@article {69679, title = {Whole-body simulation of realistic fruit fly locomotion with deep reinforcement learning}, journal = {bioRxiv}, year = {2024}, month = {2024 Nov 22}, abstract = {

The body of an animal influences how the nervous system produces behavior. Therefore, detailed modeling of the neural control of sensorimotor behavior requires a detailed model of the body. Here we contribute an anatomically-detailed biomechanical whole-body model of the fruit fly Drosophila melanogaster in the MuJoCo physics engine. Our model is general-purpose, enabling the simulation of diverse fly behaviors, both on land and in the air. We demonstrate the generality of our model by simulating realistic locomotion, both flight and walking. To support these behaviors, we have extended MuJoCo with phenomenological models of fluid forces and adhesion forces. Through data-driven end-to-end reinforcement learning, we demonstrate that these advances enable the training of neural network controllers capable of realistic locomotion along complex trajectories based on high-level steering control signals. We demonstrate the use of visual sensors and the re-use of a pre-trained general-purpose flight controller by training the model to perform visually guided flight tasks. Our project is an open-source platform for modeling neural control of sensorimotor behavior in an embodied context.Competing Interest StatementThe authors have declared no competing interest.

}, doi = {10.1101/2024.03.11.584515}, url = {https://www.biorxiv.org/content/early/2024/11/22/2024.03.11.584515}, author = {Vaxenburg, Roman and Siwanowicz, Igor and Merel, Josh and Robie, Alice A. and Morrow, Carmen and Novati, Guido and Stefanidi, Zinovia and Both, Gert-Jan and Card, Gwyneth M. and Reiser, Michael B. and Botvinick, Matthew M. and Branson, Kristin M. and Tassa, Yuval and Turaga, Srinivas C.} } @article {46557, title = {A multilevel multimodal circuit enhances action selection in Drosophila.}, journal = {Nature}, volume = {520}, year = {2015}, month = {2015 Apr 20}, pages = {633-9}, abstract = {

Natural events present multiple types of sensory cues, each detected by a specialized sensory modality. Combining information from several modalities is essential for the selection of appropriate actions. Key to understanding multimodal computations is determining the structural patterns of multimodal convergence and how these patterns contribute to behaviour. Modalities could converge early, late or at multiple levels in the sensory processing hierarchy. Here we show that combining mechanosensory and nociceptive cues synergistically enhances the selection of the fastest mode of escape locomotion in Drosophila larvae. In an electron microscopy volume that spans the entire insect nervous system, we reconstructed the multisensory circuit supporting the synergy, spanning multiple levels of the sensory processing hierarchy. The wiring diagram revealed a complex multilevel multimodal convergence architecture. Using behavioural and physiological studies, we identified functionally connected circuit nodes that trigger the fastest locomotor mode, and others that facilitate it, and we provide evidence that multiple levels of multimodal integration contribute to escape mode selection. We propose that the multilevel multimodal convergence architecture may be a general feature of multisensory circuits enabling complex input{\textendash}output functions and selective tuning to ecologically relevant combinations of cues.

}, doi = {10.1038/nature14297}, author = {Ohyama, Tomoko and Schneider-Mizell, Casey M. and Fetter, Richard D. and Aleman, Javier Valdes and Franconville, Romain and Rivera-Alba, Marta and Mensh, Brett D. and Branson, Kristin M. and Simpson, Julie H. and Truman, James W. and Cardona, Albert and Zlatic, Marta} }