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Sparse coding with memristor networks

Overview of attention for article published in Nature Nanotechnology, May 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

news
12 news outlets
blogs
4 blogs
twitter
32 tweeters
patent
2 patents

Citations

dimensions_citation
365 Dimensions

Readers on

mendeley
359 Mendeley
Title
Sparse coding with memristor networks
Published in
Nature Nanotechnology, May 2017
DOI 10.1038/nnano.2017.83
Pubmed ID
Authors

Patrick M. Sheridan, Fuxi Cai, Chao Du, Wen Ma, Zhengya Zhang, Wei D. Lu

Abstract

Sparse representation of information provides a powerful means to perform feature extraction on high-dimensional data and is of broad interest for applications in signal processing, computer vision, object recognition and neurobiology. Sparse coding is also believed to be a key mechanism by which biological neural systems can efficiently process a large amount of complex sensory data while consuming very little power. Here, we report the experimental implementation of sparse coding algorithms in a bio-inspired approach using a 32 × 32 crossbar array of analog memristors. This network enables efficient implementation of pattern matching and lateral neuron inhibition and allows input data to be sparsely encoded using neuron activities and stored dictionary elements. Different dictionary sets can be trained and stored in the same system, depending on the nature of the input signals. Using the sparse coding algorithm, we also perform natural image processing based on a learned dictionary.

Twitter Demographics

The data shown below were collected from the profiles of 32 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 359 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 <1%
United States 1 <1%
China 1 <1%
Unknown 356 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 110 31%
Researcher 56 16%
Student > Master 45 13%
Student > Bachelor 28 8%
Student > Postgraduate 11 3%
Other 30 8%
Unknown 79 22%
Readers by discipline Count As %
Engineering 126 35%
Materials Science 43 12%
Physics and Astronomy 43 12%
Computer Science 23 6%
Neuroscience 9 3%
Other 30 8%
Unknown 85 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 145. 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 31 December 2019.
All research outputs
#164,399
of 17,816,628 outputs
Outputs from Nature Nanotechnology
#146
of 3,005 outputs
Outputs of similar age
#4,820
of 277,644 outputs
Outputs of similar age from Nature Nanotechnology
#8
of 76 outputs
Altmetric has tracked 17,816,628 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,005 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 34.6. This one has done particularly well, scoring higher than 95% 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 277,644 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 98% of its contemporaries.
We're also able to compare this research output to 76 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.