Performance and features of Multi-Layer Perceptron with impulse glial network

We have proposed the glial network which was inspired from the feature of the brain. The glial network is composed by glias connecting each other. All glias generate oscillations and these oscillations propagate in the glial network. We confirmed that the glial network improved the learning performance of the Multi-Layer Perceptron (MLP). In this article, we investigate the MLP with the impulse glial network. The glias generate only impulse output, however they make the complex output by correlating with each other. We research the proposed networks´ parameter dependency. Moreover, we show that the proposed network possess better learning performance and better generalization capability than the conventional MLPs.