Learning of stable states in stochastic asymmetric networks

Boltzmann-based models with asymmetric connections are investigated. Although they are initially unstable, these networks spontaneously self-stabilize as a result of learning. Moreover, pairs of weights symmetrize during learning; however, the symmetry is not enough to account for the observed stability. To characterize the system it is useful to consider how its entropy is affected by learning and the entropy of the information stream. The stability of an asymmetric network is confirmed with an electronic model.