平成25年度電気関係学会四国支部連合大会 講演論文集 （2013 徳島大学）
2013 SHIKOKU-SECTION JOINT CONVENTION RECORD OF THE INSTITUTES OF ELECTRICAL AND RELATED ENGINEERS (TOKUSHIMA)

1-22
Four-Layer Feed-Forward Neural Networkwith Local Glia Connections
Chihiro IKUTA

Yoko UWATE
Yoshifumi NISHIO(Tokushima University)

1. Introduction

inactivity. The glia pulse is input to the neuron threshold.
The neuron updating rule is described by Eq. (2).

A glia and a neuron are nervous cells. The neuron has
been investigated about biological characteristics and applications. However, the glia had not been investigated very
well, because this cell was considered to be a support cellof the neuron. Recently, some researchers discovered thatthe glia can transmit a signal by using an ion concentration[1]. The glia uses various ions which are a glutamate acid,an adenosine triphosphate, and so on. These ions are also
used in a gap junction of the neuron. Thus, the glia closely
relates to a human cerebration.
In this study, we propose a four-layer feed-forward neuralnetwork with local glia connections. This network is composed of four-layer of the neurons. We connect the glias tothe neurons in two hidden-layers. The glias give the pulses
to the connecting neurons according to outputs of the connecting neurons. The network receives the energy from theglia for escaping out from a local minimum. By the computer simulation, we conﬁrm that the proposed network obtains a better performance than the standard network.

yi (t + 1) = f( n∑)wij (t)xj (t) − θi (t) + αψi (t)

,(2)

j=1

where α is a weight of glial eﬀect and ψ is a pulse of glia.

3. Simulation
We use the Two-Spiral Problem for a learning task. TheTSP is a famous task for the artiﬁcial neural network. Inthe TSP, coordinates of the spiral are input to the network.
The network learns a classiﬁcation of the spiral. In thissimulation, the number of the learning datum are 198. The
number of neurons are 2-10-10-1. The iterations are 500000.
The number of trials are 100. We use three kinds of thenetworks for comparison of the performance.
(1) The standard network.
(2) The standard network with random noise.
(3) The proposed network.

2. Proposed method

The standard network does not have an external unit, thus
it is often falls into local minimum. The MLP with random
noise receives the uniformed random noise. The simulation
result shows in Table 1.
From this table, the proposed network has a better performance than the standard network. The glia pulse improves the performance more than the uniformed random
noise. Moreover, the random noise increases the maximum
error than the standard network. From this result, the glia
pulse has the inﬂuence diﬀerent from the uniformed random
noise.

In this study, we propose the four-layer feed forward neural network with local glia connections which is shown inFig. 1. We generally use the Back Propagation (BP) algorithm for a learning of the network. The BP algorithm
is often used to the learning of the artiﬁcial neural network, however this algorithm falls into the local minimum[2]. For this problem, we connect the glia to the neuron
in two hidden-layers. The glia receives and sums the connecting neuron outputs. If the summed value is over theglia excitation threshold, the glia generates the pulse. The
pulse is input to a connecting neuron threshold. We consider that the glia gives the energy for escaping out fromthe local minimum.

Table 1: Learning performance.
(1)
(2)
(3)

Average0.01384
0.012200.00905

Minimum
0.00001
0.00000
0.00001

Maximum
0.08687
0.094700.07259

Std. Dev.
0.01959
0.01772
0.01234

4. Conclusions
In this study, we have proposed the four-layer feed forward neural network with local glia connections. The neurons in the hidden-layer are connected with the glias. Theglia generates the pulse according to the connecting neuron output. After that the pulse is input to the connecting
neuron threshold. The glia response gives the energy tothe network. Thus, the glia pulse helps the network about
escaping out from the local minimum. By the simulation,
we conﬁrmed that the glia response improves the learning
performance of the network.

Figure 1: Proposed MLP.
The pulse generation rule of the glia is deﬁned by Eq. (1).
ψi (t + 1) ={ 1, I = 0, (g (t) > θ ∩ I > θ )iigit−1, Ii = 0, (gi (t) < −θg ∩ Ii > θt ) ,γψi (t), Ii = Ii + 1, else,(1)References[1] P.G. Haydon, “Glia: Listening and Talking to the Synapse,”
Nature Reviews Neuroscience, vol. 2, pp. 844-847, 2001.
[2] D.E. Rumelhart, G.E. Hinton and R.J. Williams, “Learning
Representations by Back-Propagating Errors,” Nature, vol. 3239, pp. 533-536, 1986.

where ψ is a glia output, g is a sum of outputs of the connecting neurons, γ is an attenuation parameter, θg is anexcitation threshold of the glia, I is a time length of a nonexcitation of the glia, and θt is a time length of a period of22