A neural network model rapidly learning gains and gating of reflexes necessary to adapt to an arm's dynamics

Kalveram, Karl Theodor

doi:10.1007/bf00201440

Cited by 22 publications

(9 citation statements)

References 14 publications

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“…Two types of internal motor models can be distinguished. An inverse dynamics model (IDM) is part of a neural controller that transforms planned kinematic trajectories into appropriate patterns of muscular innervation (Kalveram, 1992; Wolpert et al, 1995; Konczak et al, 2003) necessary to generate the dynamics (i.e., the joint forces) to realize the planned movement (see Figure 3). A forward dynamics model (FDM) transforms efferent motor commands that specify limb dynamics into a set of joint kinematics (Wolpert and Kawato, 1998).…”

Section: A Computational Approach To Sensorimotor Integration and Motmentioning

confidence: 99%

Focal dystonia in musicians: linking motor symptoms to somatosensory dysfunction

Konczak¹,

Abbruzzese²

2013

Front. Hum. Neurosci.

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Musician's dystonia (MD) is a neurological motor disorder characterized by involuntary contractions of those muscles involved in the play of a musical instrument. It is task-specific and initially only impairs the voluntary control of highly practiced musical motor skills. MD can lead to a severe decrement in a musician's ability to perform. While the etiology and the neurological pathomechanism of the disease remain unknown, it is known that MD like others forms of focal dystonia is associated with somatosensory deficits, specifically a decreased precision of tactile and proprioceptive perception. The sensory component of the disease becomes also evident by the patients' use of “sensory tricks” such as touching dystonic muscles to alleviate motor symptoms. The central premise of this paper is that the motor symptoms of MD have a somatosensory origin and are not fully explained as a problem of motor execution. We outline how altered proprioceptive feedback ultimately leads to a loss of voluntary motor control and propose two scenarios that explain why sensory tricks are effective. They are effective, because the sensorimotor system either recruits neural resources normally involved in tactile-proprioceptive (sensory) integration, or utilizes a fully functioning motor efference copy mechanism to align experienced with expected sensory feedback. We argue that an enhanced understanding of how a primary sensory deficit interacts with mechanisms of sensorimotor integration in MD provides helpful insights for the design of more effective behavioral therapies.

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Section: A Computational Approach To Sensorimotor Integration and Motmentioning

confidence: 99%

Focal dystonia in musicians: linking motor symptoms to somatosensory dysfunction

Konczak¹,

Abbruzzese²

2013

Front. Hum. Neurosci.

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“…In general, two types of internal motor models can be distinguished. Inverse models are part of a neural controller that transforms planned kinematic trajectories into appropriate patterns of muscular innervation (Jordan, Flash, & Amon, 1994;Kalveram, 1992;Wolpert, Ghahramani. & Jordan, 1995).…”

mentioning

confidence: 99%

Development of Force Adaptation During Childhood

Konczak

Jansen-Osmann

Kalveram

2003

Journal of Motor Behavior

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Humans learn to make reaching movements in novel dynamic environments by acquiring an internal motor model of their limb dynamics. Here, the authors investigated how 4- to 11-year-old children (N = 39) and adults (N = 7) adapted to changes in arm dynamics, and they examined whether those data support the view that the human brain acquires inverse dynamics models (IDM) during development. While external damping forces were applied, the children learned to perform goal-directed forearm flexion movements. After changes in damping, all children showed kinematic aftereffects indicative of a neural controller that still attempted to compensate the no longer existing damping force. With increasing age, the number of trials toward complete adaptation decreased. When damping was present, forearm paths were most perturbed and most variable in the youngest children but were improved in the older children. The findings indicate that the neural representations of limb dynamics are less precise in children and less stable in time than those of adults. Such controller instability might be a primary cause of the high kinematic variability observed in many motor tasks during childhood. Finally, the young children were not able to update those models at the same rate as the older children, who, in turn, adapted more slowly than adults. In conclusion, the ability to adapt to unknown forces is a developmental achievement. The present results are consistent with the view that the acquisition and modification of internal models of the limb dynamics form the basis of that adaptive process.

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“…However, both methods obviously lack biological plausibility (Kalveram and Schinauer 2002). A recent alternative suggestion is the 'learner-operator model of motor control' (Kalveram 2004) which applies auto-imitation (Kalveram 1981(Kalveram , 1992(Kalveram , 2000 to the part of the learner while exerting control through the operator part. This algorithm appears biologically more plausible than those just mentioned but dispenses with a forward model.…”

Section: Discussionmentioning

confidence: 99%

“…1, suggests an integrative approach to motor control, Correspondence to: K. T. Kalveram (e-mail: kalveram@uni-duesseldorf. de) expressed in terms of cybernetics, uses predicted measurements based on explicit peripheral sensing, and is widely accepted (Kalveram 1992;Stroeve 1997;Kalveram 1998;Bhushan and Shadmehr 1999;Kawato 1999;Desmurget and Grafton 2000;Flanagan et al 2003), though not completely uncontroversial (Ostry and Feldman 2003).…”

Section: Introductionmentioning

confidence: 99%

Threading neural feedforward into a mechanical spring: How biology exploits physics in limb control

et al. 2005

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A solution is proposed of the hitherto unsolved problem as to how neural feedforward through inverse modelling and negative feedback realised by a mechanical spring can be combined to achieve a highly effective control of limb movement. The revised spring approach that we suggest does not require forward modelling and produces simulated data which are as close as possible to experimental human data. Control models based on peripheral sensing with forward modelling, which are favoured in the current literature, fail to create such data. Our approach suggests that current views on motor control and learning should be revisited.

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A neural network model rapidly learning gains and gating of reflexes necessary to adapt to an arm's dynamics

Cited by 22 publications

References 14 publications

Focal dystonia in musicians: linking motor symptoms to somatosensory dysfunction

Focal dystonia in musicians: linking motor symptoms to somatosensory dysfunction

Development of Force Adaptation During Childhood

Threading neural feedforward into a mechanical spring: How biology exploits physics in limb control

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