Title :
Episodic associative memory
Author :
Hattori, Motonobu ; Hagiwara, Masafumi ; Nakagawa, Masao
Author_Institution :
Dept. of Electr. Eng., Keio Univ., Yokohama, Japan
fDate :
27 Jun-2 Jul 1994
Abstract :
Episodic associative memory (EAM) is introduced and simulated. It uses quick learning for bidirectional associative memory (QLBAM) and pseudo-noise (PN) sequences. The features of the proposed EAM are: it can memorize and recall episodic associations; it can store plural episodes; it has high memory capacity
Keywords :
Hebbian learning; content-addressable storage; learning (artificial intelligence); neural nets; Hebbian learning; bidirectional associative memory; episodic associations; episodic associative memory; high memory capacity; neural networks; plural episodes; pseudo-noise sequences; quick learning; simulation; Associative memory; Biological neural networks; Hebbian theory; Humans; Interference; Magnesium compounds; Neurons; Problem-solving; Vectors;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374330
