A hierarchical foundation for models of sensorimotor control
Experimental Brain Research, (1999)Abstract
Successful performance of a sensorimotor task arises from the interaction
of descending commands from the brain with the intrinsic properties
of the lower levels of the sensorimotor system, including the dynamic
mechanical properties of muscle, the natural coordinates of somatosensory
receptors, the interneuronal circuitry of the spinal cord, and computational
noise in these elements. Engineering models of biological motor control
often oversimplify or even ignore these lower levels because they
appear to complicate an already difficult problem. We modeled three
highly simplified control systems that reflect the essential attributes
of the lower levels in three tasks: acquiring a target in the face
of random torque-pulse perturbations, optimizing fusimotor gain for
the same perturbations, and minimizing postural error versus energy
consumption during low- versus high-frequency perturbations. The
emergent properties of the lower levels maintained stability in the
face of feedback delays, resolved redundancy in over-complete systems,
and helped to estimate loads and respond to perturbations. We suggest
a general hierarchical approach to modeling sensorimotor systems,
which better reflects the real control problem faced by the brain,
as a first step toward identifying the actual neurocomputational
steps and their anatomical partitioning in the brain
Description
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