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Coordinated Optimization of Visual Cortical Maps (I) Symmetry-based Analysis

Overview of attention for article published in PLoS Computational Biology, November 2012
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62 Mendeley
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Title
Coordinated Optimization of Visual Cortical Maps (I) Symmetry-based Analysis
Published in
PLoS Computational Biology, November 2012
DOI 10.1371/journal.pcbi.1002466
Pubmed ID
Authors

Lars Reichl, Dominik Heide, Siegrid Löwel, Justin C. Crowley, Matthias Kaschube, Fred Wolf

Abstract

In the primary visual cortex of primates and carnivores, functional architecture can be characterized by maps of various stimulus features such as orientation preference (OP), ocular dominance (OD), and spatial frequency. It is a long-standing question in theoretical neuroscience whether the observed maps should be interpreted as optima of a specific energy functional that summarizes the design principles of cortical functional architecture. A rigorous evaluation of this optimization hypothesis is particularly demanded by recent evidence that the functional architecture of orientation columns precisely follows species invariant quantitative laws. Because it would be desirable to infer the form of such an optimization principle from the biological data, the optimization approach to explain cortical functional architecture raises the following questions: i) What are the genuine ground states of candidate energy functionals and how can they be calculated with precision and rigor? ii) How do differences in candidate optimization principles impact on the predicted map structure and conversely what can be learned about a hypothetical underlying optimization principle from observations on map structure? iii) Is there a way to analyze the coordinated organization of cortical maps predicted by optimization principles in general? To answer these questions we developed a general dynamical systems approach to the combined optimization of visual cortical maps of OP and another scalar feature such as OD or spatial frequency preference. From basic symmetry assumptions we obtain a comprehensive phenomenological classification of possible inter-map coupling energies and examine representative examples. We show that each individual coupling energy leads to a different class of OP solutions with different correlations among the maps such that inferences about the optimization principle from map layout appear viable. We systematically assess whether quantitative laws resembling experimental observations can result from the coordinated optimization of orientation columns with other feature maps.

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Mendeley readers

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 %
Germany 1 2%
Switzerland 1 2%
Chile 1 2%
Italy 1 2%
Greece 1 2%
United States 1 2%
Unknown 56 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 24%
Student > Master 14 23%
Student > Ph. D. Student 7 11%
Student > Doctoral Student 3 5%
Professor > Associate Professor 3 5%
Other 9 15%
Unknown 11 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 23%
Neuroscience 9 15%
Physics and Astronomy 7 11%
Mathematics 4 6%
Computer Science 4 6%
Other 11 18%
Unknown 13 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 24 November 2012.
All research outputs
#15,229,642
of 25,461,852 outputs
Outputs from PLoS Computational Biology
#6,541
of 8,981 outputs
Outputs of similar age
#113,951
of 198,559 outputs
Outputs of similar age from PLoS Computational Biology
#59
of 110 outputs
Altmetric has tracked 25,461,852 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,981 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 25th percentile – i.e., 25% 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 198,559 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 110 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.