Second-order statistics of natural images

Assuming adaptation of the visual cortex to its environment, we analyse the invariance found in natural images to explain the selective response of visual cortical neurons. We argue that the invariant structure of images can be formally expressed by dot-products. Utilizing the specific structure of the proposed model we show how non-linear functions can be learned efficiently from natural images. In addition to localized edge detectors we found model neurons that respond to changes in texture and cells that detect edge curvature. The analysis suggests new types of features neurons in primary visual cortex may be selective to.