Multi-Layer Perceptron including glial pulse and switching between learning and non-learning

A glia is a nervous cell which is existing in a brain. This cell changes a Ca²⁺ concentration. This ion affects a neuron membrane potential and it is propagated to the neighboring glia. Moreover, the Ca²; directly affects the human memory by increasing of a D-serine. From these functions, we propose a Multi-Layer Perceptron (MLP) including glial pulse and switching between a learning and non-learning. In this method, the neurons in the hidden-layer received the pulse from connected glias. The pulse is generated depending on the neuron outputs and it is propagated to the neighboring glias and neurons. Moreover, the neurons are separated to some groups. Each group periodically switches a learning term and a non-learning term. Each group starts the learning term having a small lag each other. We consider that a performance of the MLP improves by two different methods influencing each other. By two simulations, we confirm that the MLP obtains the high solving ability by using our methods.