Biology subjects

Claudi, F.

Publications and source records attributed to Claudi, F..

8 recordsLinked to original sources

bioRxiv2026

Toward A Whole-Brain Interface Enabled by High-Resolution Surface Electrophysiology

The goal of building a whole-brain interface - capable of generating a real-time digital representation of the state of the entire central nervous system - has long been framed as a hardware challenge of recording simultaneously from ever-larger numbers of microelectrodes. Advances in high-resolution neural interfaces now enable electrophysiologic interaction with the human nervous system at spatial and temporal resolutions previously unattainable without disrupting tissue architecture or physiology. These capabilities create new opportunities for mechanistic neuroscience, neuroprosthetics, and precision-guided neurosurgery. With the advent of modern microelectrode array technology, however, the whole-brain interface has also become a data challenge. Rather than pursuing large-scale human brain recording purely through a single device placed in a single patient, we demonstrate here that systematic multi-subject aggregation, across brain regions and behavioral paradigms, in a common anatomical framework, can achieve something functionally similar to a whole-brain interface. Here we present an approach to a functional atlas of the human central nervous system derived from high-resolution surface electrophysiology recordings obtained in the first 70 patients implanted with an anatomically conformal, thin-film neural interface positioned in more than 100 locations. The atlas comprises >100,000 electrode deployments and catalogs placement across a broad range of neuroanatomic targets spanning the motor, sensory, language, auditory, and associative domains, as well as brainstem and spinal cord structures that have historically been difficult to access at high electrophysiologic resolution. Across participants, aggregated recordings reveal somatotopic organization, including digit representations that follow a lateral-to-medial ordering consistent with classical homuncular maps, with transitions between adjacent representations occurring over spatial scales of approximately 0.8 - 2.0 mm; canonical frequency-band signatures, such as movement-related beta-band suppression and focal high-gamma (70 - 200 Hz) increases during motor and speech behaviors; and task-modulated population dynamics in both evoked and spontaneous activity across cortical, brainstem, and spinal cord regions. These findings yield insight into patterns of mesoscale electrophysiological dynamics conserved across the entire human central nervous system, suggesting that fine-scale functional organization is preserved across individuals when aligned to common anatomical landmarks. We further demonstrate several applications of these high-resolution recordings, including real-time neural decoding from upper extremity motor cortex to enable real-time neuroprosthetic control, and from ventrolateral cortex to decode cortical correlates of spoken language, facial movements, and complex facial expressions of emotion, supporting initial feasibility of combined verbal and nonverbal communication interfaces. Together, these results establish a paradigm for approaching large-scale interfaces with the human brain, as well as a starting point for a scalable, extensible atlas of human electrophysiologic function designed to accelerate discovery across basic neuroscience, translational research, and clinical neurotechnology.

neuroscience↗

bioRxiv2025

A theory and recipe to construct general and biologically plausible integrating continuous attractor neural networks

Across the brain, circuits with continuous attractor dynamics underpin the representation and storage in memory of continuous variables for motor control, navigation, and mental computations. The represented variables have various dimensions and topologies (lines, rings, euclidean planes), and the circuits exhibit continua of fixed points to store these variables, and the ability to use input velocity signals to update and maintain the representation of unobserved variables, effectively integrating the incoming velocity signal. Integration constitutes a general computational strategy that enables variable state estimation when direct observation of the variable is not possible, suggesting that it may play a critical role in other cognitive processes. While some neural network models for integration exist, a comprehensive theory for constructing neural circuits with a given topology and integration capabilities is lacking. Here, we present a theoretically-driven design framework, Manifold Attractor Direct Engineering (MADE), to automatically, analytically, and explicitly construct biologically plausible continuous attractor neural networks with diverse user-specified topologies. We show how these attractor networks can be endowed with accurate integration functionality through biologically realistic circuit mechanisms. MADE networks closely resemble biological circuits where the attractor mechanisms have been characterized. Additionally, MADE offers innovative and minimal circuit models for uncharacterized topologies, enabling a systematic approach to developing and testing mathematical theories related to cognition and computation in the brain.

neuroscience↗

bioRxiv2023

Tonically active GABAergic neurons in the dorsal periaqueductal gray control the initiation and execution of instinctive escape

To avoid predation, animals perform defensive actions that are both instinctive and adaptable to the environment. In mice, the decision to escape from imminent threats is implemented by a feed-forward circuit in the midbrain, where excitatory VGluT2+ neurons in the dorsal periaqueductal gray (dPAG) compute escape initiation and escape vigour from threat evidence. Here we show that GABAergic VGAT+ neurons in the dPAG dynamically control this process by modulating the excitability of excitatory escape neurons. Using in vitro patchclamp and in vivo neural activity recordings in freely behaving mice we found that VGAT+ dPAG neurons fire action potentials tonically in the absence of synaptic inputs and are a major source of synaptic inhibition to VGluT2+ dPAG neurons. Activity in these spontaneously firing VGAT+ cells transiently decreases at escape onset and increases during escape, peaking at escape termination. Optogenetically increasing or decreasing VGAT+ dPAG activity bidirectionally changes the probability of escape when the stimulation is delivered at the time of threat onset, and the duration of escape when delivered after escape initiation. We conclude that the activity of tonically firing VGAT+ dPAG neurons sets a threshold for escape initiation and controls the execution of the flight locomotor action.

neuroscience↗

bioRxiv2021

Innate heuristics and fast learning support escape route selection in mice

When faced with imminent danger, animals must rapidly take defensive actions to reach safety. Mice can react to innately threatening stimuli in less than 250 milliseconds [1] and, in simple environments, use spatial memory to quickly escape to shelter [2,3]. Natural habitats, however, often offer multiple routes to safety which animals must rapidly identify and choose from to maximize the chances of survival [4]. This is challenging because while rodents can learn to navigate complex mazes to obtain rewards [5,6], learning the value of different routes through trial-and-error during escape from threat would likely be deadly. Here we have investigated how mice learn to choose between different escape routes to shelter. By using environments with paths to shelter of varying length and geometry we find that mice prefer options that minimize both path distance and path angle relative to the shelter. This choice strategy is already present during the first threat encounter and after only ~10 minutes of exploration in a novel environment, indicating that route selection does not require experience of escaping. Instead, an innate heuristic is used to assign threat survival value to alternative paths after rapidly learning the spatial environment. This route selection process is flexible and allows quick adaptation to arenas with dynamic geometries. Computational modelling of different classes of reinforcement learning agents shows that the observed behavior can be replicated by model-based agents acting in an environment where the shelter location is rewarding during exploration. These results show that mice combine fast spatial learning with innate heuristics to choose escape routes with the highest survival value. They further suggest that integrating priors acquired through evolution with knowledge learned from experience supports adaptation to changing environments while minimizing the need for trial-and-error when the errors are very costly.

neuroscience↗

bioRxiv2021

Differential geometry methods for constructing manifold-targeted recurrent neural networks.

Neural computations can be framed as dynamical processes, whereby the structure of the dynamics within a neural network are a direct reflection of the computations that the network performs. A key step in generating mechanistic interpretations within this computation through dynamics framework is to establish the link between network connectivity, dynamics and computation. This link is only partly understood. Recent work has focused on producing algorithms for engineering artificial recurrent neural networks (RNN) with dynamics targeted to a specific goal manifold. Some of these algorithms only require a set of vectors tangent to the target manifold to be computed, and thus provide a general method that can be applied to a diverse set of problems. Nevertheless, computing such vectors for an arbitrary manifold in a high dimensional state space remains highly challenging, which in practice limits the applicability of this approach. Here we demonstrate how topology and differential geometry can be leveraged to simplify this task, by first computing tangent vectors on a low-dimensional topological manifold and then embedding these in state space. The simplicity of this procedure greatly facilitates the creation of manifold-targeted RNNs, as well as the process of designing task-solving on-manifold dynamics. This new method should enable the application of network engineering-based approaches to a wide set of problems in neuroscience and machine learning. Furthermore, our description of how fundamental concepts from differential geometry can be mapped onto different aspects of neural dynamics is a further demonstration of how the language of differential geometry can enrich the conceptual framework for describing neural dynamics and computation.

neuroscience↗

bioRxiv2021

Tools for accurate post hoc determination of marker location within whole-brain microscopy images

To interpret in vivo experiments designed to understand brain function, high-resolution whole-brain microscopy provides a means for post hoc determination of the location of implanted devices and recorded cells in three dimensional brain space that is a critical step for data interrogation. Here we have developed Python-based tools (brainreg and brainreg-segment) to accurately map, in a common coordinate space, the position of dye-labelled probe tracks and two-photon imaged cell populations expressing fluorescent protein. The precise location of probes and cells were validated using physiological recordings and human raters that indicate accuracy levels to less than 70{micro}m. These flexible, open-source methodologies are expected to further evolve with need and to deliver the anatomical precision that is necessary for understanding the functional architecture of the brain.

neuroscience↗

bioRxiv2020

Brainrender. A python based software for visualisation of neuroanatomical and morphological data.

The recent development of high-resolution three-dimensional (3D) digital brain atlases and high-throughput brain wide imaging techniques has fueled the generation of large datasets that can be registered to a common reference frame. This registration facilitates integrating data from different sources and resolutions to assemble rich multidimensional datasets. Generating insights from these new types of datasets depends critically on the ability to easily visualize and explore the data in an interactive manner. This is, however, a challenging task. Currently available software is dedicated to single atlases, model species or data types, and generating 3D renderings that merge anatomically registered data from diverse sources requires extensive development and programming skills. To address this challenge, we have developed brainrender: a generic, open-source Python package for simultaneous and interactive visualization of multidimensional datasets registered to brain atlases. Brainrender has been designed to facilitate the creation of complex custom renderings and can be used programmatically or through a graphical user interface. It can easily render different data types in the same visualization, including user-generated data, and enables seamless use of different brain atlases using the same code base. In addition, brainrender generates high-quality visualizations that can be used interactively and exported as high-resolution figures and animated videos. By facilitating the visualization of anatomically registered data, brainrender should accelerate the analysis, interpretation, and dissemination of brain-wide multidimensional data.

neuroscience↗

bioRxiv2019

Cerebellar-recipient motor thalamus drives behavioral context-specific movement initiation

To initiate goal-directed behavior, animals must transform sensory cues into motor commands that generate appropriately timed actions. Sensorimotor transformations along the cerebellar-thalamocortical pathway are thought to shape motor cortical output and movement timing, but whether this pathway initiates goal-directed movement remains poorly understood. Here, we recorded and perturbed activity in cerebellar-recipient regions of motor thalamus (dentate / interpositus nucleus-recipient regions, MThDN/IPN) and primary motor cortex (M1) in mice trained to execute a cued forelimb lever push task for reward. MThDN/IPN population responses were dominated by a time-locked increase in activity immediately prior to movement that was temporally uncoupled from cue presentation, providing a fixed latency feedforward motor timing signal to M1FL. Blocking MThDN/IPN output suppressed cued movement initiation. Stimulating the MThDN/IPN thalamocortical pathway in the absence of the cue recapitulated cue-evoked M1 membrane potential dynamics and forelimb behavior in the learned behavioral context, but generated semi-random movements in an altered behavioral context. Thus, cerebellar-recipient motor thalamocortical input to M1 is indispensable for the generation of motor commands that initiate goal-directed movement, refining our understanding of how the cerebellar-thalamocortical pathway contributes to movement timing.

neuroscience↗