Nested mechanosensory feedback actively damps visually guided head movements in Drosophila

Abstract

Executing agile locomotion requires animals to integrate sensory feedback, often from multiple sources. For example, human gaze is mediated by multiple feedback loops that integrate visual and vestibular information. A central challenge in studying biological feedback loops is that they are nested and dynamically coupled. Here, we develop a framework based on control theory for unraveling nested feedback systems and apply it to study gaze stabilization in the fruit fly (Drosophila). By combining experimental and mathematical methods to manipulate control topologies, we uncovered the role of body-generated mechanosensory feedback nested within visual feedback in the control of head movements. We discovered that visual feedback changed the tuning of head movements across visual motion frequencies whereas mechanosensory feedback damped head movements. Head saccades had slower dynamics when the body was free to move, further pointing to the role of damping via mechanosensory feedback. By comparing head responses between self-generated and externally generated body motion, we revealed a nonlinear gating of mechanosensory feedback that is motor-context dependent. Altogether, our findings reveal the role of nested feedback loops in flies and uncover mechanisms that reconcile differences in head kinematics between body-free and body-fixed flies. Our framework is generalizable to biological and robotic systems relying on nested feedback control for guiding locomotion.

Document Details

Document Type
Pub Defense Publication
Publication Date
Oct 19, 2022
Source ID
10.7554/elife.80880

Entities

People

  • Benjamin Cellini
  • Jean-Michel Mongeau

Organizations

  • Air Force Office of Scientific Research
  • Alfred P. Sloan Foundation
  • Pennsylvania State University

Tags

Fields of Study

  • Biology

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Neurological Diseases/Conditions/Disorders
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • Autonomy
  • Autonomy - Autonomous System Control