Biology subjects

Dupret, D.

Publications and source records attributed to Dupret, D..

13 recordsLinked to original sources

bioRxiv2026

A shared manifold for scalable temporal representations in self-paced timing

Adaptive behaviour often relies on tracking the passage of time, yet how distinct brain regions generate coherent temporal representations remains unclear. Using a self-paced interval timing task in mice, we show that heterogeneous single-neuron temporal firing profiles distributed across regions are organized within a shared ring manifold. Within this low-dimensional space, population activity evolves along a common trajectory across different intervals and encodes elapsed time in a relative reference frame. Different durations are not represented by separate neural states, but by modulation of traversal speed. These scalable dynamics arise from coordinated population-wide co-scaling of single-neuron activity and support trial-by-trial adjustments in timing behaviour. A cross-regional assembly of start neurons predicts, at interval onset, upcoming waiting duration and behavioural adjustments, linking initial population states to trajectory evolution. Together, these findings identify population traversal of a shared activity manifold as a mechanism for scalable temporal representation across brain regions.

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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.

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bioRxiv2026

The scaling behavior of hippocampal activity in sleep/rest predicts spatial memory performance

In the hippocampus, reactivation of spatial memories during sleep enhances consolidation and recall. However, network dynamics may independently influence memory retention. We applied the Phenomenological Renormalization Group to CA1 neuronal activity in rats during sleep/rest epochs surrounding a spatial learning task. The scaling exponent of activity variance (), assessed either before or after learning, predicted subsequent recall performance independently of reactivation. The prediction model was transferable across subjects, suggesting that it is a robust biomarker of learning ability. Single-cell features, such as burst propensity and intrinsic timescales, correlated with and memory, predicted retention even when controlling for these factors. Our results identified a link between scale-free circuit dynamics and memory stabilization, suggesting that the tuning of the underlying dynamical regime near criticality is a key determinant of memory longevity. Targeted modulation of these collective states could offer new avenues for memory enhancement.

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bioRxiv2026

A learning-evoked slow-oscillatory architecture paces population activity for offline reactivation across the human medial temporal lobe

Memory processing requires the coordinated engagement of neuronal populations across distributed brain networks and across time. How such coordination is organized in the human medial temporal lobe (MTL) remains unclear. Here, we show that MTL population activity is dynamically structured by a transient slow-oscillatory architecture that emerges during online learning to promote offline consolidation and later recall. Using intracranial recordings that combine single-neuron spiking activity and local field potentials in human participants, we find that mnemonic engagement evokes on-demand slow-oscillatory bursts in the hippocampus. These hippocampal bursts synchronize gamma-band patterns across MTL regions, defining discrete coordination events that pace cross-regional coactivity motifs during learning. These learning-time population motifs are then selectively reactivated during hippocampal ripples in post-learning rest, and the strength of their reactivation predicts subsequent recall accuracy. Together, these findings identify a multi-scale coordination mechanism that links distributed population activity across learning, consolidation, and recall in humans.

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bioRxiv2025

Hippocampal Ripple Diversity organises Neuronal Reactivation Dynamics in the Offline Brain

SO_SCPLOWUMMARYC_SCPLOWHippocampal ripples are highly synchronized neuronal population patterns reactivating past waking experiences in the offline brain. Whether the level, structure, and content of ripple-nested activity are consistent across consecutive events or are tuned in each event remains unclear. By profiling individual ripples using laminar currents in the mouse hippocampus during sleep/rest, we identified Radsink and LMsink ripples featuring current sinks in stratum radiatum versus stratum lacunosum-moleculare, respectively. These two ripple profiles recruit neurons differently. Radsink ripples integrate recent motifs of waking coactivity, combining superficial and deep CA1 principal cells into denser, higher-dimensional patterns that undergo hour-long stable reactivation. In contrast, LMsink ripples contain core motifs of prior coactivity, engaging deep cells into sparser, lower-dimensional patterns that undergo a reactivation drift, gradually updating their pre-structured content for recent wakefulness. We propose that ripple-by-ripple diversity instantiates parallel reactivation channels for stable integration of recent wakefulness and flexible updating of prior internal representations.

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bioRxiv2023

Waveform-based classification of dentate spikes

Synchronous excitatory discharges from the entorhinal cortex (EC) to the dentate gyrus (DG) generate fast and prominent patterns in the hilar local field potential (LFP), called dentate spikes (DSs). As sharp-wave ripples in CA1, DSs are more likely to occur in quiet behavioral states, when memory consolidation is thought to take place. However, their functions in mnemonic processes are yet to be elucidated. The classification of DSs into types 1 or 2 is determined by their origin in the lateral or medial EC, as revealed by current source density (CSD) analysis, which requires recordings from linear probes with multiple electrodes spanning the DG layers. To allow the investigation of the functional role of each DS type in recordings obtained from single electrodes and tetrodes, which are abundant in the field, we developed an unsupervised method using Gaussian mixture models to classify such events based on their waveforms. Our classification approach achieved high accuracies (> 80%) when validated in 8 mice with DG laminar profiles. The average CSDs, waveforms, rates, and widths of the DS types obtained through our method closely resembled those derived from the CSD-based classification. As an example of application, we used the technique to analyze single-electrode LFPs from apolipoprotein (apo) E3 and apoE4 knock-in mice. We observed that the latter group, which is a model for Alzheimers disease, exhibited wider DSs of both types from a young age, with a larger effect size for DS type 2, likely reflecting early pathophysiological alterations in the EC-DG network, such as hyperactivity. In addition to the applicability of the method in expanding the study of DS types, our results show that their waveforms carry information about their origins, suggesting different underlying network dynamics and roles in memory processing. Author summaryThe entorhinal cortex and the dentate gyrus are regions of the brains hippocampal formation that play a crucial role in learning and memory. Their synchronous activation generates fast and high-amplitude electrophysiological patterns in the dentate gyrus, called dentate spikes (DSs). However, the functional role of DSs is still poorly understood. A technical limitation for the study of DSs is that their classification into types 1 and 2 is only possible through laminar profiles obtained by multicontact linear probes. In the present work, we propose a method to classify the different types of DSs through their waveforms, thus expanding the investigation of their significance in mnemonic processes by making possible their study in single-site recordings.

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bioRxiv2023

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 supporting this offline process. SWRs have been studied extensively but the contribution of DSs remains unclear. By combining triple-(DG-CA3-CA1) ensemble 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 in a multi-object recognition task 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.

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bioRxiv2023

Spatio-temporal organization of network activity patterns in the hippocampus

The hippocampus is a layered brain network, composed of diverse cell types arranged in multiple microcircuits with anatomically structured inputs, all working in concert to support memory. This intricate organization generates a myriad of electrophysiological signatures. While specific aspects of these activity patterns have been explored, a comprehensive understanding of the hippocampal layer-embedded dynamics remains elusive. Here, we developed a low-dimensional manifold to capture electrophysiological patterns, mapping their anatomical trajectory along the CA1-to-DG axis, and distinguishing layers based on sharp-wave and theta profiles. This profiling led to the characterization of selective theta-nested gamma signatures for each layer. It further revealed spike patterns associated with gamma rhythms, which highlight specific firing motifs between principal cells and interneurons, and differential firing properties within pyramidal sub-layers. These findings support a holistic understanding of the spatio-temporal activity patterns across hippocampal layers for unraveling the network operations that drive memory-guided behavior.

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bioRxiv2023

Organizing the coactivity structure of the hippocampus from robust to flexible memory

New memories are integrated into prior knowledge of the world. But what if consecutive memories exert opposing demands on the host brain network? We report that acquiring a robust (food-context) memory constrains the hippocampus within a population activity space of highly correlated spike trains that prevents subsequent computation of a flexible (object-location) memory. This densely correlated firing structure developed over repeated mnemonic experience, gradually coupling neurons of the superficial CA1 pyramidale sublayer to whole population activity. Applying hippocampal theta-driven closed-loop optogenetic suppression to mitigate this neuronal recruitment during (food-context) memory formation relaxed the topological constraint on hippocampal coactivity and restored subsequent flexible (object-location) memory. These findings uncover an organizational principle for the peer-to-peer coactivity structure of the hippocampal cell population to successfully meet memory demands.

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bioRxiv2022

Disk-Drive-Like Operations in the Hippocampus

The rapid computation of re-playable memories within the hippocampus in the form of spike sequences is a near computer-like operation. Information can be encoded once during the initial experience, and replayed numerous times after in a compressed-time representation [1-8]. Theta oscillations, sharp-wave ripples, and attractor dynamics have been posited to collectively play a role in the formation and replay of memories. However, the precise interplay between these dynamical states remains elusive. Here, we show that the memory formation dynamics and operations of the hippocampus are not just computer-like, but map directly onto the dynamics and operations of a disk-drive. We constructed a tripartite spiking neural network model where the hippocampus is explicitly described as a disk drive with a rotating disk, an actuator arm, and a read/write head. In this Neural Disk Drive (NDD) model, hippocampal oscillations map to disk rotations in the rotating disk network while attractor dynamics in the actuator arm network point to "tracks" (spike assemblies) on the disk. The read/write head then writes information onto these tracks, which have temporally-structured spikes. Tracks can be replayed during hippocampal ripples for consolidation. We confirmed the existence of interneuron-ring-sequences, predicted by the rotating disk network, in experimental data. Our results establish the hippocampus as a brain region displaying explicit, computer-like operations. Based on the known interactions between the hippocampus and other brain areas, we anticipate that our results may lead to additional models that revisit the hypothesis that the brain performs explicit, computer-like operations.

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bioRxiv2021

Within-cycle instantaneous frequency profiles report oscillatory waveform dynamics.

Non-sinusoidal waveform is emerging as an important feature of neuronal oscillations. However, the role of single cycle shape dynamics in rapidly unfolding brain activity remains unclear. Here, we develop an analytical framework that isolates oscillatory signals from time-series using masked Empirical Mode Decomposition to quantify dynamical changes in the shape of individual cycles (along with amplitude, frequency and phase) using instantaneous frequency. We show how phase-alignment, a process of projecting cycles into a regularly sampled phase-grid space, makes it possible to compare cycles of different durations and shapes. Normalised shapes can then be constructed with high temporal detail whilst accounting for differences in both duration and amplitude. We find that the instantaneous frequency tracks non-sinusoidal shapes in both simulated and real data. Notably, in local field potential recordings of mouse hippocampal CA1, we find that theta oscillations have a stereotyped slow-descending slope in the cycle-wise average, yet exhibiting high variability on a cycle-by-cycle basis. We show how Principal Components Analysis allows identification of motifs of theta cycle waveform that have distinct associations to cycle amplitude, cycle duration and animal movement speed. By allowing investigation into oscillation shape at high temporal resolution, this analytical framework will open new lines of enquiry into how neuronal oscillations support moment-by-moment information processing and integration in brain networks.

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bioRxiv2020

A mechanism for hippocampal memory recall based on excitatory-inhibitory fluctuations in neocortex

The brain has a remarkable capacity to acquire and store memories that can later be selectively recalled. These processes are supported by the hippocampus which is thought to index memory recall by reinstating information stored across distributed neocortical circuits. However, the mechanism that supports this interaction remains unclear. Here, in humans, we show that recall of a visual cue from a paired associate is accompanied by a transient increase in the ratio between glutamate and GABA in visual cortex. Moreover, these excitatory-inhibitory fluctuations are predicted by activity in the hippocampus. These data suggest the hippocampus gates memory recall by indexing information stored across neocortical circuits using a disinhibitory mechanism.

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bioRxiv2019

An Emergent Neural Coactivity Code for Dynamic Memory

Coincidental spike discharge amongst distributed groups of neurons is thought to provide an efficient mechanism for encoding percepts, actions and cognitive processes1-3. Short timescale coactivity can indeed bind neurons with similar tuning, giving rise to robust representations congruent with those of the participating neurons4-6. Alternatively, coactivity may also play a role in information processing through encoding variables not represented by individual neurons. While this type of emergent coactivity-based coding has been described for physically well-defined variables, including percepts and actions7-10, its role in encoding abstract cognitive variables remains unknown. Coactivity-based representation could provide a flexible code in dynamic environments, where animals must regularly learn short-lived behavioural contingencies. Here, we tested this possibility by training mice to discriminate two new behavioural contingencies every day, while monitoring and manipulating neural ensembles in the hippocampal CA1. We found that, while the spiking of neurons within their place fields is organised into congruent coactivity patterns representing discrete locations during unsupervised exploration of the learning enclosure, additional neurons synchronised their activity into spatially-untuned patterns that discriminated opposing learning contingencies. This contingency discrimination was an emergent property of millisecond timescale coactivity rather than the tuning of individual neurons, and predicted trial-by-trial memory performance. Moreover, optogenetic suppression of plastic inputs from the upstream left CA3 region during learning selectively impaired the computation of contingency-discriminating, but not space-representing CA1 coactivity patterns. This manipulation, but not silencing the more stable right CA3 inputs, impaired memory of the contingency discrimination. Thus, the computation of an emergent, coactivity-based discrimination code necessitates plastic synapses and supports dynamic, two-contingency memory.

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