@inproceedings{8f1392a6fe694ec899e615b784304f1b,
title = "The Refracted Bifurcating Neural Network",
abstract = "In this paper, a refracted bifurcating neural network (RBNN) is proposed for associative memory study, based on the understanding of refractory mechanism in neuron firing and a bifurcating neural network (BNN). The absolute refractory period is introduced into the individual neuron model of the BNN to construct RBNN with an attempt to improve the convergent speed of the original network. The results of this study suggest that introduction of refractory period into BNN can achieve comparable pattern recalling rates as those for the original network. By adjusting suitably the value of the absolute refractory period and the amplitude of relaxation level, RBNN can achieve faster convergence than that of BNN for similar pattern recalling rates.",
keywords = "Bifurcating, Network, Refractory",
author = "Hu, \{X. L.\} and Y.T. Zhang",
year = "2002",
month = oct,
doi = "10.1109/iembs.2002.1053140",
language = "English",
isbn = "0-7803-7612-9",
volume = "3",
series = "Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)",
publisher = "IEEE",
pages = "2008--2009",
booktitle = "Conference Proceedings of the Second Joint EMBS-BMES Conference 2002",
address = "United States",
note = "Second Joint EMBS-BMES Conference 2002 ; Conference date: 23-10-2002 Through 26-10-2002",
}