The neural network is one which is modeled after the nervous system in the brain. It play an important role in the field of image processing and pattern perception.
Until now, it has been considered that informations are expressed by firing rate of neurons. However, some experiments show that a firing rate of neurons doesn't change even if the neurons have external stimulus but that some neurons fire coincidentally.
If informations are expressed by neurons' correlative firing, network's outputs are expressed as a group of firing neurons. Such groups of neurons are called dynamical cell assembly. Coincident neural networks realize dynamical cell assembly.
In this study, coincident neural networks detect cyclic signals. The learning rule of coincident neurons is proposed and then coincident neural networks learn cyclic signals.