Self-Organization of Feature Columns and its Application to Object Classification
We propose a computational model for the self-organization of feature columns based on a modular framework. The proposed model consists of several modules; each module is composed of a collection of Gaussian units. The combination of hierarchical competition within and between modules and a smoothness constraint nds out continuity among input patterns, and topographically maps these series of patterns into dierent modules. Computer simulations show an example of self-organization and object classication by combining the created feature columns.