Short- and long-term changes in joint co-contraction associated with motor learning as revealed from surface EMG.

In the field of motor control, two hypotheses have been controversial: whether the brain acquires internal models that generate accurate motor commands, or whether the brain avoids this by using the viscoelasticity of musculoskeletal system. Recent observations on relatively low stiffness during trained movements support the existence of internal models. However, no study has revealed the decrease in viscoelasticity associated with learning that would imply improvement of internal models as well as synergy between the two hypothetical mechanisms. Previously observed decreases in electromyogram (EMG) might have other explanations, such as trajectory modifications that reduce joint torques. To circumvent such complications, we required strict trajectory control and examined only successful trials having identical trajectory and torque profiles. Subjects were asked to perform a hand movement in unison with a target moving along a specified and unusual trajectory, with shoulder and elbow in the horizontal plane at the shoulder level. To evaluate joint viscoelasticity during the learning of this movement, we proposed an index of muscle co-contraction around the joint (IMCJ). The IMCJ was defined as the summation of the absolute values of antagonistic muscle torques around the joint and computed from the linear relation between surface EMG and joint torque. The IMCJ during isometric contraction, as well as during movements, was confirmed to correlate well with joint stiffness estimated using the conventional method, i.e., applying mechanical perturbations. Accordingly, the IMCJ during the learning of the movement was computed for each joint of each trial using estimated EMG-torque relationship. At the same time, the performance error for each trial was specified as the root mean square of the distance between the target and hand at each time step over the entire trajectory. The time-series data of IMCJ and performance error were decomposed into long-term components that showed decreases in IMCJ in accordance with learning with little change in the trajectory and short-term interactions between the IMCJ and performance error. A cross-correlation analysis and impulse responses both suggested that higher IMCJs follow poor performances, and lower IMCJs follow good performances within a few successive trials. Our results support the hypothesis that viscoelasticity contributes more when internal models are inaccurate, while internal models contribute more after the completion of learning. It is demonstrated that the CNS regulates viscoelasticity on a short- and long-term basis depending on performance error and finally acquires smooth and accurate movements while maintaining stability during the entire learning process.

Bipedal walking and running with spring-like biarticular muscles.

Compliant elements in the leg musculoskeletal system appear to be important not only for running but also for walking in human locomotion as shown in the energetics and kinematics studies of spring-mass model. While the spring-mass model assumes a whole leg as a linear spring, it is still not clear how the compliant elements of muscle-tendon systems behave in a human-like segmented leg structure. This study presents a minimalistic model of compliant leg structure that exploits dynamics of biarticular tension springs. In the proposed bipedal model, each leg consists of three leg segments with passive knee and ankle joints that are constrained by four linear tension springs. We found that biarticular arrangements of the springs that correspond to rectus femoris, biceps femoris and gastrocnemius in human legs provide self-stabilizing characteristics for both walking and running gaits. Through the experiments in simulation and a real-world robotic platform, we show how behavioral characteristics of the proposed model agree with basic patterns of human locomotion including joint kinematics and ground reaction force, which could not be explained in the previous models.

Self-organization of reflexive behavior from spontaneous motor activity.

In mammals, the development of reflexes is often regarded as an innate process. However, recent findings show that fetuses are endowed with favorable conditions for ontogenetic development. In this article, we hypothesize that the circuitry of at least some mammalian reflexes can be self-organized from the sensory and motor interactions brought forth in a musculoskeletal system. We focus mainly on three reflexes: the myotatic reflex, the reciprocal inhibition reflex, and the reverse myotatic reflex. To test our hypothesis, we conducted a set of experiments on a simulated musculoskeletal system using pairs of agonist and antagonist muscles. The reflex connectivity is obtained by producing spontaneous motor activity in each muscle and by correlating the resulting sensor and motor signals. Our results show that, under biologically plausible conditions, the reflex circuitry thus obtained is consistent with that identified in relation to the analogous mammalian reflexes. In addition, they show that the reflex connectivity obtained depends on the morphology of the musculoskeletal system as well as on the environment that it is embedded in.

Twitching in sensorimotor development from sleeping rats to robots.

It is still not known how the 'rudimentary' movements of fetuses and infants are transformed into the coordinated, flexible and adaptive movements of adults. In addressing this important issue, we consider a behavior that has been perennially viewed as a functionless by-product of a dreaming brain: the jerky limb movements called myoclonic twitches. Recent work has identified the neural mechanisms that produce twitching as well as those that convey sensory feedback from twitching limbs to the spinal cord and brain. In turn, these mechanistic insights have helped inspire new ideas about the functional roles that twitching might play in the self-organization of spinal and supraspinal sensorimotor circuits. Striking support for these ideas is coming from the field of developmental robotics: when twitches are mimicked in robot models of the musculoskeletal system, the basic neural circuitry undergoes self-organization. Mutually inspired biological and synthetic approaches promise not only to produce better robots, but also to solve fundamental problems concerning the developmental origins of sensorimotor maps in the spinal cord and brain.