An optimality principle for locomotor central pattern generators
Two types of neural circuits contribute to legged locomotion: central pattern generators (CPGs) that produce rhythmic motor commands (even in the absence of feedback, termed "fictive locomotion"), and reflex circuits driven by sensory feedback. Each circuit alone serves a clear purpose, and the two together are understood to cooperate during normal locomotion. The difficulty is in explaining their relative balance objectively within a control model, as there are infinite combinations that could produce the same nominal motor pattern. Here we propose that optimization in the presence of uncertainty can explain how the circuits should best be combined for locomotion. The key is to re- interpret the CPG in the context of state estimator-based control: an internal model of the limbs that predicts their state, using sensory feedback to optimally balance competing effects of environmental and sensory uncertainties. We demonstrate use of optimally predicted state to drive a simple model of bipedal, dynamic walking, which thus yields minimal energetic cost of transport and best stability. The internal model may be implemented with classic neural half-center circuitry, except with neural parameters determined by optimal estimation principles. Fictive locomotion also emerges, but as a side effect of estimator dynamics rather than an explicit internal rhythm. Uncertainty could be key to shaping CPG behavior and governing optimal use of feedback. New and NoteworthySensory feedback modulates the central pattern generator (CPG) rhythm in locomotion, but there lacks an explanation for how much feedback is appropriate. We propose destabilizing noise as a determinant, where an uncertain environment demands more feedback, but noisy sensors demand less. We reinterpret the CPG as an internal model for predicting body state despite noise. Optimizing its feedback yields robust and economical gait in a walking model, and explains the advantages of feedback-driven CPG control.