Control of position and movement is simplified by combined muscle spindle and Golgi tendon organ feedback

Whereas muscle spindles play a prominent role in current theories of human motor control, Golgi tendon organs (GTO) and their associated tendons are often neglected. This is surprising since there is ample evidence that both tendons and GTOs contribute importantly to neuromusculoskeletal dynamics. Using detailed musculoskeletal models, we provide evidence that simple feedback using muscle spindles alone results in very poor control of joint position and movement since muscle spindles cannot sense changes in tendon length that occur with changes in muscle force. We propose that a combination of spindle and GTO afferents can provide an estimate of muscle-tendon complex length, which can be effectively used for low-level feedback during both postural and movement tasks. The feasibility of the proposed scheme was tested using detailed musculoskeletal models of the human arm. Responses to transient and static perturbations were simulated using a 1-degree-of-freedom (DOF) model of the arm and showed that the combined feedback enabled the system to respond faster, reach steady state faster, and achieve smaller static position errors. Finally, we incorporated the proposed scheme in an optimally controlled 2-DOF model of the arm for fast point-to-point shoulder and elbow movements. Simulations showed that the proposed feedback could be easily incorporated in the optimal control framework without complicating the computation of the optimal control solution, yet greatly enhancing the system's response to perturbations. The theoretical analyses in this study might furthermore provide insight about the strong physiological couplings found between muscle spindle and GTO afferents in the human nervous system. © 2013 the American Physiological Society

Adaptation to Delayed Force Perturbations in Reaching Movements

Adaptation to deterministic force perturbations during reaching movements was extensively studied in the last few decades. Here, we use this methodology to explore the ability of the brain to adapt to a delayed velocity-dependent force field. Two groups of subjects preformed a standard reaching experiment under a velocity dependent force field. The force was either immediately proportional to the current velocity (Control) or lagged it by 50 ms (Test). The results demonstrate clear adaptation to the delayed force perturbations. Deviations from a straight line during catch trials were shifted in time compared to post-adaptation to a non-delayed velocity dependent field (Control), indicating expectation to the delayed force field. Adaptation to force fields is considered to be a process in which the motor system predicts the forces to be expected based on the state that a limb will assume in response to motor commands. This study demonstrates for the first time that the temporal window of this prediction needs not to be fixed. This is relevant to the ability of the adaptive mechanisms to compensate for variability in the transmission of information across the sensory-motor system

Distribution of motor unit potential velocities in short static and prolonged dynamic contractions at low forces: use of the within-subject’s skewness and standard deviation variables

Behaviour of motor unit potential (MUP) velocities in relation to (low) force and duration was investigated in biceps brachii muscle using a surface electrode array. Short static tests of 3.8 s (41 subjects) and prolonged dynamic tests (prolonged tests) of 4 min (30 subjects) were performed as position tasks, applying forces up to 20% of maximal voluntary contraction (MVC). Four variables, derived from the inter-peak latency technique, were used to describe changes in the surface electromyography signal: the mean muscle fibre conduction velocity (CV), the proportion between slow and fast MUPs expressed as the within-subject skewness of MUP velocities, the within-subject standard deviation of MUP velocities [SD-peak velocity (PV)], and the amount of MUPs per second (peak frequency = PF). In short static tests and the initial phase of prolonged tests, larger forces induced an increase of the CV and PF, accompanied with the shift of MUP velocities towards higher values, whereas the SD-PV did not change. During the first 1.5–2 min of the prolonged lower force levels tests (unloaded, and loaded 5 and 10% MVC) the CV and SD-PV slightly decreased and the MUP velocities shifted towards lower values; then the three variables stabilized. The PF values did not change in these tests. However, during the prolonged higher force (20% MVC) test, the CV decreased and MUP velocities shifted towards lower values without stabilization, while the SD-PV broadened and the PF decreased progressively. It is argued that these combined results reflect changes in both neural regulatory strategies and muscle membrane state

Linear Systems Description

Introduction The systems approach is a widely used practice in modeling artificial as well as natural phenomena. Each process or sub-process is viewed as an input-output system, as described graphically in Fig.1. This approach is used extensively in engineering, for example in modeling electronic and mechanical systems and in chemical process description. In this chapter we describe this approach and its application to biological systems in general and the nervous system in particular. The systems approach can be used as a modeling tool to comprehend the function of the system and to produce a hypothetical model which can be tested in experiments. It is useful in describing and characterising experimental results, at times by relating the anatomical and physiological properties to the measured variables (see for example the muscle spindle transfer function, Houk 1963). Mathematical modeling of part of the neurological system can be used to study that and other parts by simula