@article {69401, title = {Machine learning reveals the control mechanics of an insect wing hinge}, journal = {Nature}, volume = {628}, year = {2024}, month = {2024 Apr 17}, pages = {795-803}, abstract = {

Insects constitute the most species-rich radiation of metazoa, a success that is due to the evolution of active flight. Unlike pterosaurs, birds and bats, the wings of insects did not evolve from legs, but are novel structures that are attached to the body via a biomechanically complex hinge that transforms tiny, high-frequency oscillations of specialized power muscles into the sweeping back-and-forth motion of the wings. The hinge consists of a system of tiny, hardened structures called sclerites that are interconnected to one another via flexible joints and regulated by the activity of specialized control muscles. Here we imaged the activity of these muscles in a fly using a genetically encoded calcium indicator, while simultaneously tracking the three-dimensional motion of the wings with high-speed cameras. Using machine learning, we created a convolutional neural network that accurately predicts wing motion from the activity of the steering muscles, and an encoder-decoder that predicts the role of the individual sclerites on wing motion. By replaying patterns of wing motion on a dynamically scaled robotic fly, we quantified the effects of steering muscle activity on aerodynamic forces. A physics-based simulation incorporating our hinge model generates flight manoeuvres that are remarkably similar to those of free-flying flies. This integrative, multi-disciplinary approach reveals the mechanical control logic of the insect wing hinge, arguably among the most sophisticated and evolutionarily important skeletal structures in the natural world.

}, keywords = {Animals, Biomechanical Phenomena, Calcium, Drosophila melanogaster, Female, Flight, Animal, Machine Learning, Movement, Muscles, Neural Networks, Computer, Robotics, Wings, Animal}, issn = {1476-4687}, doi = {10.1038/s41586-024-07293-4}, author = {Melis, Johan M and Siwanowicz, Igor and Dickinson, Michael H} } @article {69484, title = {Mechanosensory representation of wing deformations}, journal = {bioRxiv}, year = {2024}, month = {2024 Jun 02}, abstract = {

Efficient representation of structural deformations is crucial for monitoring the instantaneous state of biological structures. Insects{\textquoteright} ability to encode wing deformations during flight demonstrates a general morphological computing principle applicable across sensory systems in nature as well as engineered systems. To characterize how relevant features are encoded, we measured and modelled displacement and strain across dragonfly wing surfaces in tethered and free flight. Functional interpretations were supported by neuroanatomical maps, and ablation and perturbation experiments. We find that signal redundancy is reduced by non-random sensor distributions and that morphology limits the stimulus space such that sensory systems can monitor natural states with few sensors. Deviations from the natural states are detected by a flexible population of additional sensors with many distinguishable activation patterns.

}, doi = {10.1101/2024.06.02.596338}, url = {https://www.biorxiv.org/content/early/2024/06/02/2024.06.02.596338}, author = {Yarger, Alexandra M and Maeda, Masateru and Siwanowicz, Igor and Kajiyama, Haruhiro and Walker, Simon M and Bomphrey, Richard J and Lin, Huai-Ti} } @article {69301, title = {Motor neurons generate pose-targeted movements via proprioceptive sculpting.}, journal = {Nature}, year = {2024}, month = {2024 Mar 20}, abstract = {

Motor neurons are the final common pathway through which the brain controls movement of the body, forming the basic elements from which all movement is composed. Yet how a single motor neuron contributes to control during natural movement remains unclear. Here we anatomically and functionally characterize the individual roles of the motor neurons that control head movement in the fly, Drosophila melanogaster. Counterintuitively, we find that activity in a single motor neuron rotates the head in different directions, depending on the starting posture of the head, such that the head converges towards a pose determined by the identity of the stimulated motor neuron. A feedback model predicts that this convergent behaviour results from motor neuron drive interacting with proprioceptive feedback. We identify and genetically suppress a single class of proprioceptive neuron that changes the motor neuron-induced convergence as predicted by the feedback model. These data suggest a framework for how the brain controls movements: instead of directly generating movement in a given direction by activating a fixed set of motor neurons, the brain controls movements by adding bias to a continuing proprioceptive-motor loop.

}, issn = {1476-4687}, doi = {10.1038/s41586-024-07222-5}, author = {Gorko, Benjamin and Siwanowicz, Igor and Close, Kari and Christoforou, Christina and Hibbard, Karen L and Kabra, Mayank and Lee, Allen and Park, Jin-Yong and Li, Si Ying and Chen, Alex B and Namiki, Shigehiro and Chen, Chenghao and Tuthill, John C and Bock, Davi D and Rouault, Herv{\'e} and Branson, Kristin and Ihrke, Gudrun and Huston, Stephen J} } @article {68413, title = {Muscles that move the retina augment compound eye vision in Drosophila.}, journal = {Nature}, year = {2022}, month = {2022 Oct 26}, abstract = {

Most animals have compound eyes, with tens to thousands of lenses attached rigidly to the exoskeleton. A natural assumption is that all of these species must resort to moving either their head or\ their body to actively change their visual input. However, classic anatomy has revealed that flies have muscles poised to move their retinas under the stable lenses of each compound eye. Here we show that Drosophila use their retinal muscles to smoothly track visual motion, which helps to stabilize the retinal image, and also to perform small saccades when viewing a stationary scene. We show that when the retina moves, visual receptive fields shift accordingly, and that even the smallest retinal saccades activate visual neurons. Using a head-fixed behavioural paradigm, we find that Drosophila perform binocular, vergence movements of their retinas-which could enhance depth perception-when crossing gaps, and impairing the physiology of retinal motor neurons alters gap-crossing trajectories during free behaviour. That flies evolved an ability to actuate their retinas suggests that moving the eye independently of the head is broadly paramount for animals. The similarities of smooth and saccadic movements of the Drosophila retina and the vertebrate eye highlight a notable example of convergent evolution.

}, issn = {1476-4687}, doi = {10.1038/s41586-022-05317-5}, author = {Fenk, Lisa M and Avritzer, Sofia C and Weisman, Jazz L and Nair, Aditya and Randt, Lucas D and Mohren, Thomas L and Siwanowicz, Igor and Maimon, Gaby} } @article {65852, title = {Multi-regional circuits underlying visually guided decision-making in Drosophila.}, journal = {Current Opinion in Neurobiology}, volume = {65}, year = {2020}, month = {2020 Nov 17}, pages = {77-87}, abstract = {

Visually guided decision-making requires integration of information from distributed brain areas, necessitating a brain-wide approach to examine its neural mechanisms. New tools in Drosophila melanogaster enable circuits spanning the brain to be charted with single cell-type resolution. Here, we highlight recent advances uncovering the computations and circuits that transform and integrate visual information across the brain to make behavioral choices. Visual information flows from the optic lobes to three primary central brain regions: a sensorimotor mapping area and two {\textquoteright}higher{\textquoteright} centers for memory or spatial orientation. Rapid decision-making during predator evasion emerges from the spike timing dynamics in parallel sensorimotor cascades. Goal-directed decisions may occur through memory, navigation and valence processing in the central complex and mushroom bodies.

}, issn = {1873-6882}, doi = {10.1016/j.conb.2020.10.010}, author = {Cheong, Han and Siwanowicz, Igor and Card, Gwyneth M} } @article {65907, title = {Multi-regional circuits underlying visually guided decision-making in Drosophila.}, journal = {Current Opinion in Neurobiology}, volume = {65}, year = {2020}, month = {2020 Dec 01}, pages = {77-87}, abstract = {

Visually guided decision-making requires integration of information from distributed brain areas, necessitating a brain-wide approach to examine its neural mechanisms. New tools in Drosophila melanogaster enable circuits spanning the brain to be charted with single cell-type resolution. Here, we highlight recent advances uncovering the computations and circuits that transform and integrate visual information across the brain to make behavioral choices. Visual information flows from the optic lobes to three primary central brain regions: a sensorimotor mapping area and two {\textquoteright}higher{\textquoteright} centers for memory or spatial orientation. Rapid decision-making during predator evasion emerges from the spike timing dynamics in parallel sensorimotor cascades. Goal-directed decisions may occur through memory, navigation and valence processing in the central complex and mushroom bodies.

}, issn = {1873-6882}, doi = {10.1016/j.conb.2020.10.010}, author = {Cheong, Han Sj and Siwanowicz, Igor and Card, Gwyneth M} } @article {5291, title = {Mushroom body efferent neurons responsible for aversive olfactory memory retrieval in Drosophila.}, journal = {Nature Neuroscience}, volume = {14}, year = {2011}, month = {2011 Jun 19}, pages = {903-10}, abstract = {

Aversive olfactory memory is formed in the mushroom bodies in Drosophila melanogaster. Memory retrieval requires mushroom body output, but the manner in which a memory trace in the mushroom body drives conditioned avoidance of a learned odor remains unknown. To identify neurons that are involved in olfactory memory retrieval, we performed an anatomical and functional screen of defined sets of mushroom body output neurons. We found that MB-V2 neurons were essential for retrieval of both short- and long-lasting memory, but not for memory formation or memory consolidation. MB-V2 neurons are cholinergic efferent neurons that project from the mushroom body vertical lobes to the middle superiormedial protocerebrum and the lateral horn. Notably, the odor response of MB-V2 neurons was modified after conditioning. As the lateral horn has been implicated in innate responses to repellent odorants, we propose that MB-V2 neurons recruit the olfactory pathway involved in innate odor avoidance during memory retrieval.

}, keywords = {Afferent Pathways, Analysis of Variance, Animal, Animals, Antigens, Avoidance Learning, Behavior, Biological, CD8, Choline O-Acetyltransferase, Conditioning (Psychology), Drosophila melanogaster, Drosophila Proteins, Gene Expression Regulation, Genetically Modified, Glutamate Decarboxylase, Green Fluorescent Proteins, Memory, Mental Recall, Models, Motor Neurons, Mushroom Bodies, Odors, Olfactory Pathways, Short-Term, Smell, Synapsins, Temperature, Time Factors, Transcription Factors, Tyrosine 3-Monooxygenase, Vesicular Glutamate Transport Proteins}, issn = {1546-1726}, doi = {10.1038/nn.2846}, author = {S{\'e}journ{\'e}, Julien and Placais, Pierre-Yves and Aso, Yoshinori and Siwanowicz, Igor and Trannoy, S{\'e}verine and Thoma, Vladimiros and Tedjakumala, Stevanus R and Rubin, Gerald M and Tch{\'e}nio, Paul and Ito, Kei and Isabel, Guillaume and Tanimoto, Hiromu and Preat, Thomas} }