The stability of the synchronization learning of the oscillatory neural networks
Hiroaki Kurokawa, C.Y. Ho, Susumu Mori · 2002
Since the applications of oscillatory neural network for information processing are well interested, the method to control the phase or the frequency of oscillatory neural networks are required for its future development. Authors have proposed learning rule of oscillatory neural network which is composed of two neurons and only one neuron has the positive feedback weight. In this paper, we consider that the learning rule will be applied to the neural oscillator which has two plastic weight and we show the convergence ability and stability of this Synchronization Learning. We also show the examples of synchronization using the learning rule to show the efficiency of the learning rule.