Learning independent causes in natural images explains the spacevariant oblique effect

Abstract

The efficient coding hypothesis posits that sensory processing increases independence between neural responses to natural stimuli by removing their statistical redundancy reflective of the structure present in the natural environment. While there is consensus on the role of the statistical structure of the physical environment in shaping the natural input to the sensory system, it is not well understood how the sensory apparatus itself and its active use during behavior determine the statistics of the input. To explore this issue, a virtual human agent is simulated navigating through a wooded environment under full control of its gaze allocation during walking. Independent causes for the images obtained during navigation are learned with algorithms that have been shown to extract computationally useful representations similar to those encountered in the primary visual cortex of the mammalian brain. The distributions of properties of the learned simple cell like units are in good agreement with a wealth of data on the visual system including the oblique effect, the meridional effect, properties of neurons in the macaque visual cortex, and functional Magnetic Resonance Imaging (fMRI) data on orientation selectivity in humans and monkeys. Finally, this analysis sheds new light on the discussion on orientation anisotropies based on carpented environments. Thus, when learning computational representations it is not sufficient to consider only the regularities of the environment but also the regularities imposed by the sensory apparatus and its use during behavior need to be taken into account.