Deciphering dark and bright
2015-05-28
(Press-News.org) The human sensory systems contend with enormous diversity in the natural world. But it has been known for a long time the brain is adapted to exploit statistical regularities that nonetheless arise amongst this diversity. Research publishing this week in PLOS Computational Biology reports that established statistical distributions of visual features, such as visual contrast, spatial scale and depth, differ between dark and bright components of the natural world.
For scientists Emily Cooper and Anthony Norcia, gaining a more detailed description of statistical regularities in the natural world leads to a further understanding of how the nervous system has adapted to its environment.
Distinguishing between dark and bright visual features is a key computation of early visual pathways. However, the authors demonstrate that the dark and bright visual patterns driving cortical networks are asymmetric, producing predictable differences that may be exploited by the brain.
The authors expect that this dichotomy in dark and bright plays a key role in the generation of cortical and perceptual asymmetries.
"Our sensory systems are so tied to the demands of our environment that looking outwards to study world around us can often provide insights into what might be going on inside the brain," says Emily Cooper.
INFORMATION:
All works published in PLOS Computational Biology are Open Access, which means that all content is immediately and freely available. Use this URL in your coverage to provide readers access to the paper upon publication: http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004268
Press-only preview: https://www.plos.org/wp-content/uploads/2015/05/pcbi.1004268-Cooper.pdf
Contact:
Emily A. Cooper
Address: Dartmouth College
Dept. of Psychological and Brain Sciences
6207 Moore Hall
Hanover, NH 03755
UNITED STATES
Phone: 773-655-3896
Email: emily.a.cooper@dartmouth.edu
Citation: Cooper EA, Norcia AM (2015) Predicting Cortical Dark/Bright Asymmetries from Natural Image Statistics and Early Visual Transforms. PLoS Comput Biol 11(5): e1004268. doi:10.1371/journal.pcbi.1004268
Funding: This work was supported by National Institutes of Health Grant 5R01EY018875-05 (AMN) and a research contract between Sony Corporation and Stanford University (AMN). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
About PLOS Computational Biology
PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales through the application of computational methods. All works published in PLOS Computational Biology are Open Access. All content is immediately available and subject only to the condition that the original authorship and source are properly attributed. Copyright is retained. For more information follow @PLOSCompBiol on Twitter or contact ploscompbiol@plos.org.
About PLOS
PLOS is a nonprofit publisher and advocacy organization founded to accelerate progress in science and medicine by leading a transformation in research communication. For more information, visit http://www.plos.org.
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