Feature Coding with a Statistically Independent Cortical Representation.

Current models of primary visual cortex (V1) include a linear filtering stage followed by a gain control mechanism that explains some of the nonlinear behavior of neurons. The nonlinear stage has been modeled as a divisive normalization in which each input linear response is squared and then divided by a weighted sum of squared linear responses in a certain neighborhood. In this communication, we show that such a scheme permits an efficient coding of natural image features. In our case, the linear stage is implemented as a four-level Daubechies decomposition, and the nonlinear normalization parameters are determined from the statistics of natural images under the hypothesis that sensory systems are adapted to signals to which they are exposed. In particular, we fix the weights of the divisive normalization to the mutual information of the corresponding pair of linear coefficients. This nonlinear process extracts significant, statistically independent, visual events in the image. [ABSTRACT FROM AUTHOR]