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Reprogramming movements: extraction of motor intentions from cortical ensemble activity when movement goals change

Overview of attention for article published in Frontiers in Neuroengineering, July 2012
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)

Mentioned by

1 news outlet
1 tweeter

Readers on

62 Mendeley
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Reprogramming movements: extraction of motor intentions from cortical ensemble activity when movement goals change
Published in
Frontiers in Neuroengineering, July 2012
DOI 10.3389/fneng.2012.00016
Pubmed ID

Peter J. Ifft, Mikhail A. Lebedev, Miguel A. L. Nicolelis, Ifft PJ, Lebedev MA, Nicolelis MA, Ifft, Peter James, Lebedev, Mikhail A, Nicolelis, Miguel A.L


The ability to inhibit unwanted movements and change motor plans is essential for behaviors of advanced organisms. The neural mechanisms by which the primate motor system rejects undesired actions have received much attention during the last decade, but it is not well understood how this neural function could be utilized to improve the efficiency of brain-machine interfaces (BMIs). Here we employed linear discriminant analysis (LDA) and a Wiener filter to extract motor plan transitions from the activity of ensembles of sensorimotor cortex neurons. Two rhesus monkeys, chronically implanted with multielectrode arrays in primary motor (M1) and primary sensory (S1) cortices, were overtrained to produce reaching movements with a joystick toward visual targets upon their presentation. Then, the behavioral task was modified to include a distracting target that flashed for 50, 150, or 250 ms (25% of trials each) followed by the true target that appeared at a different screen location. In the remaining 25% of trials, the initial target stayed on the screen and was the target to be approached. M1 and S1 neuronal activity represented both the true and distracting targets, even for the shortest duration of the distracting event. This dual representation persisted both when the monkey initiated movements toward the distracting target and then made corrections and when they moved directly toward the second, true target. The Wiener filter effectively decoded the location of the true target, whereas the LDA classifier extracted the location of both targets from ensembles of 50-250 neurons. Based on these results, we suggest developing real-time BMIs that inhibit unwanted movements represented by brain activity while enacting the desired motor outcome concomitantly.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Netherlands 1 2%
Germany 1 2%
Brazil 1 2%
Unknown 57 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 44%
Researcher 7 11%
Student > Doctoral Student 6 10%
Professor 5 8%
Student > Master 5 8%
Other 8 13%
Unknown 4 6%
Readers by discipline Count As %
Engineering 17 27%
Neuroscience 8 13%
Agricultural and Biological Sciences 8 13%
Medicine and Dentistry 7 11%
Psychology 6 10%
Other 7 11%
Unknown 9 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 15 August 2016.
All research outputs
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Outputs from Frontiers in Neuroengineering
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Outputs of similar age
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Outputs of similar age from Frontiers in Neuroengineering
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Altmetric has tracked 11,409,579 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 85 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one has done well, scoring higher than 85% 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 127,029 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them