(YIP) FLYING IN AN UNCERTAIN WORLD: DECODING RULES OF ADAPTIVE NEURAL CONTROL IN INSECT FLIGHT
Abstract
Flying in an Uncertain World: Decoding Rules of Adaptive Neural Control in Insect Flight Topic Area: Human Performance and Biosystems ABSTRACT Compared to the best flying robots, insect flight is remarkably robust. This is partly due to the ability of insects to learn over time in a world with ubiquitous uncertainty as well as to adapt to performance degradation from physical damage. For flying insects, environmental uncertainties can have severe consequences for survival, in some instances causing internal perturbations (e.g. wing damage) and thereby reduced mobility. How do flying insects adapt and learn in uncertain environments? Using system analysis of flight behavior in virtual reality, neurogenetics and control theoretic modeling of fly neuro-mechanics, in this proposal we seek to quantify the flexibility of adaptive control mechanisms in fly flight. We aim to reverse engineer insect adaptive control by pursuing three broad but inter-related objectives. First, we will investigate how flies adapt to internal perturbations such as wing damage in virtual reality. Second, we will study how flies implement adaptive control of the head and wings by using a real-time reinforcement learning algorithm. Finally, we will quantify how flies modulate feedforward internal models in ‘augmented reality’ flight. Our proposed work will advance the state of the art by 1) leveraging technical innovations that will decode the rules that flies implement to adapt to internal perturbations, control multiple motor outputs and tune internal models and 2) unravel neural
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 12, 2021
- Source ID
- FA95502010084
Entities
People
- Jean-Michel Mongeau
Organizations
- Air Force Office of Scientific Research
- Pennsylvania State University
- United States Air Force