Meeting Abstract

S2-10  Thursday, Jan. 4 14:30 - 15:00  Perturbing the classical muscle work loop paradigm to unravel the neuromechanics of unsteady locomotion SAWICKI, Gregory S.*; SPONBERG, Simon ; Georgia Tech; Georgia Tech

Locomotion emerges from the interaction between the neuromechanics of muscle, compliant skeletal and connective tissues, and the physics of the environment. Classical workloop studies that coupled prescribed, steady-state strain cycles with phasic stimulation and measured net work/cycle revealed that a muscle can adopt diverse functions depending on the context in which it is activated (e.g., motor vs. brake). Perturbations away from steady-state yield transient but extreme demands on both muscle structure and function and offer a unique window for probing time-dependent factors driving force production that cannot not be captured by the FL/FV relations (e.g., short range stiffness). In this talk, we highlight two novel, in situ approaches that aim to extend the classical work loop paradigm in order to more closely approximate ‘real world' locomotion on the benchtop. (1) Top-down: We can first identify salient, unsteady strain and stimulation parameters by recording limb kinematics and muscle activations from freely-moving animals during perturbed behaviors. Then, we can re-play these pre-recorded dynamics back onto that same muscle in isolation driving an intact limb/joint on the benchtop, enabling a systematic exploration of activation timings and or limb/joint trajectories in the vicinity of the ‘real-world’ baseline. (Sponberg) (2) Bottom-up: Alternatively, we can use feedback-controlled robotic tools to emulate the physics of the body/limb and environment on the benchtop, enabling muscle virtual reality experiments - that is, workloops where real muscles interact with artificially rendered loads. (Sawicki). Perturbing the classical muscle work loop paradigm should yield new insights into the dynamic processes and functional limits of muscle that are not exposed during steady conditions.