Storage capacity of rotor Hopfield neural networks

Hopfield neural networks have been studied by many researchers. A complex-valued Hopfield neural network (CHNN) is a multistate model of Hopfield neural network, and has been applied to the storage of multilevel data, such as image data. A rotor Hopfield neural network (RHNN) is an extension of CHNN. The RHNNs demonstrated double the storage capacity of CHNNs and excellent noise tolerance by computer simulations. Jankowski et al. analyzed the storage capacity of CHNNs by approximating the crosstalk term using central limit theorem. In this work, we show that the RHNNs have double the storage capacity of the CHNNs based on their theory.