Ground states of partially connected binary neural networks

Neural networks defined by outer products of vectors over {-1, 0, 1} are considered. Patterns over {-1, 0, 1} define by their outer products partially connected neural networks consisting of internally strongly connected externally weakly connected subnetworks. Subpatterns over {-1, 1} define subnetworks, and their combinations that agree in the common bits define permissible words. It is shown that the permissible words are locally stable states of the network, provided that each of the subnetworks stores mutually orthogonal subwords, or, at most, two subwords. It is also shown that when each of the subnetworks stores two mutually orthogonal binary subwords at most, the permissible words, defined as the combinations of the subwords (one corresponding to each subnetwork), that agree in their common bits are the unique ground states of the associated energy function