Abstract
Convergence times and the corresponding dispersions have been studied numerically as parameters to measure the efficiency of neural network models. These quantities are also supposed to be related to the number of spurious states for each configuration of stored patterns. In this work we measure these quantities for a recent multineuron interaction model presenting an enhanced performance compared to other traditional schemes.
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