K. Schulten Beckman -Insti t u te University of Illinois Urbana, IL 61801
Feature selective cells in the primary visual cortex of several species are or(cid:173) ganized in hierarchical topographic maps of stimulus features like "position in visual space", "orientation" and" ocular dominance". In order to un(cid:173) derstand and describe their spatial structure and their development, we in(cid:173) vestigate a self-organizing neural network model based on the feature map algorithm. The model explains map formation as a dimension-reducing mapping from a high-dimensional feature space onto a two-dimensional lattice, such that "similarity" between features (or feature combinations) is translated into "spatial proximity" between the corresponding feature selective cells. The model is able to reproduce several aspects of the spatial structure of cortical maps in the visual cortex.