PERIODIC SIGNAL LEARNING AND RECOGNITION IN COUPLED OSCILLATORS

Responses to periodic signals in the oscillator network are studied nu merically. The network used here is globally coupled oscillators with distributed natural frequencies of the oscillatory elements. The abili ty of the temporal-pattern recognition in the system is studied using the learning rule which changes the distribution of the natural freque ncies. It is found that the coherency representing the order of the sy stem exhibits the periodic and complex behaviors. The periodic respons e of the coherency is observed in the case where the input signal is s imilar to the learned signal. The behavior of the coherency becomes co mplex; i.e., the number of the degrees of freedom of the behavior incr eases with the decrease in the similarity of the input signal to the l earned signal. Moreover, the long transient process is observed after turning off the input.