| Abstract: | Background It has been shown that the classical receptive fields of simple and complex cells in the primary visual cortex emerge from
the statistical properties of natural images by forcing the cell responses to be maximally sparse or independent. We investigate
how to learn features beyond the primary visual cortex from the statistical properties of modelled complex-cell outputs. In
previous work, we showed that a new model, non-negative sparse coding, led to the emergence of features which code for contours
of a given spatial frequency band. |
