Adaptive thresholds for neural networks with synaptic noise

Details Adaptive thresholds for neural networks with synaptic noise Adaptive thresholds for neural networks with synaptic noise

2007-08-02

arxiv.org/abs/0708.0328v1

International Journal of Neural Systems, Vol. 17, No. 4 (2007) 241-252

Physics

Condensed Matter

Disordered Systems and Neural Networks

12 pages, 10 figures

Scientific paper

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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