Cross-modal sensory interactions, processing, and representation in the Drosophila brain

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Robust navigation, which is critical for an animal’s survival, requires the processing of complex sensory information spanning different modalities and time scales. Unlike human-engineered systems, where sensors are passive and modularized and decisions are typically made centrally, biological sensors constantly interact and influence each other, and behavioral decisions are made on different time scales with diverse goals. Further, such decisions are based on actively collected sensory information. Our team’s long-term goal is to elucidate the entire neural circuit dynamics from inter-sensory interaction and multisensory integration in the central brain to the generation of motor commands across different time scales. We use Drosophila melanogaster to take advantage of the genetic tools, rich behaviors, and the available tools to reconstruct the neural circuits with full brain electron microscopy data. We will investigate how visual information, mechanical information (wind), and gyroscopic information (sensing body rotation) are integrated and used to generate motor commands. We aim to test the following hypotheses: (1) Sensory information from one modality affects the processing of different sensory modalities (e.g., gyroscopic sensation may drive neck muscles to effectively change the visual input). (2) Information from multiple sensors is integrated in the central brain using attractor dynamics that unify winner-takes-all and Kalman filter mechanisms. (3) The central brain not only integrates sensory stimuli, but actively drives sensory muscles to maximize task-relevant information, increasing robustness of sensory processing. To test these hypotheses, we combine our team’s diverse and complementary expertise, including two- photon calcium imaging, one photon muscle imaging, whole-cell patch clamping, precise sensory perturbation, high speed behavioral analysis, aerodynamics, single-cell resolution optogenetic perturbation, computational modeling, network theory, and control theory. We will develop a new control theoretical framework supported by anatomical, behavioral, and physiological experiments. We will test predictions of models using behavioral and optogenetic perturbation methods to further refine the theory. The successful completion of this project will put our team in an ideal position to further investigate multiple sensorimotor transformation pathways with different time scales, spanning reflexive response (fast), obstacle avoidance (medium), and voluntary navigational decisions (slow). Overall, our team aims to reveal the computational principles of neural network dynamics underlying robust navigation.