Biology subjects

Destexhe, A.

Publications and source records attributed to Destexhe, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Responsiveness of cerebral cortex to hippocampal inputs depends on brain region and brain state

Gamma oscillations (30-90 Hz) are a prominent signature of cortical network state, but whether they facilitate or hinder inter-areal communication remains unresolved. The communication-through-coherence hypothesis posits that gamma enhances transmission between areas, whereas recent computational work suggests that high-amplitude gamma oscillations may instead filter incoming inputs and reduce their impact. To distinguish between these accounts, we used a well-defined physiological input--sharp-wave ripple (SWR) complexes--to probe cortical responsiveness via two parallel monosynaptic pathways: from ventral CA1 to prefrontal cortex (PFC), and from dorsal CA1 to retrosplenial cortex (RSC). By classifying the cortical state immediately preceding each ripple as low- or high-amplitude gamma, we found that PFC responses to ripples were significantly larger during low-amplitude gamma states, an effect carried by the ventral CA1-PFC pathway and driven primarily by ripples during quiet wakefulness. RSC showed no such state-dependent modulation. A mean-field model of PFC reproduced the enhanced responsiveness during low-amplitude gamma and revealed that this modulation depends on the excitatory-inhibitory balance of the afferent input and on the level of recurrent excitation--providing a mechanistic explanation for the distinct behaviors of PFC and RSC, which differ in their local recurrent connectivity. Extending the model to a chain of cortical areas predicted that low-amplitude gamma supports robust propagation of activity across regions, whereas high-amplitude gamma confines it locally. Together, these results argue that low-amplitude gamma, rather than strong gamma synchronization, constitutes a favorable substrate for communication between brain areas.

neuroscience↗

bioRxiv2026

BRIDGE: A Computational Workflow from Single Neurons to Network of Mean-Field Models

Mean-field models are extensively used in large-scale brain simulations because they provide a wieldy description of population dynamics while preserving key features of neural activity. Despite their widespread adoption, no common and reproducible methodology currently exists to systematically derive and validate mean-field models starting from biologically grounded single neuron dynamics. As a result, implementations are often ad hoc, difficult to reproduce and rarely reusable. Here we introduce BRIDGE, a modular, open-source Python pipeline that enables the bottom-up reconstruction, analysis, validation, and simulation of mean-field models from single neurons. The framework integrates single neurons modelling, network simulations, extraction of population statistics, parameters analysis, quantitative comparisons between spiking neural networks and corresponding mean-field representations, and simulation of network of mean-fields. Its flexible architecture allows users to incorporate different neuron models and to generate region-specific or state-dependent mean-field formulations. BRIDGE provides a reproducible foundation for developing biologically informed mean-field models suitable for large-scale and whole-brain simulations, supporting the transition from generic homogeneous population models toward region-specific ones. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC="FIGDIR/small/742067v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1124404org.highwire.dtl.DTLVardef@2f8b2aorg.highwire.dtl.DTLVardef@1598f37org.highwire.dtl.DTLVardef@c9814b_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2026

Directed Human Structural Connectome Reveals Hierarchical Organization and Shapes Large-Scale Brain Dynamics

The human structural connectome, most commonly derived from diffusion-weighted imaging (DWI) and tractography, provides a macroscopic description of whole-brain wiring and serves as the structural foundation of network neuroscience, large-scale brain simulations, and personalized digital brain twins. However, tractography-derived connectomes are fundamentally limited by their inability to distinguish afferent from efferent connections, yielding networks that are undirected and therefore blind to the hierarchical organization imposed by the directionality of anatomical connections. In this study, we introduce a directed human structural connectome (DHSC) by transferring tracer-derived projection patterns from macaque to human using cross-species connectivity blueprints. Topological analysis of the DHSC manifests biological plausibility, a small-world network organization, and a directionality-based hierarchy, which offer the hierarchical organization of human brain networks. In the context of brain dynamics, the introduction of directionality reshapes the propagation and persistence of sensory inputs. DHSC also best captures the empirical spatiotemporal dynamics of stimulus-evoked brain activity. The findings demonstrate that anatomical directionality is a critical determinant of large-scale brain organization and dynamics. This provides evidence that directed connectome may offer potential advantages in large-scale simulations of the human brain. The resulting DHSC, along with all related analyses and data are openly available.

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bioRxiv2026

Circadian Modulation of Excitation-Inhibition Balance Drives Ictal Transitions in a Mechanistic Model of Epileptic Networks

Epileptic seizures emerge from pathological synchronization in neuronal networks and are strongly influenced by circadian rhythms. Here, we developed a computational framework to investigate how circadian modulation of excitation/inhibition (E/I) balance shapes transitions from physiological to pathological activity. The model consists of interacting excitatory and inhibitory populations containing varying proportions of impaired neurons with altered intrinsic excitability. Circadian effects were incorporated through modulation of synaptic time constants, mimicking daily fluctuations in E/I dynamics. Network activity was characterized using firing rates and the Spike Time Tiling Coefficient (STTC), enabling simultaneous assessment of excitability and synchrony. Our results show that seizure-like dynamics arise from nonlinear interactions between network composition, neuronal impairment, and synaptic kinetics. Distinct dynamic regimes emerged, separated by sharp transitions in synchrony and activity patterns. These findings provide a mechanistic link between circadian regulation and seizure susceptibility, supporting the development of chronotherapy approaches for epilepsy.

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bioRxiv2026

A continuum of asynchronous states in cerebral cortex networks, and how they determine responsiveness

The awake brain is known to display asynchronous (AS) states during periods of attention and arousal, but the responsiveness properties of such states remain unclear. Here, we investigate this question using computational models of spiking networks of excitatory and inhibitory neurons, mimicking recurrently-connected networks in layer 2/3 of the cerebral cortex. The networks can generate a continuum of AS states, but with different responsiveness characteristics. By using a mean-field model to infer the dynamic properties of the system, we find that there are two families of AS states, which we call "underdamped" (UD) and "overdamped" (OD). Responsiveness is maximised at the transition between OD and UD states, which identifies a "working point" that may present advantageous computational properties.

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bioRxiv2026

The transfer function as a tool to reduce morphological models into point-neuron models

Building a simple model that precisely and functionally characterizes a neuron is a challenging and important task to select the best concise and computationally efficient model. However, this type of work has only been done for subthreshold properties of neurons. Here, we take a different perspective and suggest a method to obtain point-neuron models from morphologically-detailed models with dendrites. To do this, we focus on the functional characterization of the neuron response under in vivo conditions, and compute the transfer function of the detailed model. The parameters of this transfer function, in terms of mean voltage, voltage standard deviation and correlation time, can be used to compute the "best" point-neuron model that generates a transfer function very close to that of the morphologically-detailed model. We illustrate this approach for two very different neuronal morphologies, one from Drosophila larvae and one from mammals. In conclusion, this approach provides a tool to generate point-neuron models from detailed models, based on a functional characterization of the neuron response. Significance StatementThis study provides a new computational method to reduce morphological models into point-neuron models. To do so, we calculate the transfer function parameters, ie the voltage standard deviation, the mean voltage and the correlation time, of the morphological model and fit a point neuron-model onto this data. Here, we successfully apply this approach for two very different neuron morphologies, a drosophila neuron and a rat motoneuron.

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bioRxiv2025

Cholinergic heterogeneity facilitates synchronization and information flow in a whole-brain model

The human brain displays substantial regional variability in molecular, anatomical, and physiological organization. Yet, how this heterogeneity shapes large-scale neuronal dynamics remains poorly understood. To address this question, we employed a biologically informed whole-brain computational model capable of generating distinct brain states, from awake-like to sleep-like regimes. Our model was constrained by empirical human structural connectivity and spatial maps of cholinergic receptor gene expression, thereby embedding regional neuromodulatory variability into a macroscopic framework. We found that incorporating cholinergic heterogeneity had a significant impact on brain dynamics: it not only facilitated network synchronization but also enhanced information flow between brain regions. Furthermore, we addressed a particularly intricate dynamic regime characterized by the coexistence of localized sleep-like activity within otherwise awake-like states. We showed that the emergence of these slow waves was a byproduct of both regional levels of neuronal adaptation and structural connectivity. In summary, our findings highlight the critical role of molecular and anatomical heterogeneity in shaping global brain dynamics, suggesting new avenues for linking microscale diversity to macroscale function.

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bioRxiv2025

Simulated 5-HT2A receptor activation accounts for the high complexity of brain activity during psychedelic states

Serotonergic psychedelics, such as LSD, psilocybin, and DMT, have strong effects on human brain activity, yet their mechanisms of action are only partially understood. Here, we present a biophysically-based mean-field model that integrates cellular and network-level details to simulate the effects of these compounds at different spatial scales. By incorporating the brain-wide distribution of 5-HT2A receptors, our model mechanistically links receptor activation through reducing leak membrane potassium conductances, based on electrophysiological data. Our simulations reveal that this microscopic perturbation leads to the emergence of a brain state characterised by asynchronous irregular dynamics with increased firing rates and alterations in spectral power, in particular reduction of alpha-frequency bands, consistent with empirical findings. This change in dynamics is accompanied by an increase in spontaneous complexity, as quantified by the Lempel-Ziv complexity index, as observed experimentally. Furthermore, our model accurately replicates experimental findings regarding the Perturbational Complexity Index (PCI), demonstrating that PCI does not increase significantly by psychedelic drug administration. This crucial dissociation, where spontaneous complexity and spectral power are affected while perturbational complexity is preserved, highlights the distinct neurophysiological substrates underlying different metrics in psychedelic states. Our multiscale model provides a robust, mechanistic framework for understanding how psychedelics modulate global brain activity.

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bioRxiv2025

Convergent information flows explain recurring firing patterns in cerebral cortex

Cortical population events, short-lived patterns of neuronal activity that recur with some consistency, are central to sensorimotor coordination. These reproducible firing patterns are often attributed to attractor dynamics, supported by strong mutual connectivity. However, using multi-modal datasets -- including 2-photon imaging, electrophysiology, and electron microscopy -- we show that these reproducible patterns do not involve strongly interconnected neurons. Instead, we show that cortical networks exhibit hierarchical modularity, with core neurons acting as high-information-flow nodes positioned at module interfaces. These cores funnel activity but lack structural signatures of pattern completion units expected in an attractor network. Using computational models, we find that distance-dependent connectivity is necessary and sufficient to generate the modularity and transient reproducible events observed in cortex. Our findings suggest that cortical networks are instead pre-configured to support sensorimotor coordination. This work redefines the structural and dynamical basis of cortical activity, highlighting the link between modular structure and function.

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bioRxiv2025

Neuromodulators control neuronal dynamics through feature space reshaping

The activity of neurons in the central nervous system changes drastically across different states like asleep or awake, attentive or drowsy, executing motor commands or resting. Neuromodulation supports these changes by affecting neuronal excitability. Much work has been devoted to understanding the effects of neuromodulators on various neurons. However, we still lack a cohesive picture of how neuromodulation affects neuronal dynamics. Here, we analysed electrophysiological data from published papers and extracted features for seven types of neurons from five areas of the human and rodent central nervous system, under the effect of five neuromodulators. We studied neuromodulation dynamics using the widespread, simple, yet biologically accurate, Adaptive Exponential Integrate-and-Fire model. We have found that different neuromodulators remap the feature space of neurons into non-overlapping clusters. This work organises our knowledge about neuromodulation in a compact format, useful for modellers and theoreticians.

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bioRxiv2025

Listening to Your Own Brain Waves Sound Enhances Your Sleep Quality and Quantity

This pilot study examined the effects of relaxing personalized sound sequences (PSS), derived from individual slow-wave brain activity on sleep in adults with subjective insomnia complaints. Thirteen participants underwent one-night polysomnography to record delta wave activity (0.5-4 Hz), which was then transformed into individualized sound sequences. A randomized, single-blind, crossover protocol was then conducted at home, including two conditions of 3 to 5 consecutive nights: listening to the PSS and a non-personalized placebo sound sequence (PLA) for 30 minutes at bedtime. Objective sleep was assessed using a dry-electroencephalographic (EEG) headband and subjective sleep with a digital sleep diary. Compared to PLA, the PSS condition significantly increased total sleep time ({Delta} = +18.9 min, p = 0.05) and REM sleep proportion ({Delta} = +2.3%, p < 0.05), reduced REM latency ({Delta} = -16.6 min, p < 0.05) and improved overall sleep quality score ({Delta} = +1.4 A.U., p < 0.05). Participants with the shorter sleep duration (< 390 min, n = 5) and longer sleep onset latencies (> 20 min, n = 4) in PLA condition experienced greater improvements with PSS. These preliminary results suggest that listening to ones own slow brain waves converted into sound may improve both sleep quality and quantity in individuals with moderate insomnia, with potentially enhanced benefits for those with more severe sleep difficulties. HighlightsO_LIPersonalized soundtracks were created by transforming each participants N3 slow-wave activity into audio sequences to listen to before bedtime. C_LIO_LIParticipants reported significantly better subjective sleep quality when listening to their own brainwave-based soundtracks compared to placebo. C_LIO_LIObjective measures increased total sleep time and REM sleep during nights with personalized sound exposure. C_LI

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bioRxiv2025

Comprehensive profiling of anaesthetised brain dynamics across phylogeny

Intrinsic dynamics of neuronal circuits shape information processing. Combining neuroimaging with causal perturbation offers the opportunity to understand how local dynamics mediate the link between neurobiology and functional repertoire. We compile a unique dataset of multi-scale neural activity during wakefulness and anaesthetic-induced suppression of information processing encompassing human, macaque, marmoset, mouse, zebrafish and nematode. Applying massive feature extraction, we comprehensively characterise local neural dynamics across >6,000 time-series features. Using dynamics as a common space for cross-species comparison reveals a conserved dynamical profile of anaesthesia across species, characterised by shorter intrinsic timescales of neural activity and dampened interregional synchrony. This dynamical regime is experimentally reversed in vivo by deep-brain stimulation of the macaque centromedian thalamus, restoring behavioural responsiveness. Spatially, this conserved dynamical phenotype covaries with conserved transcriptional profiles of excitatory and inhibitory neurotransmission across human, macaque, marmoset and mouse cortex. Biophysical modelling provides a mechanistic link between the macroscale dynamical phenotype of anaesthesia, and microscale effects of key molecular targets on the timescales of synaptic excitation and inhibition. Altogether, comprehensive dynamical phenotyping reveals a shared neural endpoint of anaesthesia: across species and scales, anaesthetics induce spatio-temporal isolation of local neural activity.

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bioRxiv2025

Biologically realistic mean field model of spiking neural networks with fast and slow inhibitory synapses

We present a mean field model for a spiking neural network of excitatory and inhibitory neurons with fast GABAA and nonlinear slow GABAB inhibitory conductance-based synapses. This mean field model can predict the spontaneous and evoked response of the network to external stimulation in asynchronous irregular regimes. The model displays theta oscillations for sufficiently strong GABAB conductance. Optogenetic activation of interneurons and an increase of GABAB conductance caused opposite effects on the emergence of gamma oscillations in the model. In agreement with direct numerical simulations of neural networks and experimental data, the mean field model predicts that an increase of GABAB conductance reduces gamma oscillations. Furthermore, the slow dynamics of GABAB synapses regulates the appearance and duration of transient gamma oscillations, namely gamma bursts, in the mean field model. Finally, we show that nonlinear GABAB synapses play a major role to stabilize the network from the emergence of epileptic seizures.

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bioRxiv2024

Modeling impairment of ionic regulation with extended Adaptive Exponential integrate-and-fire models

To model the dynamics of neuron membrane excitability many models can be considered, from the most biophysically detailed to the highest level of phenomenological description. Recent works at the single neuron level have shown the importance of taking into account the evolution of slow variables such as ionic concentration. A reduction of such a model to models of the integrate-and-fire family is interesting to then go to large network models. In this paper, we introduce a way to consider the impairment of ionic regulation by adding a third, slow, variable to the adaptive Exponential integrate-and-fire model (AdEx). We then implement and simulate a network including this model. We find that this network was able to generate normal and epileptic discharges. This model should be useful for the design of network simulations of normal and pathological states.

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bioRxiv2024

Multiscale modelling of neuronal dynamics in hippocampus CA1

The development of biologically realistic models of brain microcircuits and regions is currently a very relevant topic in computational neuroscience. From basic research to clinical applications, there is an increasing demand for accurate models that incorporate local cellular and network specificities, able to capture a broad range of dynamics and functions associated with given brain regions. One of the main challenges of these models is the passage between different scales, going from the microscale (cellular) to the meso (microcircuit) and macroscale (region or whole-brain level), while keeping at the same time a constraint on the demand of computational resources. One novel approach to this problem is the use of mean-field models of neuronal activity to build large-scale simulations. This provides an effective solution to the passage between scales with relatively low computational demands, which is achieved by a drastic reduction in the dimensionality of the system. In this paper we introduce a multiscale modelling framework for the hippocampal CA1, a region of the brain that plays a key role in functions such as learning, memory consolidation and navigation. Our modelling framework goes from the single cell level to the macroscale and makes use of a novel mean-field model of CA1, introduced in this paper, to bridge the gap between the micro and macro scales. To develop the mean-field model we make use of a recently introduced formalism based on a bottom-up approach that is easily applicable to different neuronal models and cell types. We test and validate the model by analyzing the response of the system to the main brain rhythms observed in the hippocampus and comparing our results with the ones of the corresponding spiking network model of CA1. In addition, we show an example of the implementation of our model to study a stimulus propagation at the macro-scale, and we compare the results obtained from our model with the corresponding spiking network model of the whole CA1 area.

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bioRxiv2024

Transcranial direct current stimulation modulates primate brain dynamics across states of consciousness.

The resting primate brain is traversed by spontaneous functional connectivity patterns that show striking differences between conscious and unconscious states. Transcranial direct current stimulation, a non-invasive neuromodulatory technique, can improve signs of consciousness in disorders of consciousness; however, can it influence both conscious and unconscious dynamic functional connectivity? We investigated the modulatory effect of prefrontal cortex (PFC) transcranial direct current stimulation (tDCS) on brain dynamics in awake and anesthetized non-human primates using functional MRI. In awake macaques receiving either anodal or cathodal tDCS, we found that cathodal stimulation robustly disrupted the repertoire of functional connectivity patterns, increased structure-function correlation, decreased Shannon entropy, and favored transitions towards anatomically-based patterns. Under deep sedation, anodal tDCS significantly altered brain pattern distribution and reduced structure-function correlation. The prefrontal stimulation also modified dynamic connectivity arrangements typically associated with consciousness and unconsciousness. Our findings offer compelling evidence that PFC tDCS induces striking modifications in the fMRI-based dynamic organization of the brain across different states of consciousness. This study contributes to an enhanced understanding of tDCS neuromodulation mechanisms and has important clinical implications for disorders of consciousness.

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bioRxiv2023

A multiscale model of striatum microcircuit dynamics

The striatum is the largest structure in the basal ganglia, and is known for its key role in functions such as learning and motor control. Studying these aspects requires investigating cellular/microcircuits mechanisms, in particular related to learning, and how these small-scale mechanisms affect large-scale behavior, and its interactions with other structures, such as the cerebral cortex. In this paper, we provide a multiscale approach to investigate these aspects. We first investigate striatum dynamics using spiking networks, and derive a mean-field model that captures these dynamics. We start with a brief introduction to the microcircuit of the striatum and we describe, step by step, the construction of a spiking network model, and its mean-field, for this area. The models include explicitly the different cell types and their intrinsic electrophysiological properties, and the synaptic receptors implicated in their recurrent interactions. Then we test the mean-field model by analyzing the response of the striatum network to the main brain rhythms observed experimentally, and compare this response to that predicted by the mean-field. We next study the effects of dopamine, a key neuromodulator in the basal ganglia, on striatal neurons. Integrating dopamine receptors in the spiking network model leads to emerging dynamics, which are also seen in the mean-field model. Finally, we introduce a basic implementation of reinforcement learning (one of the main known functions of the basal-ganglia) using the mean-field model of the striatum microcircuit. In conclusion, we provide a multiscale study of the striatum microcircuits and mean-field, that capture its response to periodic inputs, the effect of dopamine and can be used in reinforcement learning paradigms. Given that several mean-field models have been previously proposed for the cerebral cortex, the mean-field model presented here should be a key tool to investigate large-scale interactions between basal ganglia and cerebral cortex, for example in motor learning paradigms, and to integrate it in large scale and whole-brain simulations.

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bioRxiv2023

EEG and computational aspects of how aging affects sleep slow waves

Sleep slow-waves have been reported to vary with age in human subjects, as well as in mouse, but the underlying mechanisms remain unclear. Here, we perform a precise quantification of the effect of aging on the shape and dynamics of sleep slow waves, in a large cohort of human subjects recorded with the electro-encephalogram (EEG) during sleep. The fine-structure analysis of slow waves reveals that they slow-down, increase of variability and decrease in amplitude with age. We next investigate a computational model of the genesis of slow-wave activity and model the aging by a global decrease of the strength of the external excitatory drive to the network. This simple model reproduces some of the main features observed in the EEG, suggesting that changes of long-range excitatory connection strength may explain the evolution of slow-waves with age.

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