On the utility of sparse neural representations in adaptive behaving agents

A number of unsupervised learning algorithms seeking to account for the receptive field properties of simple cells in the mammalian primary visual cortex have been proposed. Among these are principal component analysis and sparse coding. While it appears that the receptive field properties learned by sparse coding match those measured in cortical cells better than those learned by principal component analysis, it is still not clear why biological neural systems might prefer to use sparse codes. In this paper we explore another reason why sparse representations might be preferred over principal component analysis by studying the utility of different coding schemes in an adaptive behaving agent. We suggest that the qualitative properties of representations based on sparse coding are more stable in the presence of changes in the input statistics than those of representations based on principal component analysis. We demonstrate this by examining representations learned on binocular visual input with different disparity distributions. Our results show that in encoding retinal disparity, the properties of sparse codes are more stable, and that this has important implications in adaptive agents, where the statistics change over time. In particular, in an agent who jointly learns a representation for binocular visual inputs along with a vergence control policy, the learned behavior is unstable when actions are driven by PCA based representations, but stable and self-calibrating when driven by sparse coding based representations.