  The dynamics and the stationary states of an exactly solvable three-state
layered feed-forward neural network model with asymmetric synaptic connections,
finite dilution and low pattern activity are studied in extension of a recent
work on a recurrent network. Detailed phase diagrams are obtained for the
stationary states and for the time evolution of the retrieval overlap with a
single pattern. It is shown that the network develops instabilities for low
thresholds and that there is a gradual improvement in network performance with
increasing threshold up to an optimal stage. The robustness to synaptic noise
is checked and the effects of dilution and of variable threshold on the
information content of the network are also established.
