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5 Janelia Publications

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    03/14/24 | Whole-body simulation of realistic fruit fly locomotion with deep reinforcement learning
    Roman Vaxenburg , Igor Siwanowicz , Josh Merel , Alice A Robie , Carmen Morrow , Guido Novati , Zinovia Stefanidi , Gwyneth M Card , Michael B Reiser , Matthew M Botvinick , Kristin M Branson , Yuval Tassa , Srinivas C Turaga
    bioRxiv. 2024-03-14:. doi: 10.1101/2024.03.11.584515

    The body of an animal determines 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. With a visually guided flight task, we demonstrate a neural controller that can use the vision sensors of the body model to control and steer flight. 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.

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    06/09/23 | Organization of an Ascending Circuit that Conveys Flight Motor State
    Han S. J. Cheong , Kaitlyn N. Boone , Marryn M. Bennett , Farzaan Salman , Jacob D. Ralston , Kaleb Hatch , Raven F. Allen , Alec M. Phelps , Andrew P. Cook , Jasper S. Phelps , Mert Erginkaya , Wei-Chung A. Lee , Gwyneth M. Card , Kevin C. Daly , Andrew M. Dacks
    bioRxiv. 2023 Jun 09:. doi: 10.1101/2023.06.07.544074

    Natural behaviors are a coordinated symphony of motor acts which drive self-induced or reafferent sensory activation. Single sensors only signal presence and magnitude of a sensory cue; they cannot disambiguate exafferent (externally-induced) from reafferent sources. Nevertheless, animals readily differentiate between these sources of sensory signals to make appropriate decisions and initiate adaptive behavioral outcomes. This is mediated by predictive motor signaling mechanisms, which emanate from motor control pathways to sensory processing pathways, but how predictive motor signaling circuits function at the cellular and synaptic level is poorly understood. We use a variety of techniques, including connectomics from both male and female electron microscopy volumes, transcriptomics, neuroanatomical, physiological and behavioral approaches to resolve the network architecture of two pairs of ascending histaminergic neurons (AHNs), which putatively provide predictive motor signals to several sensory and motor neuropil. Both AHN pairs receive input primarily from an overlapping population of descending neurons, many of which drive wing motor output. The two AHN pairs target almost exclusively non-overlapping downstream neural networks including those that process visual, auditory and mechanosensory information as well as networks coordinating wing, haltere, and leg motor output. These results support the conclusion that the AHN pairs multi-task, integrating a large amount of common input, then tile their output in the brain, providing predictive motor signals to non-overlapping sensory networks affecting motor control both directly and indirectly.

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    06/01/23 | Single-cell type analysis of wing premotor circuits in the ventral nerve cord of Drosophila melanogaster
    Erica Ehrhardt , Samuel C Whitehead , Shigehiro Namiki , Ryo Minegishi , Igor Siwanowicz , Kai Feng , Hideo Otsuna , FlyLight Project Team , Geoffrey W Meissner , David Stern , Jim Truman , David Shepherd , Michael H. Dickinson , Kei Ito , Barry J Dickson , Itai Cohen , Gwyneth M Card , Wyatt Korff
    bioRxiv. 2023 Jun 01:. doi: 10.1101/2023.05.31.542897

    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 function. 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 driver lines targeting 198 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. Taken together, the resources and results presented here form a powerful toolkit for future investigations of neural circuits and connectivity of premotor circuits while linking them to behavioral outputs.

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    03/14/22 | A population of descending neurons that regulates the flight motor of Drosophila.
    Namiki S, Ros IG, Morrow C, Rowell WJ, Card GM, Korff W, Dickinson MH
    Current Biology. 2022 Mar 14;32(5):1189-1196. doi: 10.1016/j.cub.2022.01.008

    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.

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    12/06/21 | Functional architecture of neural circuits for leg proprioception in Drosophila.
    Chen C, Agrawal S, Mark B, Mamiya A, Sustar A, Phelps JS, Lee WA, Dickson BJ, Card GM, Tuthill JC
    Current Biology. 2021 Dec 06;31(23):5163. doi: 10.1016/j.cub.2021.09.035

    To effectively control their bodies, animals rely on feedback from proprioceptive mechanosensory neurons. In the Drosophila leg, different proprioceptor subtypes monitor joint position, movement direction, and vibration. Here, we investigate how these diverse sensory signals are integrated by central proprioceptive circuits. We find that signals for leg joint position and directional movement converge in second-order neurons, revealing pathways for local feedback control of leg posture. Distinct populations of second-order neurons integrate tibia vibration signals across pairs of legs, suggesting a role in detecting external substrate vibration. In each pathway, the flow of sensory information is dynamically gated and sculpted by inhibition. Overall, our results reveal parallel pathways for processing of internal and external mechanosensory signals, which we propose mediate feedback control of leg movement and vibration sensing, respectively. The existence of a functional connectivity map also provides a resource for interpreting connectomic reconstruction of neural circuits for leg proprioception.

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