  We analyze the Blume-Emery-Griffiths (BEG) associative memory with sparse
patterns and at zero temperature. We give bounds on its storage capacity
provided that we want the stored patterns to be fixed points of the retrieval
dynamics. We compare our results to that of other models of sparse neural
networks and show that the BEG model has a superior performance compared to
them.
