Role of descending pathways in sensorimotor transformations: from single nodes to complex networks

Emerging Drosophila EM datasets of the complete nervous system provide a wealth of testable predictions for visual-to-motor transformations. Here, I will discuss how predictions emerging from our connectomics analyses have been used to map descending networks that drive object avoidance maneuvers during flight. Visual input predictions from connectomics have informed our understanding of the encoded visual information provided to these networks. Circuit motifs identified through connectomics have required us to revise current ideas on how descending networks are organized to exert behavioral control. Motor outputs identified through connectomics have aided our understanding of hierarchy and redundancy in motor control. By combining our connectomics analyses with electrophysiology, behavioral assays, and computational modeling, we are able to predict and directly evaluate the behavioral impact of silencing particular nodes within descending networks, and resolve subtle changes in motor patterns even when the overall behavioral output remains intact. Altogether, we demonstrate how connectomics analyses and direct experimental evaluation can generate a mechanistic understanding of how behaviors arise from sensory inputs. 

Dr. Catherine von Reyn is an Associate Professor at Drexel University.

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