平成 29 年度電気関係学会四国支部連合大会 講演論文集 (2017 愛媛大学)
2017 SHIKOKU-SECTION JOINT CONVENTION RECORD OF THE INSTITUTES OF ELECTRICAL AND RELATED ENGINEERS (EHIME)

15-1
Neural Network Having Artificial Neurotransmitter
Kazuki NAGAO1 Shinsaburo KITTAKA1 Yoko UWATE1 Yoshifumi NISHIO1(1 Tokushima University)
1. Introduction

Characteristics of acetylcholine are not fully understoodyet. We use two reported characteristics of acetylcholine
in this study. First, the amount of acetylcholine secretion
varies in a daily life. Second, the amount of secretion recovers by having breakfast, lunch and dinner. For using
these characteristics, we made two patterns which artificial
characteristics of acetylcholine (Fig. 2). Pattern 1 showsthat the amount of secretion decreases curvilinearly at eachlearnning loop. Pattern 2 shows that the amount of secretion decreases linearly at each learnning loop.

Neural Network (NN) and Deep Learning are made toimitate the human nervous system. There are the following three substances constituting the human neural circuit.
First, there are many nerve cells (neuron) in the humanbrain, and information is passed on next neurons throughthe connection, and we can judge things. Second, synapse
connects to neurons and becomes junction for communicating information. Third, neurotransmitter is substances
generated from synapse. It excites and suppresses neurons.
It has a role to promote learning. Now, neuron and synapse
are used in NN and Deep Learning. However, neurotransmitter is not used in NN and Deep Learning.
In this study, we propose new systems of feed forward
Neural Network (FFNN) having artificial neurotransmitter. We use characteristic of acetylcholine among neurotransmitter. Acetylcholine has been reported to be related
to diseases and memory. We use this system and improvelearning accuracy more than basic system.

hij (t + 1) = f{∑}wij (t)x(t) − θ (t)(1)

Equation (1) shows the propagating equation of the conventional neuron system at hidden layer. It does not havethe acetylcholine’s system. x is input data. w is connection
weight. θ is threshold. h is output value.

2. Proposed methodhij (t + 1) = f

We used artificial characteristics of acetylcholine for themiddle layer of FFNN. Proposed system that is used in thisstudy is shown in Fig. 1.

{∑}
wij (t)(x(t) + χ) − θ (t)Equation (2) shows the propagating equation of the neuron having the acetylcholine’s system at hidden layer.
And Eqs. (1) and (2) using sigmoid function Eq. (3) for
activation function.
f (a) =

1
1 + e−a(3)3. Simulation result
We define as the learning loops = 50000. We use Iris
data set and define as the number of learning data sets =150. The number of neurons in each layer is 4, 10 and 3.
Table 1 shows error rate for conventional system and proposed systems.

Table 1: Error rate.
basicpattern1
pattern2

Figure 1: Schematic diagram of new system about
FFNN.

error
0.051390.000230.00023

From Table 1, the learning accuracy is improved in proposed systems over conventional system. Error rate of pattern 1 and 2 are same.

4. Conclusion(a) pattern1.

(2)

We can approach human neural circuits more than conventional FFNN system by the proposed method. And
we can improve learning accuracy. We understand that itis good to use artificial characteristics of acetylcholine forFFNN.
In the future, we will discover new patterns about characteristics of acetylcholine. And we further deeply examine
how to use acetylcholine in NN.

(b) pattern2.

Figure 2: Two patterns which artificial characteristicsof acetylcholine.

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