Detecting sequences and understanding language with neural associative memories and cell assemblies

Abstract

Using associative memories and sparse distributed representations we have developed a system that can learn to associate words with objects, properties like colors, and actions. This system is used in a robotics context to enable a robot to respond to spoken commands like "bot show plum" or "bot put apple to yellow cup". This involves parsing and understanding of simple sentences and "symbol grounding", for example, relating the nouns to concrete objects sensed by the camera and recognized by a neural network from the visual input. © 2005 Springer-Verlag Berlin/Heidelberg.