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• W1983162973 endingPage "4871" @default.
• W1983162973 startingPage "4865" @default.
• W1983162973 abstract "Studies have been made on the phase transition phenomena of an oscillator network model based on a standard Hebb learning rule like the Hopfield model. The relative phase informations---the in-phase and anti-phase, can be embedded in the network. By self-consistent signal-to-noise analysis (SCSNA), it was found that the storage capacity is given by $alpha_c = 0.042$, 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." @default.
• W1983162973 created "2016-06-24" @default.
• W1983162973 creator A5036559138 @default.
• W1983162973 date "1998-10-01" @default.
• W1983162973 modified "2023-10-02" @default.
• W1983162973 title "Phase transitions of an oscillator neural network with a standard Hebb learning rule" @default.
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• W1983162973 doi "https://doi.org/10.1103/physreve.58.4865" @default.
• W1983162973 hasPublicationYear "1998" @default.
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