Stability of Equilibrium Points and Storage Capacity of Hopfield Neural Networks with Higher Order Nonlinearity

In this paper, we consider the storage capacity and stability of the so-called Hopfield neural networks with higher order nonlinearity. There are different ways to introduce higher order nonlinearity to the network; however we have considered one which does not have a huge computational cost. It is shown that, this modification of the Hopfield model significantly improves the storage capacity. We also classify the model via a stability measure, and study the effect of training the network with biased patterns on the stability.