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A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields

Overview of attention for article published in Journal of Computational Neuroscience, October 2006
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About this Attention Score

  • Among the highest-scoring outputs from this source (#33 of 211)
  • Good Attention Score compared to outputs of the same age (66th percentile)

Mentioned by

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1 Wikipedia page

Citations

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107 Dimensions

Readers on

mendeley
171 Mendeley
Title
A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields
Published in
Journal of Computational Neuroscience, October 2006
DOI 10.1007/s10827-006-0003-9
Pubmed ID
Authors

Martin Rehn, Friedrich T. Sommer

Abstract

Computational models of primary visual cortex have demonstrated that principles of efficient coding and neuronal sparseness can explain the emergence of neurones with localised oriented receptive fields. Yet, existing models have failed to predict the diverse shapes of receptive fields that occur in nature. The existing models used a particular "soft" form of sparseness that limits average neuronal activity. Here we study models of efficient coding in a broader context by comparing soft and "bard" forms of neuronal sparseness. As a result of our analyses, we propose a novel network model for visual cortex. The model forms efficient visual representations in which the number of active neurones, rather than mean neuronal activity, is limited. This form of hard sparseness also economises cortical resources like synaptic memory and metabolic energy. Furthermore, our model accurately predicts the distribution of receptive field shapes found in the primary visual cortex of cat and monkey.

Mendeley readers

The data shown below were compiled from readership statistics for 171 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 11 6%
Germany 10 6%
France 3 2%
United Kingdom 2 1%
Australia 2 1%
China 1 <1%
Netherlands 1 <1%
Greece 1 <1%
Unknown 140 82%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 56 33%
Researcher 43 25%
Student > Master 13 8%
Student > Postgraduate 10 6%
Professor > Associate Professor 9 5%
Other 39 23%
Unknown 1 <1%
Readers by discipline Count As %
Computer Science 50 29%
Agricultural and Biological Sciences 39 23%
Engineering 19 11%
Neuroscience 18 11%
Psychology 14 8%
Other 30 18%
Unknown 1 <1%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 05 March 2014.
All research outputs
#3,537,881
of 12,320,131 outputs
Outputs from Journal of Computational Neuroscience
#33
of 211 outputs
Outputs of similar age
#78,804
of 266,368 outputs
Outputs of similar age from Journal of Computational Neuroscience
#1
of 3 outputs
Altmetric has tracked 12,320,131 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 211 research outputs from this source. They receive a mean Attention Score of 2.5. This one has gotten more attention than average, scoring higher than 66% 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 266,368 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 66% of its contemporaries.
We're also able to compare this research output to 3 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