@article {68799, title = {Single-cell type analysis of wing premotor circuits in the ventral nerve cord of Drosophila melanogaster}, journal = {eLife}, year = {2025}, month = {2025 May 06}, abstract = {

To perform most behaviors, animals must send commands from higher-order processing centers in the brain to premotor circuits that reside in ganglia distinct from the brain, such as the mammalian spinal cord or insect ventral nerve cord. How these circuits are functionally organized to generate the great diversity of animal behavior remains unclear. An important first step in unraveling the organization of premotor circuits is to identify their constituent cell types and create tools to monitor and manipulate these with high specificity to assess their functions. This is possible in the tractable ventral nerve cord of the fly. To generate such a toolkit, we used a combinatorial genetic technique (split-GAL4) to create 195 sparse transgenic driver lines targeting 196 individual cell types in the ventral nerve cord. These included wing and haltere motoneurons, modulatory neurons, and interneurons. Using a combination of behavioral, developmental, and anatomical analyses, we systematically characterized the cell types targeted in our collection. In addition, we identified correspondences between the cells in this collection and a recent connectomic data set of the ventral nerve cord. Taken together, the resources and results presented here form a powerful toolkit for future investigations of neuronal circuits and connectivity of premotor circuits while linking them to behavioral outputs.

}, doi = {10.7554/eLife.106548.1}, url = {https://doi.org/10.7554/eLife.106548.1}, author = {Erica Ehrhardt and Samuel C Whitehead and Shigehiro Namiki and Ryo Minegishi and Igor Siwanowicz and Kai Feng and Hideo Otsuna and FlyLight Project Team and Geoffrey W Meissner and David Stern and Jim Truman and David Shepherd and Michael H. Dickinson and Kei Ito and Barry J Dickson and Itai Cohen and Gwyneth M Card and Wyatt Korff} } @article {69290, title = {Whole-body simulation of realistic fruit fly locomotion with deep reinforcement learning}, journal = {Nature}, volume = {643}, year = {2025}, month = {2025 Jul}, 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} } @article {69007, title = {Driver lines for studying associative learning in Drosophila}, journal = {bioRxiv}, year = {2023}, month = {2023 Sep 16}, abstract = {

The mushroom body (MB) is the center for associative learning in insects. In Drosophila, intersectional split-GAL4 drivers and electron microscopy (EM) connectomes have laid the foundation for precise interrogation of the MB neural circuits. However, many cell types upstream and downstream of the MB remained to be investigated due to lack of driver lines. Here we describe a new collection of over 800 split-GAL4 and split-LexA drivers that cover approximately 300 cell types, including sugar sensory neurons, putative nociceptive ascending neurons, olfactory and thermo-/hygro-sensory projection neurons, interneurons connected with the MB-extrinsic neurons, and various other cell types. We characterized activation phenotypes for a subset of these lines and identified the sugar sensory neuron line most suitable for reward substitution. Leveraging the thousands of confocal microscopy images associated with the collection, we analyzed neuronal morphological stereotypy and discovered that one set of mushroom body output neurons, MBON08/MBON09, exhibits striking individuality and asymmetry across animals. In conjunction with the EM connectome maps, the driver lines reported here offer a powerful resource for functional dissection of neural circuits for associative learning in adult Drosophila.

}, doi = {https://doi.org/10.7554/elife.94168.4}, url = {https://doi.org/10.7554/elife.94168.4 }, author = {Yichun Shuai and Megan Sammons and Gabriella Sterne and Karen Hibbard and He Yang and Ching-Po Yang and Claire Managan and Igor Siwanowicz and Tzumin Lee and Gerald M. Rubin and Glenn Turner and Yoshinori Aso} } @article {68274, title = {Biomechanical origins of proprioceptive maps in the Drosophila leg}, journal = {bioRxiv}, year = {2022}, month = {2022 Aug 09}, abstract = {

Proprioception, the sense of body position and movement, is essential for effective motor control. Because proprioceptive sensory neurons are embedded in complex and dynamic tissues, it has been challenging to understand how they sense and encode mechanical stimuli. Here, we find that proprioceptor neurons in the\ Drosophila\ femur are organized into functional groups that are biomechanically specialized to detect features of tibia joint kinematics. The dendrites of position and vibration-tuned proprioceptors receive distinct mechanical signals via the\ arculum, an elegant mechanical structure that decomposes movement of the tibia joint into orthogonal components. The cell bodies of position-tuned proprioceptors form a\ goniotopic\ map of joint angle, whereas the dendrites of vibration-tuned proprioceptors form a\ tonotopic\ map of tibia vibration frequency. Our findings reveal biomechanical mechanisms that underlie proprioceptor feature selectivity and identify common organizational principles between proprioception and other topographically organized sensory systems.

}, doi = {10.1101/2022.08.08.503192}, url = {https://doi.org/10.1101/2022.08.08.503192}, author = {Akira Mamiya and Pralaksha Gurung and Igor Siwanowicz and Anne Sustar and Chenghao Chen and Jasper S. Phelps and Aaron T. Kuan and Alexandra Pacureanu and Wei-Chung Allen Lee and Natasha Mhatre and John C. Tuthill} } @article {68570, title = {Eye structure shapes neuron function in Drosophila motion vision}, journal = {bioRxiv}, year = {2022}, month = {2022 Dec 15}, abstract = {

Many animals rely on vision to navigate through their environment. The pattern of changes in the visual scene induced by self-motion is the\ optic flow1, which is first estimated in local patches by directionally selective (DS) neurons2{\textendash}4. But how should the arrays of DS neurons, each responsive to motion in a preferred direction at a specific retinal position, be organized to support robust decoding of optic flow by downstream circuits? Understanding this global organization is challenging because it requires mapping fine, local features of neurons across the animal{\textquoteright}s field of view3. In\ Drosophila, the asymmetric dendrites of the T4 and T5 DS neurons establish their preferred direction, making it possible to predict DS responses from anatomy4,5. Here we report that the preferred directions of fly DS neurons vary at different retinal positions and show that this spatial variation is established by the anatomy of the compound eye. To estimate the preferred directions across the visual field, we reconstructed hundreds of T4 neurons in a full brain EM volume6\ and discovered unexpectedly stereotypical dendritic arborizations that are independent of location. We then used whole-head μCT scans to map the viewing directions of all compound eye facets and found a non-uniform sampling of visual space that explains the spatial variation in preferred directions. Our findings show that the organization of preferred directions in the fly is largely determined by the compound eye, exposing an intimate and unexpected connection between the peripheral structure of the eye, functional properties of neurons deep in the brain, and the control of body movements.

}, doi = {10.1101/2022.12.14.520178}, url = {https://doi.org/10.1101/2022.12.14.520178}, author = {Arthur Zhao and Eyal Gruntman and Aljoscha Nern and Nirmala A. Iyer and Edward M. Rogers and Sanna Koskela and Igor Siwanowicz and Marisa Dreher and Miriam A. Flynn and Connor W. Laughland and Henrique D.F. Ludwig and Alex G. Thomson and Cullen P. Moran and Bruck Gezahegn and Davi D. Bock and Michael B. Reiser} }