1998/08/12 by Toru Aonishi · 1 citation
Computer Science · Neuroscience · Physics and Astronomy · #Algorithm #Artificial intelligence #Artificial neural network #Asymmetry #Computer science #Coupling (piping) #Function (biology) #Hopfield network #Learning rule #Materials science #Neural Networks and Applications #Neural dynamics and brain function #Noise (video) #Nonlinear Dynamics and Pattern Formation #Phase (matter) #Phase transition #Physics #Quantum mechanics #SIGNAL (programming language) #Statistical physics #cond-mat.dis-nn #cond-mat.stat-mech
paper · pdf · doi:10.1103/physreve.58.4865
10 pages, 6 figures
arxiv created 1998/08/12 · openalex publication_date 1998/10/01 · arxiv updated 2014/09/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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 \ensuremathαc=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.