Title |
Temporal Adaptation Enhances Efficient Contrast Gain Control on Natural Images
|
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Published in |
PLoS Computational Biology, January 2013
|
DOI | 10.1371/journal.pcbi.1002889 |
Pubmed ID | |
Authors |
Fabian Sinz, Matthias Bethge |
Abstract |
Divisive normalization in primary visual cortex has been linked to adaptation to natural image statistics in accordance to Barlow's redundancy reduction hypothesis. Using recent advances in natural image modeling, we show that the previously studied static model of divisive normalization is rather inefficient in reducing local contrast correlations, but that a simple temporal contrast adaptation mechanism of the half-saturation constant can substantially increase its efficiency. Our findings reveal the experimentally observed temporal dynamics of divisive normalization to be critical for redundancy reduction. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Norway | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 7% |
United Kingdom | 2 | 3% |
Germany | 1 | 2% |
Switzerland | 1 | 2% |
Unknown | 53 | 87% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 19 | 31% |
Researcher | 18 | 30% |
Professor > Associate Professor | 6 | 10% |
Other | 4 | 7% |
Student > Bachelor | 2 | 3% |
Other | 8 | 13% |
Unknown | 4 | 7% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 20 | 33% |
Neuroscience | 9 | 15% |
Psychology | 9 | 15% |
Computer Science | 7 | 11% |
Engineering | 4 | 7% |
Other | 5 | 8% |
Unknown | 7 | 11% |