@article {69398, title = {Shorter-duration escapes driven by Drosophila giant interneurons promote survival during predation}, journal = {Proc Biol Sci}, year = {2025}, month = {2025 May 28}, abstract = {

Large axon-diameter descending neurons are metabolically costly but transmit information rapidly from sensory neurons in the brain to motor neurons in the nerve cord. They have thus endured as a common feature of escape circuits in many animal species where speed is paramount. Though often considered isolated command neurons triggering fast-reaction-time, all-or-none escape responses, giant neurons are just one of multiple parallel pathways enabling selection between behavioral alternatives. Such degeneracy among escape circuits makes it unclear if and how giant neurons benefit prey fitness. Here we competed Drosophila melanogaster flies with genetically-silenced Giant Fibers (GFs) against flies with functional GFs in an arena with wild-caught damselfly predators and find that GF silencing decreases prey survival. Kinematic analysis of damselfly attack trajectories shows that decreased prey survival fitness results from GF-silenced flies failing to escape during predator attack speeds and approach distances that would normally elicit successful escapes. When challenged with a virtual looming predator, fly GFs promote survival by enforcing selection of a short-duration takeoff sequence as opposed to reducing reaction time. Our findings support a role for the GFs in promoting prey survival by influencing action selection as a means to enhance escape performance during realistically complex predation scenarios.

Preprint:\ https://www.biorxiv.org/content/early/2024/05/01/2024.04.30.591368

}, doi = {10.1098/rspb.2024.1724}, url = {https://doi.org/10.1098/rspb.2024.1724}, author = {Cynthia M. Chai and Carmen Morrow and Dhyey D. Parikh and Catherine R. von Reyn and Anthony Leonardo and Gwyneth M Card} } @article {69290, title = {Whole-body simulation of realistic fruit fly locomotion with deep reinforcement learning}, journal = {Nature}, year = {2025}, month = {2025 Apr 23}, abstract = {

The body of an animal influences how its nervous system generates behavior1. Accurately modeling the neural control of sensorimotor behavior requires an anatomically detailed biomechanical representation of the body. Here, we introduce a whole-body model of the fruit fly Drosophila melanogaster in a physics simulator. Designed as a general-purpose framework, our model enables the simulation of diverse fly behaviors, including both terrestrial and aerial locomotion. We validate its versatility by replicating realistic walking and flight behaviors. To support these behaviors, we develop new phenomenological models for fluid and adhesion forces. Using data-driven, end-to-end reinforcement learning\ we train neural network controllers capable of generating naturalistic locomotion along complex trajectories in response to high-level steering commands. Additionally, we show the use of visual sensors and hierarchical motor control, training a high-level controller to reuse a pre-trained low-level flight controller to perform visually guided flight tasks. Our model serves as an open-source platform for studying the neural control of sensorimotor behavior in an embodied context.

Preprint:\ www.biorxiv.org/content/early/2024/03/14/2024.03.11.584515

}, doi = {10.1038/s41586-025-09029-4}, url = {https://doi.org/10.1038/s41586-025-09029-4}, author = {Roman Vaxenburg and Igor Siwanowicz and Josh Merel and Alice A Robie and Carmen Morrow and Guido Novati and Zinovia Stefanidi and Gwyneth M Card and Michael B Reiser and Matthew M Botvinick and Kristin M Branson and Yuval Tassa and Srinivas C Turaga} }