  The inclusion of a threshold in the dynamics of layered neural networks with
variable activity is studied at arbitrary temperature. In particular, the
effects on the retrieval quality of a self-controlled threshold obtained by
forcing the neural activity to stay equal to the activity of the stored paterns
during the whole retrieval process, are compared with those of a threshold
chosen externally for every loading and every temperature through optimisation
of the mutual information content of the network. Numerical results, mostly
concerning low activity networks are discussed.
