Robustness of Connectionist Swimming Controllers Against Random Variation in Neural Connections

Abstract

The ability to achieve high swimming speed and efficiency is very important to both the real lamprey and its robotic implementation. In previous studies, we used evolutionary algorithms to evolve biologically plausible connectionist swimming controllers for a simulated lamprey. This letter investigates the robustness and optimality of the best-evolved controllers as well as the biological controller hand-crafted by Ekeberg. Comparing cases of random variation in intrasegmental or intersegmental weights against each controller allows estimates of robustness to be made. We conduct experiments on the controllers' robustness at the excitation level, which corresponds to either the maximum swimming speed or efficiency by randomly varying the segmental connection weights and on some occasions also the intersegmental couplings, through varying noise ranges. Interestingly, although the swimming performance (in terms of maximum speed and efficiency) of the Ekeberg biological controller is not as good as that of the artificially evolved controllers, it is relatively robust against noise in the neural networks. This suggests that the natural evolutions have evolved a swimming controller that is good enough to survive in the real world. Our findings could inspire neurobiologists to conduct real physiological experiments to gain a better understanding on how neural connectivity affects behavior. The results can also be applied to control an artificial lamprey in simulation and possibly also a robotic one.