Learning model for coupled neural oscillators

Neurophysiological experiments have shown that many motor commands in livin g systems are generated by coupled neural oscillators. To coordinate the os cillators and achieve a desired phase relation with desired frequency, the intrinsic frequencies of component oscillators and coupling strengths betwe en them must be chosen appropriately. In this paper we propose learning mod els for coupled neural oscillators to acquire the desired intrinsic frequen cies and coupling weights based on the instruction of the desired phase pat tern or an evaluation function. The abilities of the learning rules were ex amined by computer simulations including adaptive control of the hopping he ight of a hopping robot. The proposed learning rule takes a simple form lik e a Hebbian rule. Studies on such learning models for neural oscillators wi ll aid in the understanding of the learning mechanism of motor commands in living bodies.