Learning Horizontal Connections from the Statistics of Natural Images

A common assumption in neuroscience is that the visual system is adapted to the statistics of natural images [1]. Hence, by building probabilistic models of natural images, we can gain insight into how visual information is represented and processed in the visual cortex. For example, it has been shown that learning a sparse code for images predicts the shapes of simple cell receptive fields in V1 [4].