Improvement of learning performance of multi-layer perceptron by two different pulse glial networks

A glia is the most number of nervous cells in a brain. The glia is investigated in a medical field, because the glia correlates to neuron works and composes a human cerebration. We consider that the glia function can be applied to an artificial neural network. In this study, we propose the Multi-Layer Perceptron (MLP) with the two different pulse glial networks. The proposed MLP has the glial network which is inspired from biological functions of the glia. One neuron is connected with two glias. Two glias generate the pulse depending on the output neurons. One glia connects the neuron for increasing the output of neuron. On the other hand, the glia connects the neuron for decreasing the output of neuron. Both glias composes the glial networks. These effects are propagated into the networks. The glial effects become complexity and affects the MLP learning performance. By the computer simulation, we confirm that the learning performance of the proposed MLP is better than the conventional MLP.