Abstract
The dynamic interaction of limb segments during movements that involve multiple joints creates torques in one joint due to motion about another. Evidence shows that such interaction torques are taken into account during the planning or control of movement in humans. Two alternative hypotheses could explain the compensation of these dynamic torques. One involves the use of internal models to centrally compute predicted interaction torques and their explicit compensation through anticipatory adjustment of descending motor commands. The alternative, based on the equilibrium-point hypothesis, claims that descending signals can be simple and related to the desired movement kinematics only, while spinal feedback mechanisms are responsible for the appropriate creation and coordination of dynamic muscle forces. Partial supporting evidence exists in each case. However, until now no model has explicitly shown, in the case of the second hypothesis, whether peripheral feedback is really sufficient on its own for coordinating the motion of several joints while at the same time accommodating intersegmental interaction torques. Here we propose a minimal computational model to examine this question. Using a biomechanics simulation of a two-joint arm controlled by spinal neural circuitry, we show for the first time that it is indeed possible for the neuromusculoskeletal system to transform simple descending control signals into muscle activation patterns that accommodate interaction forces depending on their direction and magnitude. This is achieved without the aid of any central predictive signal. Even though the model makes various simplifications and abstractions compared to the complexities involved in the control of human arm movements, the finding lends plausibility to the hypothesis that some multijoint movements can in principle be controlled even in the absence of internal models of intersegmental dynamics or learned compensatory motor signals.
Highlights
Human multijoint reaching movements are characterized by invariants such as straight hand paths and bell-shaped velocity profiles (Morasso, 1981; Soechting and Lacquaniti, 1981; Atkeson and Hollerbach, 1985)
MATERIALS AND METHODS we describe in detail the biomechanical model of planar arm movements employed in this study; its control via spinal reflex-like neural networks based on known physiology; the integration of spinal dynamics, proprioceptive feedback, and descending commands at α-MNs according to the equilibrium-point hypothesis; and the optimization procedure used to identify model parameters that enable kinematically realistic multijoint movements subject to varying patterns of interaction torques
An alternative view, expressed in the equilibrium point hypothesis, proposes that the combined dynamics of spinal circuitry and musculoskeletal system provide a level of abstraction in the control hierarchy that allows the CNS to plan and control movements without requiring a representation of complex bodily dynamics
Summary
Human multijoint reaching movements are characterized by invariants such as straight hand paths and bell-shaped velocity profiles (Morasso, 1981; Soechting and Lacquaniti, 1981; Atkeson and Hollerbach, 1985). The absence of a signature of these time-varying torques in observed kinematics indicates that intersegmental dynamics are compensated for in the planning or execution of arm movements (Hollerbach and Flash, 1982) Evidence suggests that this compensation is not achieved by executing movements with high stiffness (Gomi and Kawato, 1996; Gribble et al, 1998), which would allow muscle forces to dominate over the passively emerging loads. In computational approaches to motor control the brain is assumed to calculate the time-course of forces necessary to perform desired movements using internal models of the body (Kawato, 1999) This implies the prediction of interaction torques and their explicit compensation through anticipatory adjustment of descending motor commands. Empirical evidence in its support is provided, for example, by experiments showing a correlation between corticospinal excitability and upcoming interaction torques (Gritsenko et al, 2011), or Frontiers in Computational Neuroscience www.frontiersin.org
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