Learning to adapt: Cognitive architecture based on biologically inspired memory

This paper describes a cognitive architecture that is predominantly substantiated by data processing principles of biologically inspired multiple memory systems. The current memory configuration involves episodic, semantic, procedural and working memory units. We introduce the reader to essentials of the mammalian memory system and explain our highly abstracted software realization of it as a distributed, concurrently communicating node structure. We also show our first results of its implementation for cognitive function as novelty detection and exploration of a maze in the case of simulated autonomous mobile robot.