Abstract
This paper proposes the learning methods to control the frequency of an oscillatory neural network. The learning rules are applied to the neural oscillator that comprises two excitatory neurons, in which only one neuron has a positive feedback weight. Since it is assumed that only the feedback parameter has plasticity, the proposed learning rule can be realized with a high simplicity. By defining the phase of the neural oscillation, a mathematical model is conceived so as to conjure up of the blurred vision of phase trajectories in the system. Successful examples of the frequency learning of the sinusoidal function is shown by the computer simulation. With the proposed learning methods, the frequency of the oscillatory neural network can be adjusted to that of any desired value.
