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Editorial: Brain-inspired Hyperdimensional Computing: Algorithms, models, and architectures

Overview of attention for article published in Frontiers in Neuroscience, December 2022
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1 X user

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2 Mendeley
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Title
Editorial: Brain-inspired Hyperdimensional Computing: Algorithms, models, and architectures
Published in
Frontiers in Neuroscience, December 2022
DOI 10.3389/fnins.2022.1102568
Pubmed ID
Authors

Xun Jiao, Abbas Rahimi, Cornelia Fermüller, John Yiannis Aloimonos

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 1 50%
Researcher 1 50%
Readers by discipline Count As %
Computer Science 1 50%
Engineering 1 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 22 December 2022.
All research outputs
#22,778,604
of 25,392,582 outputs
Outputs from Frontiers in Neuroscience
#10,139
of 11,543 outputs
Outputs of similar age
#411,686
of 482,256 outputs
Outputs of similar age from Frontiers in Neuroscience
#319
of 414 outputs
Altmetric has tracked 25,392,582 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,543 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 482,256 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 414 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.