@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 {68566, title = {Synaptic gradients transform object location to action.}, journal = {Nature}, volume = {613}, year = {2023}, month = {2023 Jan 04}, pages = {534-42}, abstract = {

To survive, animals must convert sensory information into appropriate behaviours. Vision is a common sense for locating ethologically relevant stimuli and guiding motor responses. How circuitry converts object location in retinal coordinates to movement direction in body coordinates remains largely unknown. Here we show through behaviour, physiology, anatomy and connectomics in Drosophila that visuomotor transformation occurs by conversion of topographic maps formed by the dendrites of feature-detecting visual projection neurons (VPNs) into synaptic weight gradients of VPN outputs onto central brain neurons. We demonstrate how this gradient motif transforms the anteroposterior location of a visual looming stimulus into the fly{\textquoteright}s directional escape. Specifically, we discover that two neurons postsynaptic to a looming-responsive VPN type promote opposite takeoff directions. Opposite synaptic weight gradients onto these neurons from looming VPNs in different visual field regions convert localized looming threats into correctly oriented escapes. For a second looming-responsive VPN type, we demonstrate graded responses along the dorsoventral axis. We show that this synaptic gradient motif generalizes across all 20 primary VPN cell types and most often arises without VPN axon topography. Synaptic gradients may thus be a general mechanism for conveying spatial features of sensory information into directed motor outputs.

}, issn = {1476-4687}, doi = {10.1038/s41586-022-05562-8}, author = {Dombrovski, Mark and Peek, Martin Y and Park, Jin-Yong and Vaccari, Andrea and Sumathipala, Marissa and Morrow, Carmen and Breads, Patrick and Zhao, Arthur and Kurmangaliyev, Yerbol Z and Sanfilippo, Piero and Rehan, Aadil and Polsky, Jason and Alghailani, Shada and Tenshaw, Emily and Namiki, Shigehiro and Zipursky, S Lawrence and Card, Gwyneth M} } @article {67892, title = {A population of descending neurons that regulates the flight motor of Drosophila.}, journal = {Current Biology}, volume = {32}, year = {2022}, month = {2022 Mar 14}, pages = {1189-1196}, abstract = {

Similar to many insect species, Drosophila melanogaster is capable of maintaining a stable flight trajectory for periods lasting up to several hours. Because aerodynamic torque is roughly proportional to the fifth power of wing length, even small asymmetries in wing size require the maintenance of subtle bilateral differences in flapping motion to maintain a stable path. Flies can even fly straight after losing half of a wing, a feat they accomplish via very large, sustained kinematic changes to both the damaged and intact wings. Thus, the neural network responsible for stable flight must be capable of sustaining fine-scaled control over wing motion across a large dynamic range. In this study, we describe an unusual type of descending neuron (DNg02) that projects directly from visual output regions of the brain to the dorsal flight neuropil of the ventral nerve cord. Unlike many descending neurons, which exist as single bilateral pairs with unique morphology, there is a population of at least 15 DNg02 cell pairs with nearly identical shape. By optogenetically activating different numbers of DNg02 cells, we demonstrate that these neurons regulate wingbeat amplitude over a wide dynamic range via a population code. Using two-photon functional imaging, we show that DNg02 cells are responsive to visual motion during flight in a manner that would make them well suited to continuously regulate bilateral changes in wing kinematics. Collectively, we have identified a critical set of descending neurons that provides the sensitivity and dynamic range required for flight control.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2022.01.008}, author = {Namiki, Shigehiro and Ros, Ivo G and Morrow, Carmen and Rowell, William J and Card, Gwyneth M and Korff, Wyatt and Dickinson, Michael H} }