In this paper, we propose Episodic Associative Memories (EAMs). They use Quick Learning for Bidirectional Associative Memory (QLBAM), which enables high memory capacity, and Pseudo-Noise (PN) sequences. In the learning of the proposed EAMs, PN sequences are used as one side of training pairs. To store an episode, a scene of the episode is stored with a PN sequence, and the next scene is stored with the PN sequence which is shifted with one bit. Such a procedure enables episodic memory. The proposed EAMs can recall episodic associations by shifting the PN sequence one by one. The features of the proposed EAMs are: (1) they can memorize and recall episodic associations; (2) they can store plural episodes; (3) they have high memory capacity; (4) they are robust for noisy and incomplete inputs.
ASJC Scopus subject areas
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence