Natural Image Statistics and Divisive Normalization: Modeling Nonlinearity and Adaptation in Cortical Neurons
Martin J Wainwright , Odelia Schwartz, and Eero P Simoncelli
Published in:
Statistical Theories of the Brain
Eds. R Rao, B Olshausen, and M Lewicki
© MIT Press, 2002.

--------------------------------------------------------------------------------
We have empirically examined the responses of multi-scale oriented basis functions to natural images, and found that these responses exhibit striking statistical dependencies, even when the basis functions are chosen to optimize independence (e.g., simoncelli97b, buccigrossi97). Such dependencies cannot be removed through further linear processing. Rather, a nonlinear form of cortical processing is required, in which the linear response of each basis function is rectified (and typically squared) and then divided by a weighted sum of the rectified responses of neighboring neurons. In earlier work, we have shown that this model, with all parameters determined from the statistics of a set of natural images, can account qualitatively for recent physiological observations of suppression of V1 responses by stimuli presented outside the classical receptive field (simoncelli98d). Here, we show that the model can account for responses to non-optimal stimuli. In addition, we show that adjusting the model parameters according to the statistics of recent visual input can account for physiologically observed adaptation effects.
--------------------------------------------------------------------------------
Full Text (498k, ps.gz) / Full Text (138k, pdf) / Back