Internal Models for Motor Control

Kawato, Mitsuo; Wolpert, Daniel M.

doi:10.1002/9780470515563.ch16

Cited by 164 publications

(136 citation statements)

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“…Kistemaker et al (2006Kistemaker et al ( , 2007b. It could also be open-loop muscle activations produced by (a combination of) forward and inverse neural models (e.g., Kawato 1999;Kawato and Wolpert 1998;Todorov 2004). In particular, we incorporated the proposed scheme in an optimally controlled 2-DOF model of the arm for fast point-topoint shoulder and elbow movements, without further complicating the computation of the OC solution yet greatly enhancing the system's response to perturbations.…”

Section: Discussionmentioning

confidence: 99%

“…This is very different from using MTC lengths, because CE (reference) lengths depend on the forces produced by the muscles and MTC length does not (see RESULTS, Response to Changes in Stiffness Level). To calculate CE reference lengths, the CNS would have to have full knowledge about the statics and dynamics of muscle contraction, for example, in the form of a "forward internal model" (e.g., Kawato 1999;Kawato and Wolpert 1998;Todorov and Jordan 2002;Wolpert et al 1995), and calculate the CE reference lengths from the open-loop muscle activations. This is unequivocally not a simple task, because such a calculation in a real system would require knowledge about, among other things, motor neuron pool dynamics [such as the size principle (Henneman et al 1965) and rate coding (Monster and Chan 1977)], contraction dynamics (the force-length-velocity relationship), (history dependent) muscle activation dynamics, skeletal dynamics, and dynamics of the environment.…”

Section: Incorporating Gto Feedback In Existing Modelsmentioning

confidence: 99%

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Control of position and movement is simplified by combined muscle spindle and Golgi tendon organ feedback

Kistemaker

Soest

Wong

et al. 2013

Journal of Neurophysiology

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Kistemaker DA, Van Soest AJ, Wong JD, Kurtzer I, Gribble PL. Control of position and movement is simplified by combined muscle spindle and Golgi tendon organ feedback. J Neurophysiol 109: 1126-1139, 2013. First published October 24, 2012 doi:10.1152/jn.00751.2012.-Whereas muscle spindles play a prominent role in current theories of human motor control, Golgi tendon organs (GTO) and their associated tendons are often neglected. This is surprising since there is ample evidence that both tendons and GTOs contribute importantly to neuromusculoskeletal dynamics. Using detailed musculoskeletal models, we provide evidence that simple feedback using muscle spindles alone results in very poor control of joint position and movement since muscle spindles cannot sense changes in tendon length that occur with changes in muscle force. We propose that a combination of spindle and GTO afferents can provide an estimate of muscle-tendon complex length, which can be effectively used for low-level feedback during both postural and movement tasks. The feasibility of the proposed scheme was tested using detailed musculoskeletal models of the human arm. Responses to transient and static perturbations were simulated using a 1-degree-of-freedom (DOF) model of the arm and showed that the combined feedback enabled the system to respond faster, reach steady state faster, and achieve smaller static position errors. Finally, we incorporated the proposed scheme in an optimally controlled 2-DOF model of the arm for fast point-to-point shoulder and elbow movements. Simulations showed that the proposed feedback could be easily incorporated in the optimal control framework without complicating the computation of the optimal control solution, yet greatly enhancing the system's response to perturbations. The theoretical analyses in this study might furthermore provide insight about the strong physiological couplings found between muscle spindle and GTO afferents in the human nervous system. sensorimotor control; tendon compliance; optimal control; perturbations IN THIS ARTICLE, we provide evidence that simple low-level spinal feedback using muscle spindles alone results in very poor control of joint position and movement. In short, this is because muscle spindles cannot detect the changes in muscletendon complex (MTC) length that occur as a consequence of tendon stretch. We propose that afferent signals from Golgi tendon organs (GTOs) can be seen as a proxy for tendon length and that, in combination with muscle spindles, they can be effectively used for low-level feedback during both postural and movement tasks. Before describing in more detail the specific aims of this study, we first provide an overview of the current view on the role of muscle spindles and GTOs relevant to this article.In recent years several theories have been postulated about how the central nervous system (CNS) controls position and movement. These theories all share the common premise that to control movements, the CNS must have information about the current state of the mu...

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Section: Discussionmentioning

confidence: 99%

Section: Incorporating Gto Feedback In Existing Modelsmentioning

confidence: 99%

Control of position and movement is simplified by combined muscle spindle and Golgi tendon organ feedback

Kistemaker

Soest

Wong

et al. 2013

Journal of Neurophysiology

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“…As in previous studies, we also found day-to-day retention of learning (Shadmehr and Brashers-Krug, 1997) with aftereffects that were maintained overnight. These behavioral signatures are commonly considered to reflect modifications of internal models (Kawato and Wolpert, 1998;Kawato, 1999;Hwang et al, 2006).…”

Section: Discussionmentioning

confidence: 99%

The Neuronal Basis of Long-Term Sensorimotor Learning

Mandelblat-Cerf

Novick²,

Paz³

et al. 2011

J. Neurosci.

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The brain has a remarkable ability to learn and adjust behavior. For instance, the brain can adjust muscle activation to cope with changes in the environment. However, the neuronal mechanisms behind this adaptation are not clear. To address this fundamental question, this study examines the neuronal basis of long-term sensorimotor learning by recording neuronal activity in the primary motor cortex of monkeys during a long-term adaptation to a force-field perturbation. For 5 consecutive days, the same perturbation was applied to the monkey's hand when reaching to a single target, whereas movements to all other targets were not perturbed. The gradual improvement in performance over these 5 days was correlated to the evolvement in the population neuronal signal, with two timescales of changes in single-cell activity. Specifically, one subgroup of cells showed a relatively fast increase in activity, whereas the other showed a gradual, slower decrease. These adapted patterns of neuronal activity did not involve changes in directional tuning of single cells, suggesting that adaptation was the result of adjustments of the required motor plan by a population of neurons rather than changes in single-cell properties. Furthermore, generalization was mostly expressed in the direction of the required compensatory force during adaptation. Altogether, the neuronal activity and its generalization accord with the adapted motor plan.

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“…The ability to predict the optimal force on subsequent attempts is called feedforward control and is thought to occur due to the formation of an internal representation of the object's properties, in this case the objects' weight, in the central nervous system (Davidson and Wolpert 2004;Gordon et al 1993;Johansson and Westling 1988a). Feedforward control implies that two critical processes are occurring: 1) the error between the estimated force and the needed force is accurately detected, and 2) the error information is used to update or adapt the motor response on a subsequent attempt to execute the task optimally (Blakemore et al 1998;Drake and Palmer 2000;Kawato and Wolpert 1998;Shadmehr et al 2010;Wolpert et al 1995;Wolpert and Miall 1996). The development of feedforward control is important because it leads to rapid reduction in error and faster relearning on subsequent attempts (Huang et al 2011;Krakauer and Mazzoni 2011) and suggests that the motor system is capable of learning.…”

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confidence: 99%