Roles of Proprioceptive Input in the Programming of Arm Trajectories

Ghez, Claude; Gordon, James; Ghilardi, M. F.; Christakos, Constantinos N.; Cooper, Scott E.

doi:10.1101/sqb.1990.055.01.079

Cited by 315 publications

(128 citation statements)

References 14 publications

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“…The broad implication of such results is that the inertial properties of the limbs have proprioceptive consequences, an implication in concert with the findings ofGhez, Gordon, and colleagues on the guidance of limb movements (Ghez, Gordon, Ghilardi, Christakos, & Cooper, 1990;Ghez, Gordon, Ghilardi, & Sainburg, 1995;Gordon, Ghilardi, & Ghez, 1994) and with the "mobility tensor" perspective on multijoint posture and movement control (Hogan, 1985;Mussa-Ivaldi, Hogan, & Bizzi, 1985). A particularly significant feature of the present results in respect to the eigenvectors hypothesis is that the effect of {ek } was independent of other manipulations of potential relevance to proprioception.…”

Section: Discussionmentioning

confidence: 67%

Spatial and physical frames of reference in positioning a limb

Garrett

Pagano

Austin

et al. 1998

Perception & Psychophysics

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Section: Discussionmentioning

confidence: 67%

Spatial and physical frames of reference in positioning a limb

Garrett

Pagano

Austin

et al. 1998

Perception & Psychophysics

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“…The imposition of a visuomotor rotation causes a mismatch in the alignment of the visuallyand proprioceptively-perceived hand positions. Learning, in this case, is a gradual process involving the formation of new visuomotor maps and of new internal models [6,8,10,11]. Classic studies of motor and procedural learning predict that, initially, subjects should adapt to the imposed perturbation at the expense of a large increase in variability.…”

Section: Discussionmentioning

confidence: 99%

Sleep improves the variability of motor performance

Hill

Tononi

Ghilardi³

2008

Brain Research Bulletin

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Sleep after learning often enhances task performance, but the underlying mechanisms remain unclear. Using a well-characterized rotation learning paradigm implemented both behaviorally and in computer simulations, we compared two main hypotheses: the first, that off-line replay during sleep leads to further potentiation of synaptic circuits involved in learning; the second, that sleep enhances performance by uniformly downscaling synaptic strength. A simple computer model implemented synaptic changes associated with rotation adaptation (30°), yielding a reduction in mean directional error. Simulating further synaptic potentiation led to a further reduction of mean directional error, but not of directional variability. By contrast, simulating sleep-dependent synaptic renormalization by scaling down all synaptic weights by 15% decreased both mean directional error and variability. Two groups of subjects were tested after either two rotation adaptation training sessions or after a single training session followed by sleep. After two training sessions, mean direction error decreased, but directional variability remained high. However, subjects who slept after a single training session showed a reduction in both directional error and variability, consistent with a downscaling mechanism during sleep.

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“…This has been demonstrated in human movements where cases of large fiber neuropathy illustrate how loss of afferent nerve fibers from the limbs greatly impairs multi-joint arm behaviors (e.g. Sanes et al, 1985;Ghez et al, 1990;Gordon et al, 1995;Sainburg et al, 1995), with a significant decrease in movement coordination and accuracy. Functioning with such loss requires other sensory modalities, particularly vision, to provide compensatory information on limb posture and movement (e.g.…”

Section: Introductionmentioning

confidence: 99%

Fin ray sensation participates in the generation of normal fin movement in the hovering behavior of the bluegill sunfish (Lepomis macrochirus).

Williams

Hale

2015

Journal of Experimental Biology

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For many fish species, the pectoral fins serve as important propulsors and stabilizers and are precisely controlled. Although it has been shown that mechanosensory feedback from the fin ray afferent nerves provides information on ray bending and position, the effects of this feedback on fin movement are not known. In other taxa, including insects and mammals, sensory feedback from the limbs has been shown to be important for control of limb-based behaviors and we hypothesized that this is also the case for the fishes. In this study, we examined the impact of the loss of sensory feedback from the pectoral fins on movement kinematics during hover behavior. Research was performed with bluegill sunfish (Lepomis macrochirus), a model for understanding the biomechanics of swimming and for bio-inspired design of engineered fins. The bluegill beats its pectoral fins rhythmically, and in coordination with pelvic and median fin movement, to maintain a stationary position while hovering. Bilateral deafferentation of the fin rays results in a splay-finned posture where fins beat regularly but at a higher frequency and without adducting fully against the side of the body. For unilateral transections, more irregular changes in fin movements were recorded. These data indicate that sensory feedback from the fin rays and membrane is important for generating normal hover movements but is not necessary for generating rhythmic fin movement.

show abstract

Roles of Proprioceptive Input in the Programming of Arm Trajectories

Cited by 315 publications

References 14 publications

Spatial and physical frames of reference in positioning a limb

Spatial and physical frames of reference in positioning a limb

Sleep improves the variability of motor performance

Fin ray sensation participates in the generation of normal fin movement in the hovering behavior of the bluegill sunfish (Lepomis macrochirus).

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