Generating exponentially stable states for a Hopfield Neural Network

An algorithm that generates an exponential number of stable states for the very well-known Hopfield Neural Network (HNN) is introduced in this paper. We show that the quantity of stable states depends on the dimension and number of components of the input pattern supporting noise. Extensive tests verify that the states generated by our algorithm are stable states and show the exponential storage capacity of a HNN. This paper opens the possibility of designing improved HNNs able to achieve exponential storage, and thus find their applicability in complex real-world problems.