A neural network simulating human reach–grasp coordination by continuous updating of vector positioning commands

Ulloa, Antonio; Bullock, Daniel

doi:10.1016/s0893-6080(03)00079-0

Cited by 41 publications

(22 citation statements)

References 48 publications

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“…We propose that the differences between the Davare et al study and the studies conducted within our lab can be accounted for by the timing of the TMS pulses and the task employed. We recently showed that TMS to left aIPS disrupts grasping, but only when applied simultaneous with hand movement execution (not during the planning phase of the movement) (Rice et al, 2006), potentially reflecting a role of aIPS in the computation of a difference vector (Ulloa and Bullock 2003). In the Davare et al study they applied the TMS pulses between 0 and 200ms after the go signal, and reported an average reaction time (i.e.…”

Section: Discussionmentioning

confidence: 99%

On-line grasp control is mediated by the contralateral hemisphere

et al. 2007

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Electrophysiological recordings from monkeys, as well as functional imaging and neuropsychological work with humans have suggested that a region in the anterior portion of the intraparietal sulcus (aIPS) is involved in prehensile movements. With recent methodological advances using transcranial magnetic stimulation (TMS), we can now causally attribute anatomy with function to more precisely determine the specific involvement of aIPS in grasping. It has recently been demonstrated that aIPS is specifically involved in executing a grasp under conditions of both constant target requirements, as well as in correcting a movement under conditions in which a target perturbation occurs. In the present study we extend these findings by determining the differential contribution of the left and right hemisphere to executing a grasping movement with the left and right hands. Transient disruption of left aIPS at movement onset impairs grasping with the right but not the left hand, and disruption of right aIPS impairs grasping with the left but not the right hand. We conclude that grasping is a lateralized process, relying exclusively on the contralateral hemisphere, and discuss the implications of these findings in relationship to models of hemispheric dominance for motor control.

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Section: Discussionmentioning

confidence: 99%

On-line grasp control is mediated by the contralateral hemisphere

et al. 2007

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“…Attempts at quantifying this process in a way that may be usable for robot control are few. Glke et al [36], Bae and Armstrong [37] showed that the finger motion during reach to grasp tasks could be described by a simple polynomial function of time, while Ulloa and Bullock [38] modeled the covariation of the arm and the finger motion. Interestingly, these authors also report on an involuntary reopening of the fingers upon perturbation of the target location; an observation which we will revisit in this paper.…”

Section: Biological Evidencesmentioning

confidence: 99%

Coupled dynamical system based arm–hand grasping model for learning fast adaptation strategies

Shukla

Billard

2012

Robotics and Autonomous Systems

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“…Thus neither the component durations nor the maximum apetture are known in advance. Recent VITE-based models of Ulloa and colleagues 50 • 51 show how reach-grasp coordination can be achieved without the advance knowledge and internal accounting of durations assumed in the Hoff-Arbib model. Key timing data trends 52 • 53 emerge dynamically.…”

Section: Coordination Of Rates and Completion Times In Voluntary Actionmentioning

confidence: 99%

Isoperimetric Partitioning: A New Algorithm for Graph Partitioning

Grady¹,

Schwartz²

2006

SIAM J. Sci. Comput.

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Aclmowlcdgcments: Preparation of this article was partially supported by NIMH RO I DC02852. Neural models of queuing and timingTeaser Sentence: Fifty years after Lashley inferred parallel cerebral representations of serial plans, and twenty-five years after Grossberg proposed a parallel competitive queuing model, confinnatmy neurophysiological data arrive -but other new data cast doubt on network-delay models of cerebellar adaptive timing.Abstract. Temporal structure in skilled, fluent action exists at several nested levels. At the largest scale considered here, short sequences of actions that are planned collectively in prefrontal cortex appear to be queued for performance by a cyclic competitive process that operates in concert with a parallel analog representation that implicitly specifies the relative priority of elements of the sequence. At an intermediate scale, single acts, like reaching to grasp, depend on coordinated scaling of the rates at which many muscles shorten or lengthen in parallel. To ensure success of acts such as catching an approaching ball, such parallel rate scaling, which appears to be one function of the basal ganglia, must be coupled to perceptual variables, such as time-to-contact. At a finer scale, within each act, desired rate scaling can be realized only if precisely timed muscle activations first accelerate and then decelerate the limbs, to ensure that muscle length changes do not under-or over-shoot the amounts needed for precise acts. Each context of action may require a much different timed muscle activation pattern than similar contexts. Because context differences that require different treatment cannot be known in advance, a formidable adaptive engine -the cerebellum -is needed to amplify differences within, and continuously search, a vast parallel signal flow, in order to discover contextual "leading indicators" of when to generate distinctive parallel patterns of analog signals. From some parts of the cerebellum, such signals control muscles. But a recent model shows how the lateral cerebellum may serve the competitive queuing system (in frontal cortex) as a repository of quickly accessed long-term sequence memories. Thus different parts of the cerebellum may use the same adaptive engine design to serve the lowest and the highest of the three levels of temporal structure treated. If so, no one-to-one mapping exists between levels of temporal structure and major parts of the brain. Finally, recent data cast doubt on network-delay models of cerebellar adaptive timing.

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A neural network simulating human reach–grasp coordination by continuous updating of vector positioning commands

Cited by 41 publications

References 48 publications

On-line grasp control is mediated by the contralateral hemisphere

On-line grasp control is mediated by the contralateral hemisphere

Coupled dynamical system based arm–hand grasping model for learning fast adaptation strategies

Isoperimetric Partitioning: A New Algorithm for Graph Partitioning

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