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The minimum transition hypothesis for intermittent hierarchical motor control

Overview of attention for article published in Frontiers in Computational Neuroscience, January 2013
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  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (74th percentile)

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Title
The minimum transition hypothesis for intermittent hierarchical motor control
Published in
Frontiers in Computational Neuroscience, January 2013
DOI 10.3389/fncom.2013.00012
Pubmed ID
Authors

Amir Karniel

Abstract

In intermittent control, instead of continuously calculating the control signal, the controller occasionally changes this signal at certain sparse points in time. The control law may include feedback, adaptation, optimization, or any other control strategies. When, where, and how does the brain employ intermittency as it controls movement? These are open questions in motor neuroscience. Evidence for intermittency in human motor control has been repeatedly observed in the neural control of movement literature. Moreover, some researchers have provided theoretical models to address intermittency. Even so, the vast majority of current models, and I would dare to say the dogma in most of the current motor neuroscience literature involves continuous control. In this paper, I focus on an area in which intermittent control has not yet been thoroughly considered, the structure of muscle synergies. A synergy in the muscle space is a group of muscles activated together by a single neural command. Under the assumption that the motor control is intermittent, I present the minimum transition hypothesis (MTH) and its predictions with regards to the structure of muscle synergies. The MTH asserts that the purpose of synergies is to minimize the effort of the higher level in the hierarchy by minimizing the number of transitions in an intermittent control signal. The implications of the MTH are not only for the structure of the muscle synergies but also to the intermittent and hierarchical nature of the motor system, with various predictions as to the process of skill learning, and important implications to the design of brain machine interfaces and human robot interaction.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 4%
Germany 1 2%
Switzerland 1 2%
Norway 1 2%
France 1 2%
United Kingdom 1 2%
Brazil 1 2%
Unknown 47 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 22%
Researcher 12 22%
Student > Master 5 9%
Professor 5 9%
Student > Bachelor 5 9%
Other 11 20%
Unknown 5 9%
Readers by discipline Count As %
Neuroscience 15 27%
Engineering 11 20%
Psychology 9 16%
Agricultural and Biological Sciences 6 11%
Sports and Recreations 2 4%
Other 5 9%
Unknown 7 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 July 2020.
All research outputs
#7,181,643
of 22,699,621 outputs
Outputs from Frontiers in Computational Neuroscience
#395
of 1,336 outputs
Outputs of similar age
#80,245
of 280,695 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#31
of 131 outputs
Altmetric has tracked 22,699,621 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 1,336 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one has gotten more attention than average, scoring higher than 69% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 280,695 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 131 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.