Adaptive thresholds for layered neural networks with synaptic noise

Authors:
D. Bollé;R. Heylen
Affiliations:
Institute for Theoretical Physics, Katholieke Universiteit Leuven, Leuven, Belgium;Institute for Theoretical Physics, Katholieke Universiteit Leuven, Leuven, Belgium
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Year:
2006

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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.