Predicting fruit fly’s sensing rate with insect flight simulations

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 111; no. 31; pp. 11246 - 11251
• Main Authors: Chang, Song, Wang, Z. Jane
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 05.08.2014; National Acad Sciences
• Subjects: Aerodynamic stability; Aircraft pitch; Algorithms; Animal behavior; Animals; Biological Sciences; Biomechanical Phenomena; case studies; Computer Simulation; Control algorithms; Diptera; Drosophila melanogaster - physiology; flight; Flight dynamics; Flight mechanics; Flight, Animal - physiology; Free flight; fruit flies; Fruits; Hovering; insect control; Insect flight; Insects; Kinematics; Models, Biological; motor neurons; Neurons; Physical Sciences; prediction; Reaction Time - physiology; reflexes; Sensation - physiology; Simulation; simulation models; Time Factors; Torque; Vehicular flight; wings; Wings, Animal - physiology; discrete time-delayed controller; b1 motor neuron; stability of flapping flight; quantitative study of organismal behavior
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.1314738111

Without sensory feedback, flies cannot fly. Exactly how various feedback controls work in insects is a complex puzzle to solve. What do insects measure to stabilize their flight? How often and how fast must insects adjust their wings to remain stable? To gain insights into algorithms used by insects to control their dynamic instability, we develop a simulation tool to study free flight. To stabilize flight, we construct a control algorithm that modulates wing motion based on discrete measurements of the body-pitch orientation. Our simulations give theoretical bounds on both the sensing rate and the delay time between sensing and actuation. Interpreting our findings together with experimental results on fruit flies’ reaction time and sensory motor reflexes, we conjecture that fruit flies sense their kinematic states every wing beat to stabilize their flight. We further propose a candidate for such a control involving the fly’s haltere and first basalar motor neuron. Although we focus on fruit flies as a case study, the framework for our simulation and discrete control algorithms is applicable to studies of both natural and man-made fliers.