  A self-control mechanism for the dynamics of a three-state fully-connected
neural network is studied through the introduction of a time-dependent
threshold. The self-adapting threshold is a function of both the neural and the
pattern activity in the network. The time evolution of the order parameters is
obtained on the basis of a recently developed dynamical recursive scheme. In
the limit of low activity the mutual information is shown to be the relevant
parameter in order to determine the retrieval quality. Due to self-control an
improvement of this mutual information content as well as an increase of the
storage capacity and an enlargement of the basins of attraction are found.
These results are compared with numerical simulations.
