%0 Journal Article %1 Loeb:1999 %A Loeb, G. E. %A Brown, I. E. %A Cheng, E. J. %D 1999 %J Experimental Brain Research %K Modeling, Motor Muscle Muscles, Neural Reflexes, Spinal control, cord, hierarchical model, networks, spindles, %P 1-18 %T A hierarchical foundation for models of sensorimotor control %V 126 %X 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

@article{Loeb: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}, added-at = {2009-06-26T15:25:19.000+0200}, author = {Loeb, G. E. and Brown, I. E. and Cheng, E. J.}, biburl = {https://www.bibsonomy.org/bibtex/24aa6a54ba3427c5a3629a357ad4509ed/butz}, comment = {Es wird ein dreistufiges Modell vorgestellt: Muskeln - Spinal Cord (Regelkreise) - Brain. Dabei sind nur die Muskeln und der Spinalcord moduliert. Verschiedene Gains der Neuronen im Spinal Cord erzeugen unterschiedliches VH in unterschiedlichen Situationen (high oder low freq pertubation, z.B.). Daraus wird die These abgeleitet, dass das Redundanzproblem, dass die verschiedenen Trajektorietheorien (min jerk, etc) lösen durch interne Constraints oder die Anforderungen der Aufgabe gelöst werden. Für jede Aufgabe ist eine bestimmte Kombination von Gains unter best. Gesichtspunkten am besten.}, description = {diverse cognitive systems bib}, interhash = {e9b10fd2b264ddc12e02c5147a0db627}, intrahash = {4aa6a54ba3427c5a3629a357ad4509ed}, journal = {Experimental Brain Research}, keywords = {Modeling, Motor Muscle Muscles, Neural Reflexes, Spinal control, cord, hierarchical model, networks, spindles,}, owner = {martin}, pages = {1-18}, timestamp = {2009-06-26T15:25:45.000+0200}, title = {A hierarchical foundation for models of sensorimotor control}, volume = 126, year = 1999 }