic response. This could suggest that ON/OFF periods are currently underdefined, and their appearance is less binary than previously considered, instead representing a continuum.

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bioRxiv2022

Neuronal-spiking-based closed-loop stimulationduring cortical ON and OFF states in freely moving mice

The slow oscillation (SO) is a central neuronal dynamic during sleep and is generated by alternating periods of high and low neuronal activity (ON and OFF states). Mounting evidence causally links the SO to sleeps functions, and it has recently become possible to manipulate the SO non-invasively and phase-specifically. These developments represent promising clinical avenues, but they also highlight the importance of improving our understanding of how ON/OFF states affect incoming stimuli and what role they play in neuronal plasticity. Most studies using closed-loop stimulation rely on the electroencephalogram (EEG) and local field potential (LFP) signals, which reflect neuronal ON and OFF states only indirectly. Here we develop an online detection algorithm based on spiking activity recorded from laminar arrays in mouse motor cortex. We find that online detection of ON and OFF states reflects specific phases of spontaneous LFP SO. Our neuronal-spiking-based closed-loop procedure offers a novel opportunity for testing the functional role of SO in sleep-related restorative processes and neural plasticity.

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bioRxiv2021

Somnotate: A robust automated sleep stage classifier that exceeds human performance and identifies ambiguous states in mice

Electrophysiological recordings from freely behaving animals are a widespread and powerful mode of investigation in sleep research. These recordings generate large amounts of data that require sleep stage annotation (polysomnography), in which the data is parcellated according to three vigilance states: awake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep. Manual and computational annotation methods currently ignore intermediate states because the classification features become ambiguous. However, these intermediate states contain important information regarding vigilance state dynamics. Here, we present a new classifier, "Somnotate", which produces automated annotation accuracies that exceed human expert performance on mouse electrophysiological data, is robust to errors in the training data, compatible with different recording configurations, and maintains high performance during experimental interventions. Somnotate is a probabilistic classifier based on a combination of linear discriminant analysis (LDA) with a hidden Markov model (HMM). A unique feature of Somnotate is that it quantifies and reports the certainty of its annotations, enabling the experimenter to identify ambiguous recording periods in a principled manner. We leverage this feature to identify epochs that exhibit intermediate vigilance states, revealing that many of these cluster around state transitions, whereas others correspond to failed attempts to transition. We show that the success rates of different transitions can be experimentally manipulated and explain previously observed sleep patterns. Somnotate can thus facilitate the study of sleep stage transitions and offers new insight into the mechanisms underlying sleep-wake dynamics. Author summaryTypically, the three different vigilance states - awake, REM sleep, and non-REM sleep - exhibit distinct features that are readily recognised in electrophysiological recordings. However, particularly around vigilance state transitions, epochs often exhibit features from more than one state. These intermediate vigilance states pose challenges for existing manual and automated classification methods, and are hence often ignored. Here, we present Somnotate - an open-source, highly accurate and robust sleep stage classifier, which supports research into intermediate states and sleep stage dynamics. Somnotate quantifies and reports the certainty of its annotations, enabling the experimenter to identify abnormal epochs in a principled manner. We use this feature to identify intermediate states and to detect unsuccessful attempts to switch between vigilance states. This provides new insights into the mechanisms of vigilance state transitions in mice, and creates new opportunities for future experiments.

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bioRxiv2020

The role of the hypothalamus in cortical arousaland sleep homeostasis

Sleep and wakefulness are not simple homogenous all-or-none states, but instead are characterized by rich dynamics of brain activity across many temporal and spatial scales. Rapid global state transitions between waking and sleeping are believed to be controlled by hypothalamic circuits, but the contribution of the hypothalamus to within-state changes of sleep and wake "intensity" remains largely unexplored. Here we show that stimulation of inhibitory neurons in the preoptic hypothalamus does not merely trigger awakening from sleep, but the resulting awake state is also characterized by increased cortical activity. This activation is associated with a faster build-up of sleep pressure, proportional to the arousal level. These findings show that hypothalamic systems thought to exclusively control global state switching, also regulate within-state "intensity", which we propose as a key intrinsic variable in shaping the architecture of sleep/wake states across the 24h day.

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bioRxiv2020

A role for the cortex in sleep-wake regulation

The cortex and subcortical circuitry are thought to play distinct roles in the generation of sleep oscillations and global control of vigilance states. Here we silenced a subset of cortical layer 5 pyramidal and dentate gyrus granule cells in mice using a cell-specific ablation of the key t-SNARE protein SNAP25. We found a marked increase in wakefulness accompanied by a reduced rebound of EEG slow-wave activity after sleep deprivation. Our data illustrates an important role for the cortex in both global state control and sleep homeostasis.

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bioRxiv2020

Aberrant synaptic release underlies sleep/wake transition deficits in a mouse Vamp2 mutant

Sleep-wake transitions are modulated through extensive subcortical networks although the precise roles of their individual components remain elusive. Using forward genetics and in vivo electrophysiology, we identified a recessive mouse mutant line characterised by a reduced propensity to transition between all sleep states while a profound loss in total REM sleep time was evident. The causative mutation, an Ile102Asn substitution in VAMP2, was associated with substantial synaptic changes while in vitro electrophysiological investigations with fluorescence imaging revealed a diminished probability of vesicular release in mutants. We conclude that the synaptic efficiency of the entire subcortical brain network determines the likelihood that an animal transitions from one vigilance state to the next.

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bioRxiv2019

Cortical neuronal activity determines the dynamics of local sleep homeostasis

The homeostatic regulation of sleep manifests as a relative constancy of its daily amount and intensity. Theoretical descriptions of this phenomenon define "Process S", a variable with dynamics dependent only on sleep-wake history, whose levels are reflected in electroencephalogram (EEG) slow wave activity (0.5 - 4 Hz) during sleep. Here we developed novel mathematical models of Process S in mice, assuming that its dynamics are a function of the deviation of cortical neuronal firing rates from a locally defined set-point, crucially without explicit knowledge of sleep-wake state. Our results suggest that Process S tracks global sleep-wake history through an integration of local cortical neuronal activity levels over time. We posit that, instead of reflecting sleep-wake-dependent changes in specific variables and serving their homeostatic regulation, Process S may be a time-keeping mechanism which enables individuals to obtain a species-specific and ecologically-relevant quantity of sleep, even in the absence of external temporal information.

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