Title :
Temporal association in symmetric neural networks
Author :
Hiroike, Atsushi ; Omori, Takashi
Author_Institution :
Tokyo Univ. of Agric. & Technol., Japan
Abstract :
The authors study temporal association in a stochastic neural network model with symmetric full-connections. A symmetric system is accessible to analysis because of the existence of free-energy. The properties of the model are analytically described by critical temperature of transition between states. The result of the analysis is consistent with Monte Carlo simulations
Keywords :
neural nets; Monte Carlo simulations; critical temperature; state transition; stochastic neural network model; symmetric neural networks; temporal association; Agriculture; Hopfield neural networks; Intelligent networks; Limit-cycles; Neural networks; Neurons; Psychology; Stochastic processes; Temperature distribution;
Conference_Titel :
Neural Networks, 1991. 1991 IEEE International Joint Conference on
Print_ISBN :
0-7803-0227-3
DOI :
10.1109/IJCNN.1991.170711
