Investigation of Multi-Layer Perceptron with propagation of glial pulse to two directions

A glia is nervous cell which exists in a brain. The glia can transmit signal to other glias and neurons by change of ions´ densities. We have an interest in this feature of the glia. We consider that we can apply this feature to an artificial neural network. In this study, we propose a Multi-Layer Perceptron (MLP) with propagation of glial pulse to two directions. The proposed MLP has the glias in a hidden layer. The glias are connected with neurons and are excited by the outputs of neurons. The exciting glias generate pulses and the pulses affect neurons´ thresholds and neighboring glias. We consider that the MLP obtains the relationships of position of neurons in the hidden layer and this information give good influence to the MLP leaning. We confirm that the proposed MLP has better learning performance than the conventional MLP. Moreover, we confirm that the performance of the proposed MLP is changed by some conditions of propagation of the glial pulse.