Adaptive thresholds for layered neural networks with synaptic noise
Disordered Systems and Neural Networks
2007-05-23 v1 Statistical Mechanics
Abstract
The inclusion of a macroscopic adaptive threshold is studied for the retrieval dynamics of layered feedforward neural network models with synaptic noise. It is shown that if the threshold is chosen appropriately as a function of the cross-talk noise and of the activity of the stored patterns, adapting itself automatically in the course of the recall process, an autonomous functioning of the network is guaranteed.This self-control mechanism considerably improves the quality of retrieval, in particular the storage capacity, the basins of attraction and the mutual information content.
Cite
@article{arxiv.cond-mat/0605590,
title = {Adaptive thresholds for layered neural networks with synaptic noise},
author = {D. Bolle and R. Heylen},
journal= {arXiv preprint arXiv:cond-mat/0605590},
year = {2007}
}
Comments
10 pages, 5 figures, accepted for the ICANN 2006 conference