Biology subjects

Brunton, B. W.

Publications and source records attributed to Brunton, B. W..

At least 19 recordsLinked to original sources

bioRxiv2026

Pilifers provide rapid mechanosensory feedback during floral tracking in hawkmoths

Hawkmoths expertly hover over flowers during feeding, accurately tracking floral movements even as flowers gently sway in the wind. They use visual and mechanosensory feedback to adjust their position, while their long, flexible mouthpart, the proboscis, stays inserted inside the nectar spur. Although much is known about how hawkmoths detect and process visual motion, far less is understood about how hawkmoths detect mechanical motion cues. Pilifers are hair bundles at the proboscis-head joint, well-positioned to provide feedback about floral motion. Here, we tested if pilifers could provide mechanosensory feedback essential for successful floral tracking. We computed bending at the proboscis base during floral tracking in freely flying hawkmoths and found that floral movements induce small yet significantly correlated lateral bending. Recordings from pilifer afferents showed that they respond to lateral motions of amplitudes similar to those seen in natural floral tracking behaviors. They respond to different directions and speeds of proboscis motion with an average latency of 1.9 ms. Neural tracing revealed extensive sensory arborizations in the sub-esophageal ganglion of the brain and ventral nerve cord, indicating a feedback pathway to the neck and flight motor system that enables rapid coordination of appendages and body for successful feeding.

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bioRxiv2026

Whole-body 3D kinematics of freely behaving Drosophila

Understanding how nervous systems generate coordinated movement requires precise measurement of body kinematics during natural behavior. The fruit fly, Drosophila, is a model organism with sophisticated behavior and well-studied neural circuits, but tracking fly movements in 3D remains challenging because of their teeny bodies, rapid movements, and frequent self-occlusions. Here we present a pipeline for markerless, full-body 3D pose estimation of fly terrestrial behavior, combining seven synchronized high-speed cameras to capture whole-body kinematics at 800 frames per second. We trained a hybrid 2D/3D deep learning model to track 50 keypoints, then refined them to produce anatomically feasible kinematic trajectories through a retargeting process that solved an inverse kinematics problem constrained by a biomechanical body model. Analysis of 3D kinematics revealed that flies perform grounded running across their full speed range, without transitioning between discrete gaits. Using multi-animal tracking, we found that courting males coordinate both wings during song and modulate body pitch to track the female's vertical position. Our open-source pipeline and large 3D kinematic dataset of fly behavior provide a foundation for neuromechanical modeling and mechanistic studies of motor control in a genetically tractable model organism.

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bioRxiv2026

The digital sphinx: Can a worm brain control a fly body?

Animal intelligence is not purely a product of abstract computation in the brain, but emerges from dynamic interactions between the nervous system and the body. New connectome datasets and musculoskeletal models now enable integrated, closed-loop simulations of the neural and biomechanical systems of the fruit fly Drosophila, an ideal model organism to investigate embodied intelligence. However, many biological parameters of the nervous system and the body, as well as how they interface, remain unknown. To fill such gaps, researchers are turning to deep reinforcement learning (DRL), a data-driven optimization framework, to create virtual animals that imitate the behavior of real animals. Here, we provide a cautionary tale about the interpretation of such models. We constructed a virtual chimera of two phylogenetically distant species: a connectome of the C. elegans nematode worm and a biomechanical model of the fly body. The worm connectome receives sensory information from the fly body, and an artificial neural network is trained with DRL to map worm motor neuron activations to the flys leg actuators. The resulting digital sphinx produces highly realistic fly walking--yet it is biologically meaningless. This exercise teaches us nothing about either animal and exposes a core peril of connectome-body models: behavioral fidelity is achievable without biological fidelity, making such models easy to overinterpret. Done carefully, virtual animals can be powerful partners to biological experiments, but only if their components and interfaces are grounded in biology.

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bioRxiv2025

Connectome simulations identify a central pattern generator circuit for fly walking

Animal locomotion relies on rhythmic body movements driven by central pattern generators (CPGs): neural circuits that produce oscillating output without oscillating input. However, the circuit structure of a CPG for walking is not known in any animal. To identify the cells and synapses that underlie rhythmic leg movement in walking flies, we developed dynamic simulations of the Drosophila ventral nerve cord (VNC) connectomes. A computational activation screen of descending neurons from the central brain identified DNg100--a known command neuron for walking--as the top driver of rhythmic leg motor activity. Simulated network pruning isolated a minimal rhythm-generating circuit consisting of one inhibitory and two excitatory interneurons; this three-neuron circuit was necessary and sufficient for motor rhythms across all six legs and in four connectome datasets. Simulations also predicted that a separate descending pathway (DNb08) drives rhythmic leg movements, which we confirmed experimentally using optogenetics in behaving flies. Our results reveal the cellular identity and synaptic structure of a putative CPG circuit for walking and other rhythmic leg movements in flies.

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bioRxiv2025

TiDHy: Timescale Demixing via Hypernetworks to learn simultaneous dynamics from mixed observations

Neural activity and behavior arise from multiple concurrent time-varying systems, including neuromodulation, neural state, and history; however, most current approaches model these data as one set of dynamics with a single timescale. Here we develop Timescale Demixing via Hypernetworks (TiDHy) as a new computational method to model spatiotemporal data, decomposing them into multiple simultaneous latent dynamical systems that may span orders-of-magnitude different timescales. Specifically, we train a hyper-network to dynamically reweigh linear combinations of latent dynamics. This approach enables accurate data reconstruction, converges to true latent dynamics, and captures multiple timescales of variation. We first demonstrate that TiDHy can demix dynamics and timescales from synthetic data comprising multiple independent switching linear dynamical systems, even when the observations are mixed. Next, with a simulated locomotion behavior dataset, we show that TiDHy accurately captures both the fast dynamics of movement kinematics and the slow dynamics of changing terrains. Finally, in an open-source multi-animal social behavior dataset, we show that the keypoint trajectory dynamics extracted with TiDHy can be used to accurately identify social behaviors of multiple mice. Taken together, TiDHy is a powerful new algorithm for demixing simultaneous latent dynamical systems with applications to diverse computational domains.

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bioRxiv2024

Thalamic Control Over Laminar Cortical Dynamics Across Conscious States

The human brain must support both stable and flexible neural dynamics in order to adapt to changing contexts that are inherently non-linear. The thalamus has been linked to the coordination of these opposing dynamical regimens in the cerebral cortex, however existing methodological approaches have not integrated sufficient neurobiological details with a sensitive measure of neural dynamics that permits sensitivity to time-series non-linearities. Inspired by the field of fluid dynamics, we use a novel approach to show that spontaneous fMRI data exhibits non-trivial fluctuations in predictability over time, akin to a river that has sections of smooth and predictable (laminar) versus rough and unpredictable (non-laminar) fluid flow. We use a combination of pharmacological fMRI, macaque electrophysiology and a large-scale biophysical model of the thalamocortical system to provide robust evidence that the thalamus provides versatile control over globally linear dynamics in the cerebral cortex that characterize conscious states.

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bioRxiv2024

Feedback control of recurrent dynamics constrains learning timescales during motor adaptation

Neural activity has been observed to lie on low-dimensional manifolds, constraining what new behaviors can be easily learned. We propose that beyond this geometric constraint, learning on fast timescales is limited by how neural activity can flow over time within these manifolds--i.e., by the systems underlying dynamics. In primary motor cortex (M1), these neural dynamics are shaped not only by internal recurrence but also by sensory feedback that can continually update cortical activity. Modeling recurrent neural networks with adaptive feedback controllers in a brain-computer interface (BCI) task, we show that feedback-driven dynamics determine not just the robustness but also the flexibility of motor output. Through a control-theoretic approach, we quantitatively link learning speed and success for individual BCI decoders to the structure of input-driven network dynamics. We show that rapid learning is fundamentally limited by the networks controllability--the ease with which inputs can steer neural activity along desired directions. Crucially, this dynamical systems perspective explains a continuous form of experimentally-observed learning variability across decoders with similar geometry, that has not been addressed previously. We also make a testable prediction that rapid adaptation to new BCI decoders depends on upstream input plasticity, such as remapping of sensory feedback, beyond local plasticity within M1. Overall, our work identifies potential network mechanisms for fast but limited motor learning, and clarifies how constraints on learning arise from both the geometry of neural activity and its underlying dynamical structure.

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bioRxiv2024

Sensorimotor delays constrain robust locomotion in a 3D kinematic model of fly walking

Walking animals must maintain stability in the presence of external perturbations, despite significant temporal delays in neural signaling and muscle actuation. Here, we develop a 3D kinematic model with a layered control architecture to investigate how sensorimotor delays constrain robustness of walking behavior in the fruit fly, Drosophila. Motivated by the anatomical architecture of insect locomotor control circuits, our model consists of three component layers: a neural network that generates realistic 3D joint kinematics for each leg, an optimal controller that executes the joint kinematics while accounting for delays, and an inter-leg coordinator. The model generates realistic simulated walking that resembles real fly walking kinematics and sustains walking even when subjected to unexpected perturbations, generalizing beyond its training data. However, we found that the models robustness to perturbations deteriorates when sensorimotor delay parameters exceed the physiological range. These results suggest that fly sensorimotor control circuits operate close to the temporal limit at which they can detect and respond to external perturbations. More broadly, we show how a modular, layered model architecture can be used to investigate physiological constraints on animal behavior.

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bioRxiv2023

Arousal as a universal embedding for spatiotemporal brain dynamics

Neural activity in awake organisms shows widespread and spatiotemporally diverse correlations with behavioral and physiological measurements. We propose that this covariation reflects in part the dynamics of a unified, multidimensional arousal-related process that regulates brain-wide physiology on the timescale of seconds. By framing this interpretation within dynamical systems theory, we arrive at a surprising prediction: that a single, scalar measurement of arousal (e.g., pupil diameter) should suffice to reconstruct the continuous evolution of multidimensional, spatiotemporal measurements of large-scale brain physiology. To test this hypothesis, we perform multimodal, cortex-wide optical imaging and behavioral monitoring in awake mice. We demonstrate that spatiotemporal measurements of neuronal calcium, metabolism, and brain blood-oxygen can be accurately and parsimoniously modeled from a low-dimensional state-space reconstructed from the time history of pupil diameter. Extending this framework to behavioral and electrophysiological measurements from the Allen Brain Observatory, we demonstrate the ability to integrate diverse experimental data into a unified generative model via mappings from an intrinsic arousal manifold. Our results support the hypothesis that spontaneous, spatially structured fluctuations in brain-wide physiology--widely interpreted to reflect regionally-specific neural communication--are in large part reflections of an arousal-related process. This enriched view of arousal dynamics has broad implications for interpreting observations of brain, body, and behavior as measured across modalities, contexts, and scales.

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bioRxiv2023

Presynaptic inhibition selectively suppresses leg proprioception in behaving Drosophila

Controlling arms and legs requires feedback from proprioceptive sensory neurons that detect joint position and movement. Proprioceptive feedback must be tuned for different behavioral contexts, but the underlying circuit mechanisms remain poorly understood. Using calcium imaging in behaving Drosophila, we find that the axons of position-encoding leg proprioceptors are active across behaviors, whereas the axons of movement-encoding leg proprioceptors are suppressed during walking and grooming. Using connectomics, we identify a specific class of interneurons that provide GABAergic presynaptic inhibition to the axons of movement-encoding proprioceptors. The predominant synaptic inputs to these interneurons are descending neurons, suggesting they are driven by predictions of leg movement originating in the brain. Calcium imaging from both the interneurons and their descending inputs confirmed that their activity is correlated with self-generated but not passive leg movements. Overall, our findings elucidate a neural circuit for suppressing specific proprioceptive feedback signals during self-generated movements.

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bioRxiv2023

Neural subspaces of imagined movements in parietal cortex remain stable over several years in humans.

A crucial goal in brain-machine interfacing is long-term stability of neural decoding performance, ideally without regular retraining. Here we demonstrate stable neural decoding over several years in two human participants, achieved by latent subspace alignment of multi-unit intracortical recordings in posterior parietal cortex. These results can be practically applied to significantly expand the longevity and generalizability of future movement decoding devices.

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bioRxiv2023

Intraspecific variation in the placement of campaniform sensilla on the wings of the hawkmoth Manduca sexta

Flight control requires active sensory feedback, and insects have many sensors that help them estimate their current locomotor state, including campaniform sensilla, which are mechanoreceptors that sense strain resulting from deformation of the cuticle. Campaniform sensilla on the wing detect bending and torsional forces encountered during flight, providing input to the flight feedback control system. During flight, wings experience complex spatio-temporal strain patterns. Because campaniform sensilla detect only local strain, their placement on the wing is presumably critical for determining the overall representation of wing deformation; however, how these sensilla are distributed across wings is largely unknown. Here, we test the hypothesis that campaniform sensilla are found in stereotyped locations across individuals of Manduca sexta, a hawkmoth. We found that although campaniform sensilla are consistently found on the same veins or in the same regions of the wings, their total number and distribution can vary extensively. This suggests that there is some robustness to variation in sensory feedback in the insect flight control system. The regions where campaniform sensilla are consistently found provide clues to their functional roles, although some patterns might be reflective of developmental processes. Collectively, our results on intraspecific variation in campaniform sensilla placement on insect wings will help reshape our thinking on the utility of mechanosensory feedback for insect flight control and guide further experimental and comparative studies.

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bioRxiv2023

Dysregulated mammalian estrus cycle rescued by timed activation of VIP neurons in the circadian pacemaker and late afternoon light exposure

Jet lag and shift work disrupt the menstrual cycle and decrease fertility. The circadian pacemaker, the suprachiasmatic nucleus (SCN), is known to modulate ovulation, but the mechanism is unclear. Here we explore this connection by tracking the dynamics of vasoactive intestinal peptide (VIP)-expressing neurons in the SCN in freely-behaving mice. We show that SCNVIP activity is time-of-day- and sex-dependent, and estrous-state-dependent in late afternoon, gating downstream activation of GnRH neurons. Afternoon light, as well as specific activation of SCNVIP neurons, rescues estrous cycle regularity and egg release in animals in altered light conditions, emphasizing the role of SCNVIP neurons as a time-dependent light-responsive switch. Our results reveal the dynamic mechanism by which SCNVIP neurons mediate light responses to regulate estrous states and demonstrate light-induced fertility rescue. One Sentence SummaryModulating and recording the activity of suprachiasmatic VIP neurons in freely behaving mice reveals their regulation of fertility by mediating the response to late afternoon light.

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bioRxiv2022

Nonuniform structural properties of wings confer sensing advantages

Sensory feedback is essential to both animals and robotic systems for achieving coordinated, precise movements. Mechanosensory feedback, which provides information about body deformation and position in space, depends not only on the properties of sensors but also on the structure in which they are embedded. In insects, wing structure plays a particularly important role in flapping flight: in addition to generating aerodynamic forces, wings provide mechanosensory feedback necessary for guiding flight while undergoing dramatic deformations over the course of each wingbeat. However, the role that wing structure plays in determining mechanosensory information is relatively unexplored. Insect wings exhibit characteristic stiffness gradients, with greatest stiffness at the base and leading edge and lowest stiffness at the tip of the trailing edge. Additionally, wings are subject to both aerodynamic and structural damping. The sensory consequences of stiffness gradients and damping are unknown. Here we examine how both the nonuniform stiffness profile of a wing and its damping impacts sensory performance, using finite elements analysis combined with sensor placement optimization approaches. We show that wings with nonuniform stiffness exhibit several advantages over uniform stiffness wings, resulting in higher accuracy of rotation detection and lower sensitivity to the placement of sensors on the wing. Moreover, we show that higher damping generally improves the accuracy with which body rotations can be detected. These results contribute to our understanding of the evolution of the nonuniform stiffness patterns in insect wings, as well as suggest design principles for robotic systems.

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bioRxiv2022

Molecular markers of mechanosensation in glycinergic neurons in the avian lumbosacral spinal cord

Birds are exceptionally adept at controlling their body position. For example, they can coordinate rapid movements of their body while stabilizing their head. Intriguingly, this ability may rely in part on a mechanosensory organ in the avian lower spinal cord called the lumbosacral organ (LSO). However, molecular mechanotransduction mechanisms have not been identified in the avian spinal cord. Here, we report the presence of glycinergic neurons in the LSO that exhibit immunoreactivity for myosin7a and epsin, molecules essential for function and maintenance of hair cells in the inner ear. Specifically, we find glycinergic cell bodies near the central canal and processes that extend laterally to the accessory lobes and spinal ligaments. These LSO neurons are reminiscent of glycinergic neurons in a recently-described lateral spinal proprioceptive organ in zebrafish that detects spinal bending. The avian LSO, however, is located inside a series of fused vertebrae called the synsacrum, which constrains spinal bending. We suggest the LSO may be a modification and elaboration of a pre-existing mechanosensory spinal network in vertebrates. A mechanistic understanding of its function may be an important clue to understanding the evolution and development of avian locomotion.

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bioRxiv2021

Structured random receptive fields enable informative sensory encodings

Brains must represent the outside world so that animals survive and thrive. In early sensory systems, neural populations have diverse receptive fields structured to detect important features in inputs, yet significant variability has been ignored in classical models of sensory neurons. We model neuronal receptive fields as random, variable samples from parameterized distributions and demonstrate this model in two sensory modalities using data from insect mechanosensors and mammalian primary visual cortex. Our approach leads to a significant theoretical connection between the foundational concepts of receptive fields and random features, a leading theory for understanding artificial neural networks. The modeled neurons perform a randomized wavelet transform on inputs, which removes high frequency noise and boosts the signal. Further, these random feature neurons enable learning from fewer training samples and with smaller networks in artificial tasks. This structured random model of receptive fields provides a unifying, mathematically tractable framework to understand sensory encodings across both spatial and temporal domains.

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bioRxiv2021

Learning neural decoders without labels using multiple data streams

Recent advances in neural decoding have accelerated the development of brain-computer interfaces aimed at assisting users with everyday tasks such as speaking, walking, and manipulating objects. However, current approaches for training neural decoders commonly require large quantities of labeled data, which can be laborious or infeasible to obtain in real-world settings. One intriguing alternative uses self-supervised models that share self-generated pseudo-labels between two data streams; such models have shown exceptional performance on unlabeled audio and video data, but it remains unclear how well they extend to neural decoding. Here, we learn neural decoders without labels by leveraging multiple simultaneously recorded data streams, including neural, kinematic, and physiological signals. Specifically, we apply cross-modal, self-supervised deep clustering to decode movements from brain recordings; these decoders are compared to supervised and unimodal, self-supervised models. We find that sharing pseudo-labels between two data streams during training substantially increases decoding performance compared to unimodal, self-supervised models, with accuracies approaching those of supervised decoders trained on labeled data. Next, we develop decoders trained on three modalities that match or slightly exceed the performance of supervised models, achieving state-of-the-art neural decoding accuracy. Cross-modal decoding is a flexible, promising approach for robust, adaptive neural decoding in real-world applications without any labels.

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bioRxiv2021

AJILE12: Long-term naturalistic human intracranial neural recordings and pose

Understanding the neural basis of human movement in naturalistic scenarios is critical for expanding neuroscience research beyond constrained laboratory paradigms. Here, we describe our Annotated Joints in Long-term Electrocorticography for 12 human participants (AJILE12) dataset, the largest human neurobehavioral dataset that is publicly available; the dataset was recorded opportunistically during passive clinical epilepsy monitoring. AJILE12 includes synchronized intracranial neural recordings and upper body pose trajectories across 55 semi-continuous days of naturalistic movements, along with relevant metadata, including thousands of wrist movement events and annotated behavioral states. Neural recordings are available at 500 Hz from at least 64 electrodes per participant, for a total of 1280 hours. Pose trajectories at 9 upper-body keypoints were estimated from 118 million video frames. To facilitate data exploration and reuse, we have shared AJILE12 on The DANDI Archive in the Neurodata Without Borders (NWB) data standard and developed a browser-based dashboard.

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