平成24年度 電気関係学会四国支部連合大会

25

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Multi-Layer Perceptron Including Learning-Neuron
Chosen by Regularly-Firing-Glia
Chihiro IKUTA

Yoko UWATE
Yoshifumi NISHIO(Tokushima University)

1. Introduction

3. Simulation

A glia is a nervous cell existing in a brain. This cell
has not been investigated, because it can not use an electric signal similar to a neuron. However, some researchers
discovered that the glia uses various ions concentration tosignals [1]. Among them, we have focused attention on functions of Ca2+ . The glia generates the Ca2+ concentration
wave. The synapse long-term potentiation is controlled byincreasing the Ca2+ concentration, moreover the Ca2+ concentration wave is propagated to wide range. Thus, the glia
has a signiﬁcant inﬂuence on the neuron learning.
In this study, we propose a Multi-Layer Perceptron(MLP) including leaning-neuron chosen by regularly-ﬁringglia. The hidden-layer neurons are separated to somegroups. Each group alternately changes between a learningterm and a non-learning term. During the learning term,
the neurons in the same group are learned by Back Propagation (BP) algorithm. On the other term, the weight ofconnection between the input-layer and the hidden-layer is
not updated. By computer simulation, we conﬁrm the performance of the proposed MLP.

Two-spiral problem is a famous benchmark for the artiﬁcial neural network. It is known as a high nonlinearity
problem. In this benchmark, the MLP are inputted thecoordinates of two diﬀerent spirals and learns its classiﬁcations. After learning, the MLP classiﬁes the unknown
coordinates to either one of the two-spiral.
In this simulation, neurons in each layer are composedof 2-40-1. The iterations are 100000. The error index isthe Mean Square Error (MSE). We compare the learning
performance of the proposed MLP with the standard MLPand MLP with random noise. Moreover, we investigate theperformance when we change the number of neurons for one
group, the time length of the learning term, and time lag ofthe learning term.

2. Proposed method(a) Time lag=1.

The MLP is a famous feed forward neural network. Thisnetwork is composed of layers of neurons. The MLP generally learns by the BP algorithm. However, the MLP has thelocal minimum problem, because the BP uses the steepest
decent method. For the local minimum problem, we often
add a noise to any part of the MLP.
In this study, we propose the MLP including learningneuron chosen by regularly-ﬁring-glia. In the proposed
MLP, the hidden-layer neurons are separated to some groups
according to the connected glias as shown in Fig. 1. The
glias ﬁre with regularity. The weights of connections ofneurons which are connected with the ﬁring glia are updated. For the other neurons, their weights between thehidden-layer and the output-layer are updated, however
their weights between the input-layer and the hidden-layer
are not updated. We use the standard BP to the updating rule of weight of connections. The updating weight is
described in Eq. (1).
∆w = −η

∂E,
∂w

(1)

where w is the weight of connection, η is a learning coeﬃcient, and E is an error of the MLP.
Learning term

Excited glia(b) Time lag=5.

(c) Time lag=10.

Figure 2: Approximation results.
From Fig. 2, the performance of the proposed MLP is better than the standard MLP and the MLP with random noise
when we choose the suitable conditions. We can see that thenumber of neurons for one group contradict the time lengthof the learning term. From this result, we conﬁrm that theperformance of the proposed MLP can be tuned by changing
the parameters.

4. Conclusions
In this study, we have proposed the MLP including learning-neuron chosen by regularly-ﬁring-glias. The
hidden-layer neurons are separated to some group according
to the glias. The neurons are changed between the learning term and the non-learning term with regularity. At thelearning term, the neurons are learned. On the other term,
the weight of connection between the input-layer and thehidden-layer is not updated. Two terms impinge on MLP
learning like a noise. By the simulation, we showed that thechanging two terms improved the MLP performance. Moreover, we conﬁrmed characteristics of the proposed MLP.

Static glia
Non-learning term

Figure 1: Proposed MLP.

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References[1] P.G. Haydon, “Glia: Listening and Talking to the Synapse,”
Nature Reviews Neuroscience, vol. 2, pp. 844-847, 2001.