Adaptive thresholds for neural networks with synaptic noise.
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.
Bollé
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Institute for Theoretical Physics, Katholieke Universiteit Leuven, Celestijnenlaan 200 D, B-3001, Leuven, Belgium. desire.bolle@fys.kuleuven.be
Heylen
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D000818:Animals; D008568:Memory; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D009622:Noise; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated; D013569:Synapses / Q000502:physiology*