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Sparse Coding Can Predict Primary Visual Cortex Receptive Field Changes Induced by Abnormal Visual Input

Overview of attention for article published in PLoS Computational Biology, May 2013
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Title
Sparse Coding Can Predict Primary Visual Cortex Receptive Field Changes Induced by Abnormal Visual Input
Published in
PLoS Computational Biology, May 2013
DOI 10.1371/journal.pcbi.1003005
Pubmed ID
Authors

Jonathan J. Hunt, Peter Dayan, Geoffrey J. Goodhill

Abstract

Receptive fields acquired through unsupervised learning of sparse representations of natural scenes have similar properties to primary visual cortex (V1) simple cell receptive fields. However, what drives in vivo development of receptive fields remains controversial. The strongest evidence for the importance of sensory experience in visual development comes from receptive field changes in animals reared with abnormal visual input. However, most sparse coding accounts have considered only normal visual input and the development of monocular receptive fields. Here, we applied three sparse coding models to binocular receptive field development across six abnormal rearing conditions. In every condition, the changes in receptive field properties previously observed experimentally were matched to a similar and highly faithful degree by all the models, suggesting that early sensory development can indeed be understood in terms of an impetus towards sparsity. As previously predicted in the literature, we found that asymmetries in inter-ocular correlation across orientations lead to orientation-specific binocular receptive fields. Finally we used our models to design a novel stimulus that, if present during rearing, is predicted by the sparsity principle to lead robustly to radically abnormal receptive fields.

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Geographical breakdown

Country Count As %
Germany 3 3%
France 3 3%
United States 2 2%
Australia 1 <1%
Belarus 1 <1%
United Kingdom 1 <1%
Greece 1 <1%
Belgium 1 <1%
Unknown 102 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 33 29%
Researcher 27 23%
Student > Master 16 14%
Student > Bachelor 7 6%
Student > Postgraduate 5 4%
Other 17 15%
Unknown 10 9%
Readers by discipline Count As %
Neuroscience 22 19%
Agricultural and Biological Sciences 22 19%
Computer Science 16 14%
Psychology 10 9%
Engineering 10 9%
Other 23 20%
Unknown 12 10%