Resilience of neural networks for locomotion

Locomotion is an essential behavior for the survival of all animals. The neural circuitry underlying locomotion is therefore highly robust to a wide variety of perturbations, including injury and abrupt changes in the environment. On the short term, fault tolerance in neural networks allows locomotion to persist immediately after mild to moderate injury. On the longer term, in many invertebrates and vertebrates, neural reorganization including anatomical regeneration can restore locomotion after severe perturbations that initially caused paralysis. Despite decades of research, very little is known about the mechanisms underlying locomotor resilience at the level of the underlying neural circuits and coordination of central pattern generators (CPGs). Undulatory locomotion is an ideal behavior for exploring principles of circuit organization, neural control and resilience of locomotion, offering a number of unique advantages lending experimental accessibility and modeling tractability. In comparing three well-characterized undulatory swimmers, lampreys, larval zebrafish, and Caenorhabditis elegans, we find similarities in the manifestation of locomotor resilience. To advance our understanding, we propose a comparative approach, integrating experimental and modeling studies, which will allow the field to begin identifying shared and distinct solutions for overcoming perturbations to persist orchestrating this essential behavior.