Biology subjects

Abe, E. T. T.

Publications and source records attributed to Abe, E. T. T..

10 recordsLinked to original sources

bioRxiv2026

Temporally organized activity in mouse V1 encodes newly sampled visual content during free movement

Natural vision is continuously shaped by an animal's own movements, which determine what enters the visual system and when visual input changes. Gaze shifts are known to initiate a temporally structured sequence of activity in primary visual cortex (V1), but how the visual content sampled by each movement contributes to this sequence has remained unclear. We recorded visual input, eye and head movements, and V1 activity in freely moving mice, and asked how the visual content sampled on each gaze shift shapes the response. The magnitude of visual change induced by each gaze shift scaled the amplitude of responses according to each neuron's characteristic response profile, while movement amplitude alone did not reproduce this modulation in darkness, supporting a role of visual input in driving the sequence. Activity following gaze shifts reflected each neuron's spatial receptive field structure, and visual filters estimated under head-fixed conditions predicted the relative timing of spike responses during gaze shifts of freely moving animals, demonstrating that gaze shift responses encode visual information. At the population level, decoded V1 activity shifted toward the scene sampled after each gaze shift. Thus, across single-neuron, spatial, temporal, and population measures, V1 activity tracked the content of each sample beyond movement timing alone, indicating that gaze shifts act as sampling events that result in V1 encoding visual information in temporally ordered responses.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

bioRxiv2026

Visualizing and sonifying neurodata (ViSoND) for enhanced observation

Neuroscience needs observation. Observation lets us evaluate data quality, judge whether models are biologically realistic, and generate new hypotheses. However, high-dimensional behavioral and neural data are too complex to be easily displayed and eye-tested. Computational methods can reduce the dimensionality of data and reveal statistically robust dynamical structure but often yield results that are difficult to relate back to the underlying biology. In addition, the choice of what parameters to quantify may not capture unexpectedly relevant aspects of the data. To supplement quantification with enhanced qualitative observation, we developed Visualization and Sonification of NeuroData (ViSoND), an open-source approach for displaying multiple data streams using video and sonification. Sonification is nothing new to neuroscience. Scientists have sonified their physiological preparations since Lord Adrians earliest recordings. We extend this tradition by mapping multiple physiological datastreams to musical notes using MIDI. Synchronizing MIDI to video provides an opportunity to watch an animals movement while listening to physiological signals such as action potentials. Here we provide two demonstrations of this approach. First, we used ViSoND to interpret behavioral structure revealed by a computational model trained on the breathing rhythms of freely behaving mice. Second, ViSoND revealed patterns of neural activity in mouse visual cortex corresponding to eye blinks, events that were previously filtered out of analysis. These use cases show that ViSoND can supplement quantitative rigor with observational interpretability. Additionally, ViSoND provides an accessible way to display data which may broaden the audience for communication of neuroscientific findings.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

bioRxiv2022

A dynamic sequence of visual processing initiated by gaze shifts

Animals move their head and eyes as they explore and sample the visual scene. Previous studies have demonstrated neural correlates of head and eye movements in rodent primary visual cortex (V1), but the sources and computational roles of these signals are unclear. We addressed this by combining measurement of head and eye movements with high density neural recordings in freely moving mice. V1 neurons responded primarily to gaze shifts, where head movements are accompanied by saccadic eye movements, but not to head movements where compensatory eye movements stabilize gaze. A variety of activity patterns immediately followed gaze shifts, including units with positive, biphasic, or negative responses, and together these responses formed a temporal sequence following the gaze shift. These responses were greatly diminished in the dark for the vast majority of units, replaced by a uniform suppression of activity, and were similar to those evoked by sequentially flashed stimuli in head-fixed conditions, suggesting that gaze shift transients represent the temporal response to the rapid onset of new visual input. Notably, neurons responded in a sequence that matches their spatial frequency preference, from low to high spatial frequency tuning, consistent with coarse-to-fine processing of the visual scene following each gaze shift. Recordings in foveal V1 of freely gazing head-fixed marmosets revealed a similar sequence of temporal response following a saccade, as well as the progression of spatial frequency tuning. Together, our results demonstrate that active vision in both mice and marmosets consists of a dynamic temporal sequence of neural activity associated with visual sampling. HighlightsO_LIDuring free movement, neurons in mouse V1 respond to head movements that are accompanied by a gaze-shifting saccadic eye movement, but not a compensatory eye movement. C_LIO_LINeurons respond to gaze shifts with diverse temporal dynamics that form a sequence across the population, from early positive responses to biphasic and negative responses. C_LIO_LIIn darkness, most neurons show a uniform suppression following a gaze shift. C_LIO_LITemporal dynamics of responses correspond to a neurons temporal and spatial frequency preferences, consistent with a coarse-to-fine processing sequence. C_LIO_LIA similar temporal sequence following saccades is observed in foveal V1 of freely gazing head-fixed marmosets, demonstrating shared aspects of active visual processing across species. C_LI

neuroscience↗

bioRxiv2022

Joint coding of visual input and eye/head position in V1 of freely moving mice

SUMMARYVisual input to the brain during natural behavior is highly dependent on movements of the eyes, head, and body. Neurons in mouse primary visual cortex (V1) respond to eye and head movements, but how information about eye and head position is integrated with visual processing during free movement is unknown, since visual physiology is generally performed under head-fixation. To address this, we performed single-unit electrophysiology in V1 of freely moving mice while simultaneously measuring the mouses eye position, head orientation, and the visual scene from the mouses perspective. Based on these measures we were able to map spatiotemporal receptive fields during free movement, using a generalized linear model (GLM) that predicted the activity of V1 neurons based on gaze-corrected visual input. Furthermore, we found that a significant fraction of visually-responsive neurons showed tuning for eye position and head orientation. Incorporating these variables into the GLM revealed that visual and positional signals are integrated through a multiplicative mechanism in the majority of modulated neurons, consistent with computation via gain fields and nonlinear mixed selectivity. These results provide new insight into coding in mouse V1, and more generally provide a paradigm for performing visual physiology under natural conditions, including active sensing and ethological behavior. HIGHLIGHTSO_LINeurons in mouse V1 respond to both vision and self-motion, but it is unclear how these are combined. C_LIO_LIWe record neural activity in V1 concurrent with measurement of the visual input from the mouses perspective during free movement. C_LIO_LIThese data provide the first measurement of visual receptive fields in freely moving animals. C_LIO_LIWe show that many V1 neurons are tuned to eye position and head orientation, and these contribute a multiplicative gain on visual responses in the majority of modulated neurons. C_LI

neuroscience↗

bioRxiv2021

Distance estimation from monocular cues in an ethological visuomotor task

In natural contexts, sensory processing and motor output are closely coupled, which is reflected in the fact that many brain areas contain both sensory and movement signals. However, standard reductionist paradigms decouple sensory decisions from their natural motor consequences, and head-fixation prevents the natural sensory consequences of self-motion. In particular, movement through the environment provides a number of depth cues beyond stereo vision that are poorly understood. To study the integration of visual processing and motor output in a naturalistic task, we investigated distance estimation in freely moving mice. We found that mice use vision to accurately jump across a variable gap, thus directly coupling a visual computation to its corresponding ethological motor output. Monocular eyelid suture did not affect gap jumping success, thus mice can use cues that do not depend on binocular disparity and stereo vision. Under monocular conditions, mice altered their head positioning and performed more vertical head movements, consistent with a shift from using stereopsis to other monocular cues, such as motion or position parallax. Finally, optogenetic suppression of primary visual cortex impaired task performance under both binocular and monocular conditions when optical fiber placement was localized to binocular or monocular zone V1, respectively. Together, these results show that mice can use monocular cues, relying on visual cortex, to accurately judge distance. Furthermore, this behavioral paradigm provides a foundation for studying how neural circuits convert sensory information into ethological motor output.

neuroscience↗

bioRxiv2020

Dynamics of gaze control during prey capture in freely moving mice

Many studies of visual processing are conducted in unnatural conditions, such as head- and gaze-fixation. As this radically limits natural exploration of the visual environment, there is much less known about how animals actively use their sensory systems to acquire visual information in natural, goal-directed contexts. Recently, prey capture has emerged as an ethologically relevant behavior that mice perform without training, and that engages vision for accurate orienting and pursuit. However, it is unclear how mice target their gaze during such natural behaviors, particularly since, in contrast to many predatory species, mice have a narrow binocular field and lack foveate vision that would entail fixing their gaze on a specific point in the visual field. Here we measured head and bilateral eye movements in freely moving mice performing prey capture. We find that the majority of eye movements are compensatory for head movements, thereby acting to stabilize the visual scene. During head turns, however, these periods of stabilization are interspersed with non-compensatory saccades that abruptly shift gaze position. Analysis of eye movements relative to the cricket position shows that the saccades do not preferentially select a specific point in the visual scene. Rather, orienting movements are driven by the head, with the eyes following in coordination to sequentially stabilize and recenter the gaze. These findings help relate eye movements in the mouse to other species, and provide a foundation for studying active vision during ethological behaviors in the mouse.

neuroscience↗