LEARNING IN NEURAL NETWORKS WITH LOCAL MINIMA

An attempt is made to study learning in neural networks with local minima. For small learning parameters eta, the transition time from one mimimum to another is asymptotically given by exp(eta/eta), with eta, a constant independent of eta, called the reference learning parameter. A general scheme to calculate the reference learning parameter is presented. This scheme is valid for a large class of learning rules.