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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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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

Mendeley readers

The data shown below were compiled from readership statistics for 196 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 11 6%
United States 11 6%
France 2 1%
United Kingdom 2 1%
Australia 2 1%
Greece 1 <1%
China 1 <1%
Unknown 166 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 61 31%
Researcher 48 24%
Student > Master 14 7%
Student > Bachelor 12 6%
Student > Postgraduate 10 5%
Other 34 17%
Unknown 17 9%
Readers by discipline Count As %
Computer Science 55 28%
Agricultural and Biological Sciences 41 21%
Neuroscience 28 14%
Engineering 23 12%
Psychology 13 7%
Other 19 10%
Unknown 17 9%
Attention Score in Context

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 23 August 2017.
All research outputs
#7,453,479
of 22,786,691 outputs
Outputs from Journal of Computational Neuroscience
#68
of 307 outputs
Outputs of similar age
#23,188
of 66,312 outputs
Outputs of similar age from Journal of Computational Neuroscience
#1
of 1 outputs
Altmetric has tracked 22,786,691 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 307 research outputs from this source. They receive a mean Attention Score of 3.5. This one has gotten more attention than average, scoring higher than 60% 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 66,312 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
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