Deep Adaptive Resonance Theory for learning biologically inspired episodic memory

Abstract

Biologically inspired episodic memory is able to store time sequential events, and to recall all of them from partial information. Because of the advantages of episodic memory, the biological concepts of episodic memory have been utilized to many applications. In this research, we propose a new memory model, called Deep ART (Adaptive Resonance Theory), to make a robust memory system for learning episodic memory. Deep ART has an attribute field in the bottom layer, which is newly designed to get semantic information of inputs. After encoding all inputs with their features, events are categorized in the event field using specified inputs. Since an episode is made of a temporal sequence of events, Deep ART makes event sequences with proposed sequence encoding and decoding processes. They can encode any temporal sequence of events, even if there are duplicated events in the episode. Moreover, based on the result of the analysis of retrieval error, Deep ART does not use the complement coding for partial inputs to enhance the accuracy of episode retrieval from partial cues. The simulation results demonstrate the effectiveness of Deep ART as the long term memory.