Adaptive thresholds for neural networks with synaptic noise

D. Bolle; R. Heylen

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arXiv · 0708.0328

Adaptive thresholds for neural networks with synaptic noise

D. Bolle·R. Heylen

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Abstract

The inclusion of a macroscopic adaptive threshold is studied for the retrieval dynamics of both layered feedforward and fully connected neural network models with synaptic noise. These two types of architectures require a different method to be solved numerically. In both cases 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.

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BibTeX RIS

D. Bolle, R. Heylen. 2007-08-02. Adaptive thresholds for neural networks with synaptic noise. https://arxiv.org/abs/0708.0328

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