Glial cells include several types of cells such as astrocytes, and oligodendrocytes apart from the neurons in the brain. In particular, astrocytes are known to be important in higher brain function and are therefore sometimes simply called glial cells. An astrocyte can transmit signals to other astrocytes and neurons using ion concentrations. Thus, we expect that the functions of an astrocyte can be applied to an artificial neural network. In this study, we propose a multi-layer perceptron (MLP) with a pulse glial chain. The proposed MLP contains glia (astrocytes) in a hidden layer. The glia are connected to neurons and are excited by the outputs of the neurons. The excited glia generate pulses that affect the excitation thresholds of the neurons and their neighboring glia. The glial network provides a type of positional relationship between the neurons in the hidden layer, which can enhance the performance of MLP learning. We confirm through computer simulations that the proposed MLP has better learning performance than a conventional MLP.