Phase transitions of an oscillator neural network with a standard Hebb learning rule
Toru Aonishi · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · 1998
Studies have been carried out on the phase transition phenomena of an oscillator network model based on a standard Hebb learning rule such as the Hopfield model. The relative phase informations, the in phase and antiphase, can be embedded in the network. By self-consistent signal-to-noise analysis, it was found that the storage capacity is given by ${\ensuremath{\alpha}}_{c}=0.042,$ which is better than that of Cook's model. However, the retrieval quality is worse. In addition, an investigation was made into an acceleration effect caused by asymmetry of the phase dynamics. Finally, it was numerically shown that the storage capacity can be improved by modifying the shape of the coupling function.