Title :
Dynamical memories based on inter-module Hebbian correspondences with the chaotic neural network modules
Author_Institution :
Grad. Sch. of Inf., Kyoto Univ., Japan
Abstract :
Each memory process of the neural network is not always divisible. These memories represent by the interact with one another, supposing it is represented by nonlinear dynamics. In this article, the interacting memory process is studied in our two-module chaotic neural network model with the Hebbian learning. Internal representation of the chaotic model is classified as two types of dynamics in ordered periodic “I know” state or high-dimensional chaotic “I don´t know” state. It is found that the novel periodic “I know” state is autonomously generated in the Hebbian learning process. Moreover, the inter-module coupling against the learned Hebbian correspondences also gives a novel “I know” state. These results suggest the existence of novel memories or functions generated by the interaction in the neural networks or the brain
Keywords :
Hebbian learning; chaos; content-addressable storage; neural nets; Hebbian learning; chaotic neural network; inter-module coupling; memory process; nonlinear dynamics; Artificial neural networks; Biological neural networks; Chaos; Hebbian theory; Hopfield neural networks; Informatics; Joining processes; Neural networks; Neurons;
Conference_Titel :
Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on
Conference_Location :
Tokyo
Print_ISBN :
0-7803-5731-0
DOI :
10.1109/ICSMC.1999.815632