Biology subjects

Peyrache, A.

Publications and source records attributed to Peyrache, A..

At least 19 recordsLinked to original sources

bioRxiv2025

Coherent dynamics of thalamic head-direction neurons irrespective of input

The generation of the head-direction (HD) signal, a cornerstone of the brains navigation system, is classically attributed to the lateral mammillary nucleus (LMN) and associated brainstem structures, where attractor-like dynamics are thought to maintain population coherence. Here, using multi-site recordings and optogenetic perturbations along the mammillary-thalamic-cortical pathway, we demonstrate that HD neurons in the anterodorsal nucleus of the thalamus (ADN) maintain coherent population dynamics even when their LMN inputs become decorrelated during non-REM sleep. These findings reveal that thalamic coherence does not strictly depend on structured input from the LMN; instead, it can emerge from local thalamic processes involving shared inhibition and non-linear responses. Together, our findings reveal a previously unrecognized, state-dependent shift in the circuit organizing the HD signal, establishing the thalamus as an active substrate capable of independently sustaining internal representations across brain states.

neuroscience↗

bioRxiv2025

Coherent thalamic inputs organize head direction signal in the medial entorhinal cortex

Successful navigation relies on signals that remain stable despite environmental changes. This stability can arise by constraining neuronal activity to low-dimensional subspaces. During sleep, when external input is reduced, pairwise coordination persists in the spatial navigation system, as observed for head-direction (HD) cells of the anterodorsal nucleus (ADn) and grid cells of the medial entorhinal cortex (MEC). As ADn is crucial for spatial representation in the MEC, we hypothesized that coherent HD input underlies MEC organization. To test this, we performed simultaneous recordings in the ADn and MEC during wakefulness and sleep. We found that HD cell pairs maintained stable coordination across both states, and that MEC cell coordination was partly driven by common inputs from ADn HD cells. These results suggest that MEC activity is shaped, in part, by coherent thalamic HD signals, supporting stable network organization.

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bioRxiv2025

Age-dependent alterations in the head-direction signal in a rat model of Fragile X Syndrome

Fragile X Syndrome (FXS) is a common inherited monogenic cause of autism spectrum disorder and intellectual disability (ASD/ID), but the neural mechanisms underlying its symptoms remain unclear. Here, we investigate the development of the head-direction (HD) system in a rat model of FXS (Fmr1-/y rats) and its role in spatial cognition. Using high-density silicon probes, we recorded neuronal activity in the postsubiculum (PoSub), a cortical hub of the HD system, during exploration and sleep in juvenile and adult Fmr1-/y and wild-type (WT) rats. Surprisingly, juvenile Fmr1-/y rats exhibited enhanced HD tuning characterized by sharper directional tuning curves and improved stability of the HD signal in relation to the external environment. This enhancement was intrinsic to the HD circuit and persisted across behavioral and sleep states. However, by adulthood, HD tuning in Fmr1-/y rats became unstable, with increased drift in the allocentric reference frame despite intact intrinsic HD dynamics. These findings reveal a transient enhancement of spatial coding in the HD system of juvenile Fmr1-/y rats, followed by a degradation in adulthood, mirroring the developmental regression often observed in autism. Overall, our work reveals network-level dysfunctions in FXS and their developmental trajectories.

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bioRxiv2024

Visual objects refine head direction coding

Animals use visual objects to guide navigation-related behaviors, from hunting prey, to escaping predators, to exploring the world. However, little is known about where visual objects are encoded in the mouse brain or how they impact processing in the spatial navigation system. Using functional ultrasound imaging in mice, we conducted a brain-wide screen and identified brain areas that were preferentially activated by images of objects compared to scrambled versions of the same images. While visual cortical areas did not show a significant preference, regions associated with spatial navigation were preferentially activated by visual objects. Electrophysiological recordings in postsubiculum, the primary cortical area of the head direction (HD) system, further confirmed a preference for visual objects, which was present in both HD cells and fast-spiking interneurons. Finally, we found that visual objects dynamically modulated HD cells, selectively increasing firing rates of HD cells aligned with a visual landmarks direction, while decreasing activity in HD cells coding for other directions. These results reveal that visual objects refine population-level coding of head direction.

neuroscience↗

bioRxiv2024

Months-long stability of the head-direction system

Spatial orientation is a universal ability that allows animals to navigate their environment. In mammals, the head-direction (HD) system is an essential component of the brains navigation system, yet the stability of its underlying neuronal code remains unclear. Here, by longitudinally tracking the activity of the same HD cells in freely moving mice, we show that the internal organization of population activity in the HD system was preserved for several months. Furthermore, the HD system developed a unique mapping between its internal organization and spatial orientation in each environment. This was not affected by visits to other environments and was stabilized with experience. These findings demonstrate that stable neuronal code supports the sense of direction and forms long-lasting orientation memories.

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bioRxiv2024

Sequential predictive learning is a unifying theory for hippocampal representation and replay

The mammalian hippocampus contains a cognitive map that represents an animals position in the environment 1 and generates offline "replay" 2,3 for the purposes of recall 4, planning 5,6, and forming long term memories 7. Recently, its been found that artificial neural networks trained to predict sensory inputs develop spatially tuned cells 8, aligning with predictive theories of hippocampal function 9-11. However, whether predictive learning can also account for the ability to produce offline replay is unknown. Here, we find that spatially-tuned cells, which robustly emerge from all forms of predictive learning, do not guarantee the presence of a cognitive map with the ability to generate replay. Offline simulations only emerged in networks that used recurrent connections and head-direction information to predict multi-step observation sequences, which promoted the formation of a continuous attractor reflecting the geometry of the environment. These offline trajectories were able to show wake-like statistics, autonomously replay recently experienced locations, and could be directed by a virtual head direction signal. Further, we found that networks trained to make cyclical predictions of future observation sequences were able to rapidly learn a cognitive map and produced sweeping representations of future positions reminiscent of hippocampal theta sweeps 12. These results demonstrate how hippocampal-like representation and replay can emerge in neural networks engaged in predictive learning, and suggest that hippocampal theta sequences reflect a circuit that implements a data-efficient algorithm for sequential predictive learning. Together, this framework provides a unifying theory for hippocampal functions and hippocampal-inspired approaches to artificial intelligence.

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bioRxiv2024

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) with hippocampal rhythms in male rats acquiring and switching between spatial and visual discrimination tasks in a Y-maze. ACC population and single unit activity responded 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 detection of contingency changes for cognitive flexibility, a function that is attenuated after it is damaged.

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bioRxiv2023

Hyperpolarization-Activated Currents Drive Neuronal Activation Sequences in Sleep

Sequential neuronal patterns are believed to support information processing in the cortex, yet their origin is still a matter of debate. We report that neuronal activity in the mouse head-direction cortex (HDC, i.e., the post-subiculum) was sequentially activated along the dorso-ventral axis during sleep at the transition from hyperpolarized "DOWN" to activated "UP" states, while representing a stable direction. Computational modelling suggested that these dynamics could be attributed to a spatial gradient of hyperpolarization-activated current (Ih), which we confirmed in ex vivo slice experiments and corroborated in other cortical structures. These findings open up the possibility that varying amounts of Ih across cortical neurons could result in sequential neuronal patterns, and that travelling activity upstream of the entorhinal-hippocampal circuit organises large-scale neuronal activity supporting learning and memory during sleep. HighlightsO_LINeuronal Activation Sequence in HDC: neuronal activity was sequentially reinstated along the dorsoventral axis of the HDC at UP state but not DOWN state onset. C_LIO_LIRole of Ih in Sequence Generation: Incorporating the hyperpolarization-activated current (Ih) into computational models, we identified its pivotal role in UP/DOWN dynamics and neuronal activity sequences. C_LIO_LIEx Vivo Verification: slice physiology revealed a dorsoventral gradient of Ih in the HDC. C_LIO_LIImplications Beyond HDC: the gradient of Ih could account for the sequential organization of neuronal activity across various cortical areas. C_LI

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bioRxiv2022

Pynapple: a toolbox for data analysis in neuroscience.

Datasets collected in neuroscientific studies are of ever-growing complexity, often combining high dimensional time series data from multiple data acquisition modalities. Handling and manipulating these various data streams in an adequate programming environment is crucial to ensure reliable analysis, and to facilitate sharing of reproducible analysis pipelines. Here, we present Pynapple, the PYthon Neural Analysis Package, a lightweight python package designed to process a broad range of time-resolved data in systems neuroscience. The core feature of this package is a small number of versatile objects that support the manipulation of any data streams and task parameters. The package includes a set of methods to read common data formats and allows users to easily write their own. The resulting code is easy to read and write, avoids low-level data processing and other error-prone steps, and is open source. Libraries for higher-level analyses are developed within the Pynapple framework but are contained within in a collaborative repository of specialized and continuously updated analysis routines. This provides flexibility while ensuring long-term stability of the core package. In conclusion, Pynapple provides a common framework for data analysis in neuroscience. HighlightsO_LIAn open-source framework for data analysis in systems neuroscience. C_LIO_LIEasy-to-use object-oriented programming for data manipulation. C_LIO_LIA lightweight and standalone package ensuring long-term backward compatibility. C_LI

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bioRxiv2022

Signature of random connectivity in the distributionof neuronal tuning curves.

Understanding the relationship between circuit properties and the organization of neuronal population activity is a fundamental question in neuroscience. The fine tuning of neuronal activity to specific values of environmental or internal features are canonical examples of how information is encoded in the brain, possibly resulting from precisely organized inputs. Yet, in the cortex, finely tuned neurons are often recorded together with neurons whose tuning is much less specific, for example those of inhibitory neurons, and the connectivity statistics accounting for the overall distribution of tuning curves is unclear. Here, using recordings in the mouse head-direction system, we first show both in simulation and analytically that random linear combinations of ideal finely tuned inputs reproduce the distribution of fast-spiking neuron tuning curves, a class of neurons believed to operate in the linear regime. This transformation preserves, on the population level, the singular spectrum of the input tuning curves but the relative power of each singular component is independently distributed in each output cell, leading to a distribution ranging from uni-modal to symmetrically tuned cells. We then generalize the model to a non-linear transformation of the inputs, combined with background inhibition. Using recordings from input neurons in the thalamus, where tuning curves are near-ideal, the model reproduces for various levels of inhibition the entire range of observed neuronal responses in the cortex, from precisely and narrowly tuned neurons to multipeak excitatory cells, as well as symmetrical tuning curves of inhibitory neurons. We replicate these findings in a dataset of hippocampal recordings. In conclusion, the full distribution of tuning curves is a signature of input connectivity statistics, which, for fast-spiking neurons and thalamocortical circuits, is likely to be random rather than specifically organized.

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bioRxiv2022

Off-manifold coding in visual cortex revealed by sleep

Low-dimensional neural manifolds are controversial in part because it is unclear how to reconcile them with high-dimensional representations observed in areas such as primary visual cortex (V1). We addressed this by recording neuronal activity in V1 during slow-wave sleep, enabling us to identify internally-generated low-dimensional manifold structure and evaluate its role during visual processing. We found that movements and visual stimuli were both encoded in the "on-manifold" subspace preserved during sleep. However, only stimuli were encoded in the "off-manifold" subspace, which contains activity patterns that are less likely than chance to occur spontaneously during sleep. This off-manifold activity comprises sparse firing in neurons with the strongest low-dimensional modulation by movement, which paradoxically prevents movement-evoked activity from interfering with stimulus representations. These results reveal an unexpected link between low-dimensional dynamics and sparse coding, which together create a protected off-manifold coding space keeping high-dimensional representations separable from movement-evoked activity.

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bioRxiv2022

Reciprocal representation of encoded features by cortical excitatory and inhibitory neuronal populations.

In the cortex, the interplay between excitation and inhibition determines the fidelity of neuronal representations. However, while the receptive fields of excitatory neurons are often fine-tuned to the encoded features, the principles governing the tuning of inhibitory neurons are still elusive. We addressed this problem by recording populations of neurons in the postsubiculum (PoSub), a cortical area where the receptive fields of most excitatory neurons correspond to a specific head-direction (HD). In contrast to PoSub-HD cells, the tuning of fast-spiking (FS) cells, the largest class of cortical inhibitory neurons, was broad and heterogeneous. However, we found that PoSub-FS cell tuning curves were often fine-tuned in the spatial frequency domain, which resulted in various radial symmetries in their HD tuning. In addition, recordings and specific optogenetic manipulations of the upstream thalamic populations as well as computational models suggest that this population co-tuning in the frequency domain has a local origin. Together, these findings provide evidence that the resolution of neuronal tuning is an intrinsic property of local cortical networks, shared by both excitatory and inhibitory cell populations.

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bioRxiv2022

Flexible cue anchoring strategies enable stable head direction coding in both sighted and blind animals

Vision plays a crucial role in instructing the brains spatial navigation systems. However, little is known about how vision loss affects the neuronal encoding of spatial information. Here, recording from head direction (HD) cells in the anterior dorsal nucleus of the thalamus in mice, we find stable and robust HD tuning in blind animals. In contrast, placing sighted animals in darkness significantly impairs HD cell tuning. We find that blind mice use olfactory cues to maintain stable HD tuning and that prior visual experience leads to refined HD cell tuning in blind adult mice compared to congenitally blind animals. Finally, in the absence of both visual and olfactory cues, the HD attractor network remains intact but the preferred firing direction of HD cells continuously drifts over time. We thus demonstrate remarkable flexibility in how the brain uses diverse sensory information to generate a stable directional representation of space. HighlightsO_LIHead direction (HD) cell tuning in ADn is robust in blind animals, but unstable in sighted animals placed in the dark C_LIO_LIBlind mice use olfaction to stabilize HD cell tuning C_LIO_LIPrior visual experience leads to refined HD cell tuning in blind adult mice C_LIO_LIIn the absence of both vision and olfaction, the HD attractor network in ADn remains intact but the preferred firing direction of HD cells continuously drifts C_LI

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bioRxiv2021

Distinct ground state and activated state modes of spiking in forebrain neurons

Neuronal firing patterns have significant spatiotemporal variability with no agreed upon theoretical framework. Using a combined experimental and modeling approach, we found that spike interval statistics can be described by discrete modes of activity. Of these, a "ground state" (GS) mode of low-rate spiking is universal among forebrain excitatory neurons and characterized by irregular spiking at neuron-specific rates. In contrast, "activated state" (AS) modes consist of spiking at characteristic timescales and regularity that are specific to neuron populations in a given region and brain state. The majority of spiking is contributed by GS mode, while neurons can transiently switch to AS spiking in response to stimuli or in coordination with population activity patterns. We hypothesize that GS spiking serves to maintain a persistent backbone of neuronal activity while AS modes support communication functions.

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bioRxiv2021

Local neuronal excitation and global inhibition during epileptic fast ripples in humans.

Understanding the neuronal basis of epileptiform activity is a major challenge in neurology. Interictal epileptiform discharges are associated with fast ripples (FRs, >200 Hz) in the local field potential (LFP) and are a promising marker of the epileptogenic zone. Here, by using a novel hybrid macro-micro depth electrode, combining classic depth recording of LFP and two or three tetrodes enabling up to 15 neurons in local circuits to be recorded simultaneously, we have characterized neuronal responses to FRs on the same hybrid and other electrodes targeting other brain regions. While FRs were associated with increased neuronal activity in local circuits only, they were followed by inhibition in large-scale networks. Neuronal responses to FRs were homogeneous in local networks but differed across brain areas. Similarly, post-FR inhibition varied across recording locations and subjects and was shorter than typical inter-FR intervals, suggesting that this inhibition is a fundamental refractory process for the networks. These findings demonstrate that FRs engage local and global networks and point to network features that pave the way for new diagnostic and therapeutic strategies.

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bioRxiv2019

Precise coupling of the thalamic head-direction system to hippocampal ripples.

The anterior thalamus is a key relay of neuronal signals within the limbic system. During sleep, the occurrence of hippocampal sharp wave-ripples (SWRs), believed to mediate consolidation of explicit memories, is modulated by thalamocortical network activity, yet how information is routed around SWRs and how this communication depends on neuronal dynamics remains unclear. Here, by simultaneously recording ensembles of neurons in the anterior thalamus and local field potentials in the CA1 area of the hippocampus, we show that the head-direction (HD) cells of the anterodorsal nucleus are set in stable directions immediately before SWRs. This response contrasts with other thalamic cells that exhibit diverse couplings to the hippocampus related to their intrinsic dynamics but independent of their anatomical location. Thus, our data suggest a specific and homogeneous contribution of the HD signal to hippocampal activity and a diverse and cell-specific coupling of non-HD neurons.

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bioRxiv2019

Integrated Open-Source Software for Multiscale Electrophysiology

The methods for electrophysiology in neuroscience have evolved tremendously over the recent years with a growing emphasis on dense-array signal recordings. Such increased complexity and augmented wealth in the volume of data recorded, have not been accompanied by efforts to streamline and facilitate access to processing methods, which too are susceptible to grow in sophistication. Moreover, unsuccessful attempts to reproduce peer-reviewed publications indicate a problem of transparency in science. This growing problem could be tackled by unrestricted access to methods that promote research transparency and data sharing, ensuring the reproducibility of published results.\n\nHere, we provide a free, extensive, open-source software that provides data-analysis, data-management and multi-modality integration solutions for invasive neurophysiology. Users can perform their entire analysis through a user-friendly environment without the need of programming skills, in a tractable (logged) way. This work contributes to open-science, analysis standardization, transparency and reproducibility in invasive neurophysiology.

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bioRxiv2019

The population dynamics of a canonical cognitive circuit

The brain constructs distributed representations of key low-dimensional variables. These variables may be external stimuli or internal constructs of quantities relevant for survival, such as a sense of ones location in the world. We consider that the high-dimensional population-level activity vectors are the fundamental representational currency of a neural circuit, and these vectors trace out a low-dimensional manifold whose dimension and topology matches those of the represented variable. This manifold perspective -- applied to the mammalian head direction circuit across rich waking behaviors and sleep -- enables powerful inferences about circuit representation and mechanism, including: Direct visualization and blind discovery that the network represents a one-dimensional circular variable across waking and REM sleep; fully unsupervised decoding of the coded variable; stability and attractor dynamics in the representation; the discovery of new dynamical trajectories during sleep; the limiting role of external rather than internal noise in the fidelity of memory states; and the conclusion that the circuit is set up to integrate velocity inputs according to classical continuous attractor models.

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