A Mathematical Theory of Communication
A Mathematical Theory of Communication
Modeling visual perception for image processing
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Correlation-distortion based identification of Linear-Nonlinear-Poisson models
Journal of Computational Neuroscience
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Contrast gain control is an important mechanism underlying the visual system's adaptation to contrast of luminance in varying visual environments. Our previous work showed that the threshold and saturation determine the preferred contrast sensitivity as well as the maximum information coding capacity of the neuronal model. In this report, we investigated the design principles underlying adaptation behavior in contrast gain control by an adaptive linear-nonlinear model. We found that an adaptive rescaling mechanism predicted by information transmission maximization can explain a variety of observed contrast gain control phenomena in neurophysiological experiments, including the divisive input-output relations, and the inverse power-law relation between response gain and input contrast. Our results suggest that contrast gain control in visual systems might be designed for information maximization.