Phase Transitions of an Oscillator Neural Network with a Standard Hebb Learning Rule

Details Phase Transitions of an Oscillator Neural Network with a Standard Hebb Learning Rule Phase Transitions of an Oscillator Neural Network with a Standard Hebb Learning Rule

1998-08-12

arxiv.org/abs/cond-mat/9808121v1

Physics

Condensed Matter

Disordered Systems and Neural Networks

10 pages, 6 figures

Scientific paper

10.1103/PhysRevE.58.4865

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.

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