1 Abstract
Animals (including humans) can readily learn to compensate for destabilizing dynamics, such as balancing an object or riding a bicycle, but it remains unclear how the nervous system learns to compensate for such destabilizing dynamics, and what the benefits of the newly learned control policies are. We examined how the weakly electric glass knifefish, Eigenmannia virescens, retunes its control system in the face of novel, destabilizing dynamics. Using a real-time feedback system, we measured fish swimming movements as they tracked a moving refuge, and fed the swimming movements back through novel dynamics to alter the motion of the very refuge they were tracking, creating an artificially destabilized reafferent loop. We discovered that fish learned to retune their sensorimotor controllers as the artificially destabilizing feedback was gradually introduced. This retuned controller reduced sensitivity of the sensorimotor system to low-frequency disturbances, such as would arise from turbulence or motor noise. When the artificial feedback was extinguished, fish exhibited a clear after effect, retaining their learned sensorimotor controllers for several minutes before washing out. This study sheds light on how the nervous system adapts to changing closed-loop dynamics and how those changes improve performance, and the after effects suggest a plasticity-based mechanism reminiscent of cerebellar learning.
Competing Interest Statement
The authors have declared no competing interest.
