4th International Forum on Advanced Technologies
March 8th-9 th, 2018,Tokushima, Japan

Network with Chemical Messenger
in Feed Forward Neural Networks
Kazuki Nagao, Yoko Uwate and Yoshifumi Nishio
Tokushima University, Tokushima 770-8501, Japan* E-mail:k-nagao@ee.tokushima-u.ac.jp

1. Introduction
Neural Network (NN) and Deep Learning are made to imitate the human nervous system [1]. There are thefollowing three substances constituting the human neural circuit. First, there are many nerve cells (neuron) in
the human brain. Second, synapse connects to neurons and becomes junction for communicating information.
Third, neurotransmitter is substances generated from synapse. 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 DeepLearning.
In this study, we propose new system of feed forward Neural Network (FFNN) having artificial
neurotransmitter. We use characteristic of acetylcholine among neurotransmitter. We use this system andimprove learning accuracy more than the conventional system.
2. Neurotransmitter
Neurotransmitter, also known as chemical messenger, is endogenous chemicals that enable neurotransmission.
It is a neurotransmitter that is released from the synapse and cause an excitement or an inhibition reaction ofneurons. More than 100 types are currently being confirmed.
In this study, we focus on acetylcholine what is one of the neurotransmitter. Acetylcholine was discovered by
Henry Halletdale in 1914 and revealed to be a neurotransmitter by Otto Levy. It is early detection amongneurotransmitters. It has many features. For example, it is secretion amount changes within 1 day. Secretion
tends to decrease throughout the day. As a result, the learning function declines and you feel sleepy. However,you can restore the secretion of acetylcholine by having a meal. However, specific amount of secretion isunknown. It has a relationship with illness. It is known that too much acetylcholine secretion leads toParkinson’s disease. It is also known that too little acetylcholine secretion leads to Alzheimer’s disease.
3. Proposed Method(a) Pattern 1.

(b) Pattern 2 .

(c) Pattern 3.

Figure 2 : Three patterns which artificial characteristicsof acetylcholine.
Figure 1 : A Schematic diagramof new system about FFNN
using Iris data set.
We use artificial characteristics of acetylcholine for the middle layer of FFNN. Proposed system that is usedin this study is shown in Fig. 1.
Characteristics of acetylcholine are not fully understood yet. We use two reported characteristics ofacetylcholine in this study. First, the amount of acetylcholine secretion varies in a daily life. Second, theamount of secretion recovers by having breakfast, lunch and dinner. For using these characteristics, we madetwo patterns which artificial characteristics of acetylcholine (Fig. 2). We consider the number of trials to beone day. So we decide to recover the secretion amount three times within learning loops. Pattern 1 shows thatthe amount of secretion decreases linearly at each learning loop. Pattern 2 shows that the amount of secretion
amount of secretion decreases curvilinearly at each learning loop. Pattern 3 also shows that the amount ofsecretion decreases curvilinearly at each learning loop. And it shows that it decreases exponentially.

4th International Forum on Advanced Technologies
March 8th-9 th, 2018,Tokushima, Japanℎ𝑖𝑗 (𝑡 + 1) = 𝑓{∑ 𝑤𝑖𝑗 (𝑡)(𝑥(𝑡) + 𝜒) − 𝜃(𝑡)}(1)Equation (1) describes the propagation equation of neuron having the acetylcholine’s system at hidden layer.
4. Simulation Result
We compare with the minimum value of the average of learning error rate of 10 consecutive times. Learning
error rate is determined by using root mean squared error. Root mean square error is expressed as Eq. (2).
1A(y) = √𝑛 ∑𝑛𝑖=1(𝑦𝑙𝑒𝑎𝑟𝑛 − 𝑦)2(2)𝑦𝑙𝑒𝑎𝑟𝑛 is derived based on learning. y is the answer we have prepared. The weight correction constant is set
to 0.1 in all systems.
We verify the proposed method using two data sets [2]. First, Iris data set has 150 data and used the networkof the number of neurons in the input layer is 4, the number of neurons in the middle layer is 10 and thenumber of neurons in the output layer is 3. Second, y = 𝑥 2 data set has 201 data. It learns the point of [-1, 1]of the function of y = 𝑥 2. The network of the number of neurons in the input layer is 1, the number of neurons
in the middle layer is 10 and the number of neurons in the output layer is 1. In Tables 1 and 2, “Conventional”
shows the conventional system that does not have acetylcholine’s system. Patterns 1, 2 and 3 show proposedsystems that have features of acetylcholine. These features are shown in Fig. 2.
Table 1 : Learning error rate of using Iris data set.
SystemLearning error rate

Conventional

0.05011

Pattern 10.04246

Pattern 20.04496

Pattern 30.04659

In Table 1, all proposed patterns obtain higher learning accuracy than the conventional system. In pattern 1,
learning accuracy is improved by about 18%.
Table 2 : Learning error rate of using y = 𝑥 2 data set.
SystemLearning error rate

Conventional

0.00559

Pattern 10.00562

Pattern 20.00531

Pattern 30.00520

In Table 2, patterns 2 and 3 were able to reduce learning error rate compared with the conventional system. In
pattern 3, learning error rate is reduced by about 7%.
4. Conclusion
First, new methods were proposed for FFNN using the property of acetylcholine, which is a neurotransmitter.
Then, we used the amount of acetylcholine secretion decreases in a daily life and the amount of secretion
recovers by having meals (breakfast, lunch and dinner). For using these characteristics, we made three patterns.
As the results of verification in Iris data set, it was possible to reduce the error rate as compared with theconventional system.
We believe that the versatility of the neural network can be improved by using two methods for one system.
We would like to challenge more realistic problems in the future.
Acknowledgment
This work was partly supported by JSPS Grant-in-Aid for Scientific Research 16K06357.
References[1]Gurney and Kevin, “An introduction to neural networks,” UCL Press, ISBN 1857286731, OCLC 37875698,
1997.
[2]http://archive.ics.uci.edu/ml/index.php, “Center for Machine Learning and Intelligent Systems.”