IEEE Workshop on Nonlinear Circuit Networks
December 9-10, 2016

Artificial Neuron-Glia Network
Based on Multilayer Perceptron
Chihiro Ikuta† , Yoko Uwate‡ and Yoshifumi Nishio‡†Dept. of Creative Technology Eng., NIT, Anan College,
265 Aoki Minobayashi, Anan Tokushima, 774-0017, JAPAN
Email: ikuta@anan-nct.ac.jp‡Dept. of Electrical and Electronic Eng.,
2-1 Minami-Josanjima, Tokushima, 770-8506, JAPAN
Email: {uwate, nishio}@ee.tokushima-u.ac.jp
Abstract
An astrocyte is one of glial cells. The astrocyte was known to the support cell for neurons. However, some some researchersfound new important and interesting functions [1]-[3]. Especially, the astrocyte can be information processing different way
with the neuron. The astrocyte transmits the signal to neurons and other glial cells by using calcium [4]-[6]. Moreover, thecalcium induces the D-serin by this calcium. The D-serin is necessarily to a synapse plasticity [7]. The astrocyte has consideredthat it is closely related to the human memory. We have focused on the functions of the astrocyte and proposed applications
for an artificial neural network.
In this study, we present an artificial neuron-glia network which is based on a multilayer perceptron (MLP). The exampleof the artificial neuron-glia network is shown as Fig. 1. The MLP is an popular feed forward neural network. The neurons areconnected with other neurons in different layers, and the weight of connection of the neurons are updated by backpropagation
algorithm (BP) [8]. The BP is uses the steepest decent method; thus, it often occurs vanishing gradient. When vanishinggradient occurs, the network cannot update the weight of connection. We need acceleration methods for of MLP learning. We
connect the glia which is based on biological function of the astrocyte with the neurons in the hidden layer. The glia has several
functions and affects the connecting neurons such as giving noise and controlling learning. By using several types of artificial
neuron-glia networks, we show that the glial function improves the network performance and discuss the glial influence.

Neuron…
Glia

Fig. 1.

Artificial neuron-glia network based on MLP.

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[8] D.E. Rumelhart, G.E. Hinton and R.J. Williams, “Learning Representations by Back-Propagating Errors,” Nature, vol.323-9, pp.533-536, 1986.

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