Effect of random synaptic dilution on recalling dynamics in an oscillator neural network

Abstract

In the present paper, we study the effect of random synaptic dilution in an oscillator neural network in which information is encoded by the relative timing of neuronal firing. In order to analyze the recalling process in this oscillator network, we apply the method of statistical neurodynamics. The results show that the dynamical equations are described by some macroscopic order parameters, such as that representing the overlap with the retrieved pattern. We also present the phase diagram showing both the basin of attraction and the equilibrium overlap in the retrieval state. Our results are supported by numerical simulation. Consequently, it is found that both the attractor and the basin are preserved even though dilution is promoted. Moreover, as compared with the basin of attraction in the traditional binary model, it is suggested that the oscillator model is more robust against the synaptic dilution. Taking into account the fact that oscillator networks contain more detailed information than binary networks, the obtained results constitute significant support for the plausibility of temporal coding.