Effect of refractoriness on learning performance of a pattern sequence

The primary purpose of this study is to reveal the effects of refractoriness on learning performance. We simulated that Elman network, which consists of chaotic neurons, learns a pattern sequence using the back-propagation algorithm. Consequently, the learning speed was accelerated about 46% compared with that of the network consisting of integrate-and-fire model neurons. In addition, we analyzed the required number of hidden neurons, asynchronous activities of hidden neurons´ refractoriness. These results suggested that the refractoriness contributes to efficient encoding in the hidden layer of Elman network.