ASM: Multi-Sensory Control of Tracking Behavior in Weakly Electric Fish

Project Abstract

ASM: Multi-Sensory Control of Tracking Behavior in Weakly Electric Fish

PI: Noah J. Cowan, Dept. of Mechanical Engineering, Johns Hopkins University

Co-PI: Eric S. Fortune, Dept. of Psychological and Brain Sciences, Johns Hopkins University

Intellectual Merit- A tightly integrated and multidisciplinary approach using a uniquely

suited model system will help answer a fundamental question in sensorimotor

integration: how is information processed by the nervous system to control locomotion?

In the model system, weakly electric fish robustly and naturally swim back and forth

to stabilize visual and/or electrosensory images, just as humans smoothly track moving

objects with their eyes to stabilize visual images. This collaborative work builds on the

strengths of the PI, a modeler of sensorimotor locomotion systems and the Co-PI, an

organismal sensory neurobiologist.

The approach incorporates mathematical modeling, behavioral experiments, and

neurophysiological analyses. A mathematical model of the tracking behavior makes

specific, testable predictions of both behavior and neural processing. The model's

predictions of behavior will be tested by systematically varying visual and

electrosensory information available to the fish for tracking a moving sensory

image. The model's predictions will also be tested using central nervous system

recordings in awake, behaving animals. The stimuli will include signals identical

to those used for behavioral experiments, whose input-output relations are

predictable from the model. Broader Impacts Undergraduate and graduate trainees

will receive multidisciplinary training in neuroscience, experimental design, data

collection and analysis, and computational modeling. Further, we will co-teach a

new undergraduate course in sensor-based animal locomotion, and a companion

graduate seminar. Finally, the study of sensorimotor animal behaviors has great

potential to inspire novel strategies for autonomous control in artificial systems.