Increase of storage capacity of neural networks by preprocessing using convergence and divergence.

Abstract

The greatest practical limitation of the associative memory models, especially the Hopfield model is the low storage capacity. It has been shown by Gardner, that the Hopfield type models storage limit is 2*N, where N is the number of the processing elements or neurons. For biased patterns, on the other hand, it is much greater. But in general the input patterns are not biased. To approach to this problem and to increase the storage capacity of the model, the input patterns have to be diluted by some conversion method particularly which uses convergence and divergence in neuroanatomical sense. Based on this model these parameters can be estimated. As a consequence of this bias and the divergence, the storage capacity is increased. This preprocessing method doesn't lead to the loss of information and keeps the error correcting ability of the model.