There is a new momentum in efforts across many fields to understand animal behavior, prompted by the new technology in acquiring large sets of behavioral data and the advancement in neural-genetics. Much needed is the development of sophisticated quantitive models that can explain behavior and can make testable predictions. Finding out how we can start from basic physical laws to explain parts of neural behavior of insect flight is the central theme of the proposed work. Insects were the first that evolved to fly, and to fly is not to fall. Understanding what insects must do so as not to fall provides a pathway to probe the connection between physics of flight and the neural feedback control. This project will test the PI's conjecture on the role of fly’s steering muscle on flight stability, using genetically modified fruit flies. In addition to experiments, the PI will construct computational models that can predict the observed free flight behavior of both intact and genetically modified flies. The proposed work will consist three core parts: 1) 3D tracking of free flight with high resolution, 2) computational studies of control algorithms, and 3) understanding the control and muscle activity by constructing effective models that can explain the experiments. The field of biology is dominated by experimental studies. This key contribution is the development of new methods for analyzing complex biological systems. The work will raise new questions and have a direct impact on research in physics of living organisms, mathematical modeling, neural science, entomology, evolutionary biology, and robotics. The proposed work offers an excellent opportunity for students to engage in interdisciplinary work. The findings on how nature works will provide a compelling case for sharing the value of basic science with the general public.
The proposed work is fundamentally about integrating the physics of flight into our understanding of the neural circuitries for control. Direct neural recordings of flight circuitries require insects to be tethered. One challenge is to connect the internal control algorithms to the free flight behavior. Another challenge is to disentangle different control feedback schemes in intact flies. Using genetically modified flies with specified motor-neurons silenced allows us to single out the function of individual steering muscle. Analyses of the flight reflexes in intact and genetically modified flies will further lead to new models for control schemes that underlie the mechano-sensory feedback for flight equilibrium. This work will be the first to combine computational modeling of free flight with the progress in neuro-genetics to decipher a fly’s equilibrium reflex in free flight. The work will offer a new pathway for flight behavior guided by quantitive predictions, in addition to direct observations.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
