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Neural Control of Locomotion in Weakly Electric Fish
Figure 1: View from beneath a weakly electric knifefish hiding in a shuttle.
Figure 2: A: Schematic of our computer-controlled moving shuttle. B: Overhead view, showing the location of the shuttle r(t) (green), location of the fish x(t) (magenta) and the resulting error signal e(t) (blue).
Figure 3: Tracking data from an individual fish.
Description
Animals must rapidly process sensory information to control their locomotion. How does an animal’s nervous system translate this information into control signals in the brain? This collaborative project with Professor Noah Cowan in Mechanical Engineering, and Eric Fortune in the Psychological and Brain Sciences Department, addresses this question using a unique species of so-called “weakly electric knifefish”. The fish are called ‘weakly electric’ because they produce tiny electric voltages that enable them to “see” surrounding objects with special electroreceptors all over the body surface. They are called ‘knifefish’ because of their ‘knife-like’ body shape that allows them to slip through the water using a specialized undulatory fin (Figure 1).
Curiously, these weakly electric knifefish like to swim back and forth to remain hidden in a moving shuttle (Figure 2) using feedback from both their eyes as well as their electric sense. By driving the shuttle back and forth, the fish works to stay in the shuttle, but as the shuttle is moved at higher and higher frequencies, the fish lose the ability to track the motion (Figure 3). A detailed mathematical analysis of this behavior suggests that the animal's sensors and brain are “tuned” to take into account Newton’s laws of motion. This project is broadly applicable because locomotor mechanics are similar in a wide array of animals. Thus these data suggest that the neural systems that control locomotion are directly tuned to take into account the underlying mechanics of locomotion.
Publications
N. J. Cowan and E. S. Fortune (2006). The Critical Role of Locomotion Mechanics in Decoding Sensory Systems. J Neurosci. Accepted.