A Biologically Motivated Classifier that Preserves Implicit Relationship Information in Layered Networks

A fundamental problem with layered neural networks is the loss of information about the relationships among features in the input space and relationships inferred by higher order classifiers. Information about these relationships is required to solve problems such as discrimination of simultaneously presented objects and discrimination of feature components. We propose a biologically motivated model for a classifier that preserves this information. When composed into classification networks, we show that the classifier propagates and aggregates information about feature relationships. We discuss how the model should be capable of segregating this information for the purpose of object discrimination and aggregating multiple feature components for the purpose of feature component discrimination.