Emergent synchronous patterns in a multilayer neural network

Synchronous firing patterns are known to occur in diverging and converging connections in layered neural networks. Synchronous activities have been discovered in biological experimental studies in recent years, however, functional roles of the synchronous patterns in the brain are not yet clarified. To approach this problem, we focus on the function of conversion from asynchronous patterns to synchronous patterns in a neural network model. In this paper, using a multilayer neural network model, we investigate the input patterns which trigger a synchronous firing and the parameters which have an effect on synchronous firings by computer simulations. The results of the computer simulations lead us to conclude that the emergent synchronous firing acts as a detector for specific spatio-temporal patterns within a small time window on the order of tens of milliseconds. In addition, the characteristic parameters in a neural network such as delay and connection probability have an effect on frequency of synchronous firing.