Self-organizing map simulations confirm similarity of spatial correlation structure in natural images and cortical representations

Abstract

The goal of this paper is to extend the ecological theory of perception to the larger scale spatial organization of cortical maps. This leads to the hypothesis that cortical organization of responses to visual features reflects the environmental organization of these same features. In our previous work we have shown that the spatial statistics of natural images can be characterized by a slowly decaying, or low frequency correlational structure for color, and a rapidly decaying, or high-frequency structure for orientation features. A similar contrasting behavior of spatial statistics for color and orientation was measured in parallel in the cortical response of macaque visual cortex. In order to explore whether this parallel is meaningful, we performed a cortical simulation using an adaptation of Kohonen's self-organizing map algorithm. The simulated cortex responds to both low-frequency and high-frequency input visual features, and learns to represent these features through weight modification. We demonstrate that the learnt cortical weights show the same spatial correlation structure that is observed both in natural image statistics and the measured cortical responses. © 2009 Springer Berlin Heidelberg.