%0 Journal Article %1 Schweighofer:1998a %A Schweighofer, Nicolas %A Arbib, Michael A. %A Kawato, Mitsuo %D 1998 %J European Journal of Neuroscience %K 5, C3 C4 area cerebellum, cortex, dynamics, internal inverse models, motor neural trajectory, virtual %P 86-94 %T Role of the cerebellum in reaching movements in humans. I. Distributed inverse dynamics control %V 10 %X This study focuses on the role of the motor cortex, the spinal cord and the cerebellum in the dynamics stage of the control of arm movement. Currently, two classes of models have been proposed for the neural control of movements, namely the virtual trajectory control hypothesis and the acquisition of internal models of the motor apparatus hypothesis. In the present study, we expand the virtual trajectory model to whole arm reaching movements. This expanded model accurately reproduced slow movements, but faster reaching movements deviated significantly from the planned trajectories, indicating that for fast movements, this model was not sufficient. These results led us to propose a new distributed functional model consistent with behavioural, anatomical and neurophysiological data, which takes into account arm muscles, spinal cord, motor cortex and cerebellum and is consistent with the view that the central nervous system acquires a distributed inverse dynamics model of the arm. Previous studies indicated that the cerebellum compensates for the interaction forces that arise during reaching movements. We show here how the cerebellum may increase the accuracy of reaching movements by compensating for the interaction torques by learning a portion of an inverse dynamics model that refines a basic inverse model in the motor cortex and spinal cord.

@article{Schweighofer:1998a, abstract = {This study focuses on the role of the motor cortex, the spinal cord and the cerebellum in the dynamics stage of the control of arm movement. Currently, two classes of models have been proposed for the neural control of movements, namely the virtual trajectory control hypothesis and the acquisition of internal models of the motor apparatus hypothesis. In the present study, we expand the virtual trajectory model to whole arm reaching movements. This expanded model accurately reproduced slow movements, but faster reaching movements deviated significantly from the planned trajectories, indicating that for fast movements, this model was not sufficient. These results led us to propose a new distributed functional model consistent with behavioural, anatomical and neurophysiological data, which takes into account arm muscles, spinal cord, motor cortex and cerebellum and is consistent with the view that the central nervous system acquires a distributed inverse dynamics model of the arm. Previous studies indicated that the cerebellum compensates for the interaction forces that arise during reaching movements. We show here how the cerebellum may increase the accuracy of reaching movements by compensating for the interaction torques by learning a portion of an inverse dynamics model that refines a basic inverse model in the motor cortex and spinal cord.}, added-at = {2009-06-26T15:25:19.000+0200}, author = {Schweighofer, Nicolas and Arbib, Michael A. and Kawato, Mitsuo}, biburl = {https://www.bibsonomy.org/bibtex/27183c9fcc74a609074037679ee76d845/butz}, comment = {C4 spinal interneurons and finally causes movement. The model accounts for slow movements, but for faster movements, the trajectories are heavily curved and show overshooting and end point tremor. Hence, it is proposed that an additional torque generated by the CBM adds to the sum torque. This CBM torque is supposed to minimize the problems of the current models. Maybe, additonal forward models might assist in reducing errors due to feedback. Learning is considered FB-error learning.:A trajectory generator in Area 5 chooses desired position, velocity and acceleration. A cortical FB-loop generates a torque to minimize Error in a PD-controller like fashion. Additional, a cortical inverse dynamics model refines the feedback torque to account for limb interactions, etc. This sum torque is processed in C3 / C4 spinal interneurons and finally causes movement. The model accounts for slow movements, but for faster movements, the trajectories are heavily curved and show overshooting and end point tremor. Hence, it is proposed that an additional torque generated by the CBM adds to the sum torque. This CBM torque is supposed to minimize the problems of the current models. Maybe, additonal forward models might assist in reducing errors due to feedback. Learning is considered FB-error learning.:PDF}, description = {diverse cognitive systems bib}, interhash = {55fdb6736d98b55e4e8d1e5cb2c4217d}, intrahash = {7183c9fcc74a609074037679ee76d845}, journal = {European Journal of Neuroscience}, keywords = {5, C3 C4 area cerebellum, cortex, dynamics, internal inverse models, motor neural trajectory, virtual}, owner = {martin}, pages = {86-94}, timestamp = {2009-06-26T15:25:54.000+0200}, title = {Role of the cerebellum in reaching movements in humans. {I}. {D}istributed inverse dynamics control}, volume = 10, year = 1998 }