Enhancing Episodic Associative Memory Models for Practical Use

Associative memory models have been developed as mathematical and cognitive models to represent human memory. The mathematical models have been successfully applied in classification and pattern recognition tasks. But there is a practical value in the cognitive representation of it which has given rise to associative memory models known as episodic associative memory (EAM). To date, the practical application of EAMs is less common than the mathematical models mainly due to representation deficiencies in EAMs. This paper proposes a novel approach known as episodic associative memory with a neighborhood effect (EAMwNE), which overcomes these representative limitations and increases the practical value of EAMs. EAMwNE utilises the lateral relationships among feature values, considering a neighborhood in achieving this. Consequently, each data pattern is represented by a number of memory traces (opposed to the single representation in early models) depending on the size of neighborhood. In this paper, EAMwNE is experimented with classification tasks to demonstrate the enhanced capabilities of it. The accuracy of EAMwNE is further enhanced by a ´reward´ mechanism.