A Cortex-Inspired Neural-Symbolic Network for Knowledge Representation
نویسندگان
چکیده
Semantic systems for the representation of declarative knowledge are usually unconnected to neuro-biological 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.
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