  The influence of a macroscopic time-dependent threshold on the retrieval
process of three-state extremely diluted neural networks is examined. If the
threshold is chosen appropriately in function of the noise and the pattern
activity of the network, adapting itself in the course of the time evolution,
it guarantees an autonomous functioning of the network. It is found that this
self-control mechanism considerably improves the retrieval quality, especially
in the limit of low activity, including the storage capacity, the basins of
attraction and the information content. The mutual information is shown to be
the relevant parameter to study the retrieval quality of such low activity
models. Numerical results confirm these observations.
