The B.A.M. Storage Capacity

(Received 22 February 1994, revised 23 September 1994, accepted 4 October 1994)

Abstract
The Bidirectional Associative Memory (B.A.M.) is a neural network which can store and associate pairs of data in the form of two patterns using an input network of M neurons and an output network with N neurons. Despite its interest there are no theoretical investigations about this model. We obtain the equations of state in a rigorous way using only the assumption that the Edwards-Anderson parameters associated to the two networks are self-averaging: this important property corresponds to the replica symmetry hypothesis in the replica calculations. A comparison between the methods used in the literature is made and the connection of our derivation with Peretto's method is shown. The storage capacity of the B.A.M. is computed when N=M and a bound on it is derived when , in contrast with the strongly diluted case in which the critical capacity is unbounded for or .