Oscillator neural network model with distributed native frequencies

We study the associative memory of an oscillator neural network with distri buted native frequencies. The model is based on the use of the Hebb learnin g rule with random patterns (xi(i)(mu) = +/-1), and the distribution functi on of native frequencies is assumed to be symmetric with respect to its ave rage. Although the system with an extensive number of stored patterns is no t allowed to become entirely synchronized, long time behaviours of the macr oscopic order parameters describing partial synchronization phenomena can b e obtained by discarding the contribution from the desynchronized part of t he system. The oscillator network is shown to work as associative memory ac companied by synchronized oscillations. A phase diagram representing proper ties of memory retrieval is presented in terms of the parameters characteri zing the native frequency distribution. Our analytical calculations based o n the self-consistent signal-to-noise analysis are shown to be in excellent agreement with numerical simulations, confirming the validity of our theor etical treatment.