Department of Electrical and Electronics Engineering, Hacettepe University, Ankara, Turkey; Laboratory of Computational Sensing and Robotics, Johns Hopkins University, Baltimore, United States; Department of Biological Sciences, New Jersey Institute of Technology, Newark, United States
Shahin Sefati
Department of Mechanical Engineering, Johns Hopkins University, Baltimore, United States
Sarah A Stamper
Department of Mechanical Engineering, Johns Hopkins University, Baltimore, United States
Kyoung-A Cho
Department of Mechanical Engineering, Johns Hopkins University, Baltimore, United States
M Mert Ankarali
Department of Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkey
Laboratory of Computational Sensing and Robotics, Johns Hopkins University, Baltimore, United States; Department of Mechanical Engineering, Johns Hopkins University, Baltimore, United States
Animals vary considerably in size, shape, and physiological features across individuals, but yet achieve remarkably similar behavioral performances. We examined how animals compensate for morphophysiological variation by measuring the system dynamics of individual knifefish (Eigenmannia virescens) in a refuge tracking task. Kinematic measurements of Eigenmannia were used to generate individualized estimates of each fish’s locomotor plant and controller, revealing substantial variability between fish. To test the impact of this variability on behavioral performance, these models were used to perform simulated ‘brain transplants’—computationally swapping controllers and plants between individuals. We found that simulated closed-loop performance was robust to mismatch between plant and controller. This suggests that animals rely on feedback rather than precisely tuned neural controllers to compensate for morphophysiological variability.
