A Cortex-Inspired Neural-Symbolic Network for Knowledge Representation

Semantic systems for the representation of declarative knowledge are usually unconnected to neurobiological mechanisms in the brain. In this paper we report on efforts to bridge this gap by proposing a neural-symbolic network based on processing principles of the cortical column. We show how a locally controlled activation spread on conceptual nodes leads to bottom-up and top-down processing streams which allow for feature inheritance, context effects and the generation of predictions.