Modeling Receptive Fields with Non-Negative Sparse Coding

An important approach in visual neuroscience considers how the processing of the early visual system is dependent on the statistics of the natural environment. A particularly influential model in this respect has been sparse coding. In this paper we argue for a non-negative variant of the model. This is based partly on neurophysiological grounds and partly on the intuitive understanding of parts-based representations. We discuss the logic behind our reasoning and show experiments on natural images demonstrating the usefulness of the new model