title: A physiologically based hypothesis for learning proprioception and in approximating inverse kinematics.
abstract: A long-standing problem in muscle control is the "curse of dimensionality". In part, this problem relates to the fact that coordinated movement is only achieved through the simultaneous contraction and extension of multitude muscles to specific lengths. Couched in robotics terms, the problem includes the determination of forward and inverse kinematics. Of the many neurophysiological discoveries in cortex is the existence of position gradients. Geometrically, position gradients are described by planes in Euclidean space whereby neuronal activity increases as the hand approaches locations that lie in a plane. This work demonstrates that position gradients, when coupled with known physiology in the spinal cord, allows for a way to approximate proprioception (forward kinematics) and to specify muscle lengths for goal-directed postures (inverse kinematics). Moreover, position gradients provide a means to learn and adjust kinematics as animals learn to move and grow. This hypothesis is demonstrated using computer simulation of a human arm. Finally, experimental predictions are described that might confirm or falsify the hypothesis.
authors.affiliation: MEMM Department, California State University, Chico, California msimkins@csuchico.edu simkinsmatt@hotmail.com.
authors.forename: Matt
authors.initials: M
authors.lastname: Simkins
mesh_terms: D001696:Biomechanical Phenomena / Q000502:physiology; D003198:Computer Simulation; D005542:Forearm / Q000502:physiology; D006801:Humans; D007858:Learning / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D011434:Proprioception / Q000502:physiology*
keywords: Inverse kinematics; position gradients; synergy; virtual points