Biology subjects

May, C. E.

Publications and source records attributed to May, C. E..

5 recordsLinked to original sources

bioRxiv2026

Predicting Drosophila Body Orientation from a Translational Trajectory using an Artificial Neural Network

Body orientation is a key variable in the analysis of insect flight behavior, yet it remains difficult to measure across the full extent of a trajectory in most experimental settings. Although modern tracking systems reliably capture the position and velocity of the center of mass, resolving body yaw orientation typically requires dedicated hardware confined to a small, purpose-built volume, and is impractical for large-scale or long-duration studies. Here, we develop a data-driven estimator that predicts body yaw orientation directly from translational flight trajectory data. We trained a fully connected feedforward artificial neural network (ANN) on a dataset in which both flight trajectory and body orientation were recorded simultaneously in freely flying Drosophila, using a time-delay embedding of ground velocity, air velocity, and inferred thrust vectors as input features. Trained on 2,340 trajectories, the ANN predictor achieved a median absolute angular error of approximately 5{degrees}, with accurate heading recovery across the full [-{pi}, {pi}) range. The estimator provides a practical tool for recovering body orientation information from existing trajectory datasets in which only center-of-mass motion was recorded, extending the behavioral and computational analysis of insect navigation to previously inaccessible data.

animal behavior and cognition↗

bioRxiv2025

A compact multisensory representation of self-motion is sufficient for computing an external world variable

External forces such as gravity and wind fundamentally shape how an animal navigates through the world, but force magnitude and direction cannot be directly measured by an animal in motion. How the brain integrates multiple sensory cues to determine the direction of such external forces remains unclear. In the fruit fly (Drosophila melanogaster) columnar inputs to the navigation center, known as PFNs, have been shown to represent both visual and mechano-sensory cues about self-motion in a similar vector format, but the dynamics and integration of these cues, particularly during complex natural movements, are not known. Here we used 2-photon imaging to investigate the dynamic representation and integration of multi-sensory self-motion cues - optic flow and airflow - across PFNs. We find that one type, called PFNd, linearly integrates optic flow and airflow direction signals with distinct dynamics. We next reveal airflow speed as well as direction encoding by PFNp_c neurons. Imaging from available genetic lines suggests that many PFN types likely encode functions of airflow speed and direction. Based on these data, we construct and validate computational models of how visual and mechano-sensory dynamics are integrated and represented across PFN populations. We then use these models to estimate neural responses during real and simulated flight maneuvers. Applying a nonlinear empirical observability analysis to these responses, we show that active turning and deceleration maneuvers combined with PFN representations can decode the direction of an ambient external cue (wind) during free flight, and identify the theoretical contribution of different PFNs to this ability. Finally, we show that an anatomically-inspired artificial neural network can successfully decode wind direction from PFN representations and identify components of the algorithm used by both the network and real flies to orient upwind during olfactory navigation in flight. Our work provides evidence that active sensation, combined with multi-sensory encoding, can allow a compact system like the fly navigation center to infer a property of the external world that cannot be directly measured by a single sensory system.

neuroscience↗

bioRxiv2023

Scalable Apparatus to Measure Posture and Locomotion (SAMPL): a high-throughput solution to study unconstrained vertical behavior in small animals

Balance and movement are impaired in a wide variety of neurological disorders. Recent advances in behavioral monitoring provide unprecedented access to posture and loco-motor kinematics, but without the throughput and scalability necessary to screen candidate genes / potential therapeutics. We present a powerful solution: a Scalable Apparatus to Measure Posture and Locomotion (SAMPL). SAMPL includes extensible imaging hardware and low-cost open-source acquisition software with real-time processing. We first demonstrate that SAMPLs hardware and acquisition software can acquire data from D. melanogaster, C.elegans, and D. rerio as they move vertically. Next, we leverage SAMPLs throughput to rapidly (two weeks) gather a new zebrafish dataset. We use SAMPLs analysis and visualization tools to replicate and extend our current understanding of how zebrafish balance as they navigate through a vertical environment. Next, we discover (1) that key kinematic parameters vary systematically with genetic background, and (2) that such background variation is small relative to the changes that accompany early development. Finally, we simulate SAMPLs ability to resolve differences in posture or vertical navigation as a function of effect size and data gathered - key data for screens. Taken together, our apparatus, data, and analysis provide a powerful solution for laboratories using small animals to investigate balance and locomotor disorders at scale. More broadly, SAMPL is both an adaptable resource for laboratories looking process video-graphic measures of behavior in real-time, and an exemplar of how to scale hardware to enable the throughput necessary for screening.

neuroscience↗

bioRxiv2020

The Polycomb Repressive Complex 2.1 Links the Food Environment to a Persistent Neural State

Diets rich in sugar, salt, and fat alter taste perception and food intake, leading to obesity and metabolic disorders, but the molecular mechanisms through which this occurs are unknown. Here we show that in response to a high sugar diet, the epigenetic regulator Polycomb Repressive Complex 2.1 (PRC2.1) persistently reprograms the sensory neurons of D. melanogaster flies to reduce sweet sensation and promote obesity. In animals fed high sugar, the binding of PRC2.1 to the chromatin of the sweet gustatory neurons is redistributed to repress a developmental transcriptional network that modulates the responsiveness of these cells to sweet stimuli, reducing sweet sensation. Importantly, half of these transcriptional changes persist despite returning the animals to a control diet, causing a permanent decrease in sweet taste. Our results uncover a new epigenetic mechanism that, in response to the dietary environment, regulates neural plasticity and feeding behavior to promote obesity.

neuroscience↗

bioRxiv2019

Dietary sugar inhibits satiation by decreasing the central processing of sweet taste

From humans to flies, exposure to diets rich in sugar and fat lowers taste sensation, changes food choices, and promotes feeding. However, how these peripheral alterations influence eating is unknown. Here we used the genetically tractable organism D. melanogaster to define the neural mechanisms through which this occurs. We characterized a population of protocerebral anterior medial dopaminergic neurons (PAM DANs) that innervates the {beta}2 compartment of the mushroom body and responds to sweet taste. In animals fed a high sugar diet, the response of PAM-{beta}2 to sweet stimuli was reduced and delayed, and sensitive to the strength of the signal transmission out of the sensory neurons. We found that PAM-{beta}2 DANs activity controls feeding rate and satiation: closed-loop optogenetic activation of {beta}2 DANs restored normal eating in animals fed high sucrose. These data argue that diet-dependent alterations in taste weaken satiation by impairing the central processing of sensory signals.

neuroscience↗