We examine numerically the storage capacity and the behaviour near saturation
of an attractor neural network consisting of bistable elements with an
adjustable coupling strength, the Bistable Gradient Network (BGN). For strong
coupling, we find evidence of a first-order "memory blackout" phase transition
as in the Hopfield network. For weak coupling, on the other hand, there is no
evidence of such a transition and memorized patterns can be stable even at high
levels of loading. The enhanced storage capacity comes, however, at the cost of
imperfect retrieval of the patterns from corrupted versions.Comment: 15 pages, 12 eps figures. Submitted to Phys. Rev. E. Sequel to
cond-mat/020356