Investigating the variation of orientation tuning in a computational model of the visual cortex

The visual cortex contains well-ordered maps for visual features such as orientation. Existing computational approaches have demonstrated that biologically realistic global organization can be obtained. However, specific local properties such as variability in orientation tuning have not been thoroughly investigated. The purpose of this paper is to provide a computationally grounded interpretation of the evolution of topographic properties of cortical maps. We use a modified Kohonen approach to self-organization, and show that the orientation tuning of cortical units varies such that orientation selectivity is weaker at pinwheel centers than linear zones. This finding impacts a debate in neuroscience, where researchers are trying to resolve whether or not orientation tuning is sharp everywhere. We also examined a null-hypothesis, which modified the behavior of cortical units so that they are free to choose their orientation preference, but keep their orientation tuning constant. Our approach offers a mechanistic interpretation for how local learning algorithms can shape global organization. Our results show that a spatially differentiated orientation tuning map is intrinsic to a spatial learning process. Furthermore, constraining the orientation tuning of cortical units to be constant results in unacceptable map formation.