2018 International Symposium on Nonlinear Theory and Its Applications,NOLTA2018, Tarragona, Spain, September 2-6, 2018

Learning Method Using Acetylcholine among Neurotransmitterin Feed Forward Neural Networks
Kazuki Nagao, Yoko Uwate and Yoshifumi Nishio
Dept. of Electrical and Electronic Engineering, Tokushima University
2-1 Minami-Josanjima, Tokushima 770-8506, Japan
Email: {k-nagao, uwate, nishio}@ee.tokushima-u.ac.jp
Abstract—In this study, we propose back propagation
learning in feed forward Neural Networks using characteristics of acetylcholine. Acetylcholine is a major neurotransmitter in the brain. The main work of acetylcholine
is to excite the nerves and enhance motivation for learning and memory. In Neural Networks, we bring it closer tohuman information processing by using this function. Our
purpose is to reduce learning error rate.
1. Introduction
Neural Networks (NN) and Deep Learning (DL) aremade to imitate the human nervous system. Such systems learn to do tasks by considering examples, generally
without task-specific programming. For example, in image
recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as “cat” or “no cat” and using the analytic
results to identify cats in other images. They have foundmost use in applications diﬃcult to express in a traditional
computer algorithm using rule-based programming [1].
The human brain processes many and various kinds ofinformation got on the basis of the five senses (sight, hearing, smell, taste and touch). And we can judge things andput us into action. Thereby, the human brain has high bandwidth, high speed parallel processing and flexibility. So, artificial intelligence was created to do the same work as thehuman brain on a computer. Typical examples are NN andDL [2]. In recent years, they have been applied in various
fields and used for applications. For example, smartphone,car, stock price forecast, robot, consumer electronics andso on.
There are the following mainly substances constituting
the human neural circuit. First, there are many nerve cells(neuron) in the brain, and information is passed on next
neurons through the connection, and we can store and judge
things. Second, each connection (synapse) between neurons can transmit a signal to another neuron. The receiving
neuron can process the signals and then signal downstream
neurons connected to it. Neurons may have stated, generally represented by real numbers, typically between 0 and
1. Between neurons may also have a weight that varies
as learning proceeds, which can increase or decrease thestrength of the signal that it sends downstream. Third, neu-rotransmitter (chemical messenger) is substances generated
from synapse. It excites and suppresses neurons. It has arole to promote learning. Other substances include glialcells. Glial cells include several types of cells such as astrocytes, and oligodendrocytes apart from the neurons inthe brain. In particular, astrocytes are known to be important in higher function and are therefore sometimes simply
called glial cells. Now, neuron, synapse and glial cell areused in NN [3] and DL. However, neurotransmitter is not
used at all in NN and DL.
The original goal of NN was to solve problems in thesame way that the human brain would. Over time, attention focused on matching specific mental abilities. In thisstudy, we propose a system of Feed Forward NN (FFNN)
having artificial neurotransmitter. We use characteristic ofacetylcholine among neurotransmitter. Acetylcholine has
been reported to be related to diseases and memory [4],[5]. We use this system and improve learning error rate.
2. Acetylcholine among Neurotansmitter (ChemicalMessenger)
Neurotransmitter, also known as chemical messenger,
is endogenous chemicals that enable neurotransmission.
They transmit signals across a chemical synapse, such asa neuromuscular junction, from one neuron to another neuron. Neurotransmitter is released from synaptic vesicles insynapses into the synaptic cleft, where they are received
by receptors on the target cells. Many neurotransmitters
are synthesized from simple and plentiful precursors suchas amino acids, which are readily available from the dietand only require a small number of biosynthetic steps forconversion. Neurotransmitter plays a major role in shaping
everyday life and functions. Their exact numbers are unknown, but more than 100 chemical messengers have beenuniquely identified. For example, there are acetylcholine,
adrenaline, glutamate, dopamine and so on.
In this study, we focused on acetylcholine among neurotransmitter. Acetylcholine was discovered by Henry Halletdale in 1914 and revealed to be a neurotransmitter by
Otto Levy. It is early detection among neurotransmitters.
It has many features. First, it has a relationship with illness. It is known that too much acetylcholine secretion
leads to Parkinson’s disease. It is also known that too little- 284 -acetylcholine secretion leads to Alzheimer’s disease. Second, it has a relationship with memory. In the human brain,
there is a part that plays a central role in memory formation
called hippocampus. It is studied as acetylcholine as a substance that promotes memory of the hippocampus. Third, it
is secretion amount changes within 1 day. Secretion tends
to decrease throughout the day. As a result, the learning
function declines and you lose motivation and feel sleepy.
Seafood is known to help secrete acetylcholine. However,you can restore the secretion of acetylcholine by having ameal. However, because there are individual diﬀerences,
specific amount of secretion is unknown.

shown in Fig. 2. We consider the number of trials to be oneday. So we decide to recover the secretion amount threetimes within learning loops. The range of acetylcholine ischanged from -0.1 to 0.1 in all patterns.
Pattern 1 shows that the amount of secretion decreases
linearly at each learning loop. Pattern 2 shows that theamount of secretion decreases curvilinearly at each learning loop. The angle of the tangent becomes sharply. Pattern
3 also shows that the amount of secretion decreases curvilinearly at each learning loop. The angle of the tangent becomes gentle. Patterns 2 and 3 are represented by quadratic
functions.

3. Proposed System
We propose a system using artificial characteristics ofacetylcholine for the only middle layer of FFNN. This isbecause the middle layer plays a very large role in FFNN.
In this study, we propose two methods adding acetylcholine. The first one is “Method Using Acetylcholine for
Input Information of Middle Layer”. As a characteristic ofacetylcholine, it excites and inhibits the next neuron during
signal transduction. This is modeled between input layer
and middle layer to make it the first one.
The second one is “Method Using Acetylcholine for
Output Information of Middle Layer”. Like the characteristic of acetylcholine used earlier, it excites and inhibits thenext neuron during signal transduction. This is modeled
between middle layer and output layer to make it the second one.
3.1. Method Using Acetylcholine for Input Informationof Middle Layer(a) Pattern 1.

(b) Pattern 2.

(c) Pattern 3.
Figure 2: Three patterns which artificial characteristics ofacetylcholine.
Equation (1) shows the propagating equation of the conventional neuron system at input layer to hidden layer. It
does not have the acetylcholine’s system.
{∑}
hi j (t + 1) = f
wi j (t)x(t) − θ(t)(1)

Figure 1: A Schematic diagram of method using acetylcholine for input information of middle layer in FFNN.
Characteristics of acetylcholine are not fully understoodyet. We use two reported characteristics of acetylcholine
in this method. First, the amount of acetylcholine secretion decreases in a daily life. Second, the amount of secretion recovers by having three meals (breakfast, lunch anddinner). For using these characteristics, we propose threepatterns which artificial characteristics of acetylcholine as

x is input data. w is connection weight of neurons. θ isthreshold. h is output value.
Equation (2) shows the propagating equation of the neuron having the acetylcholine’s system at input layer to hidden layer.
{∑}
hi j (t + 1) = f
wi j (t)(x(t) + χ(t)) − θ(t)(2)
χ(t) denotes the acetylcholine’s value. It also includes thecharacteristics of Fig. 2.
And Eqs. (1) and (2) using sigmoid function Eq. (3) foractivation function [6].

- 285 -f (a) =1
1 + e−a(3)

3.2. Method Using Acetylcholine for Output Information of Middle Layer
In machine learning you may fall into the local solution.
The local solution is a system that converges to a partial
optimal solution and does not reach a real optimal solution.
In order to solve this problem dropout and others are proposed [7]. In this method, We propose a new one that turns
into dropout. And it improves learning error rate.
We use one reported characteristic of acetylcholine inthis method. It is secretion amount changes within one day.
Secretion tends to decrease throughout the day. As the result, the learning function declines and you lose motivationand feel sleepy. We use this feature to increase or decrease
the amount of output information in input layer to middle
layer of FFNN.

Φ(t) denotes the acetylcholine’s value. Equation (3) is usedas the activation function.
4. Simulation Result
We compared with the minimum value in the average oflearning error rate of 10 consecutive times. Learning error
rate is determined by using root mean squared error. Rootmean square error is expressed as Eq. (5).
vt
A(y) =1∑(ylearn − y)2n i=1n(5)ylearn is derived based on learning. y is the answer wehave prepared. The weight correction constant is set to 0.1
in all systems.
4.1. Method Using Acetylcholine for Input Informationof Middle Layer

Figure 3: A Schematic diagram of method using acetylcholine for output information of middle layer in FFNN.
Cases (0.5 - 1.5) and (0.5 - 2.0) are shown how muchto increase or decrease the amount of output information atinput layer to hidden layer. For example, in case (0.5 - 2.0),
it means changing the information volume of neurons from
1/2 to 2 times. The amount of change is shown in Fig. 4.

We verify the proposed method using three data sets.
First, iris data set [8]. It has 150 data and uses the network of the number of neurons in the input layer is 4, thenumber of neurons in the middle layer is 10, the number ofneurons in the output layer is 3. Second, we use a data set
with 1728 data. It is one that evaluates six to four featuresof cars (car data set) [8]. The network of the number ofneurons in the input layer is 6, the number of neurons in
the middle layer is 10, the number of neurons in the output
layer is 4. Third, we use a data set with 201 data. It lets
you learn the point of [-1, 1] of the function of y = x2 .
The results when using three data sets are shown in Tables 1, 2 and 3 below. In Tables 1, 2 and 3, “Conventional”
shows the conventional system that does not have acetylcholine’s system. Patterns 1, 2 and 3 show the proposedsystem that has features of acetylcholine. These featuresare 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%.

Figure 4: Changing the information volume of output.
Equation (4) shows the propagating equation of the neuron having the acetylcholine’s system at input layer to hidden layer.
{∑}
hi j (t + 1) = f(wi j (t)x(t) − θ(t))Φ(t)(4)- 286 -

Table 2: Learning error rate of using car data set.
SystemLearning error rate
Conventional
0.00001
Pattern 10.00001
Pattern 20.00002
Pattern 30.00002

In Table 2, pattern 1 has the same learning accuracy as
the conventional system, and patterns 2 and 3 have poor
error rate. As a cause of this, because the number of data
sets is large. Very high learning accuracy is obtained also
in the conventional system, so the proposed method of thistime seems to have failed.
Table 3: Learning error rate of using y = x2 data set.
SystemLearning error rate
Conventional
0.00559
Pattern 10.00562
Pattern 20.00531
Pattern 30.00520
In Table 3, 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%.
4.2. Method Using Acetylcholine for Output Information of Middle Layer
The results when using one data sets are shown in Fig. 5
and Table 4 below. In Table 4, “Conventional” shows theconventional system that does not have acetylcholine’s system.
Figure 5 shows two graphs when the information amountof neurons is changed. From Fig. 5, we can see that weare escaping from the local solution by using this learning
method.

Figure 5: Changing in error rate when changing information amount of neurons.
Table 4: Learning error rate of iris data set.
SystemLearning error rate
Conventional
0.05011
0.5 - 1.5
0.04641
0.5 - 2.0
0.04893
Next, minimum values of learning error rate of theconventional system and proposed systems are compared.
From Table 4, proposed two types are able to reduce learning error rate over the conventional system.
5. Conclusion
First, new methods were proposed for FFNN using theproperty 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 resultsof verification in three data sets, it was possible to reduce
the error rate as compared with the conventional system.
However, it was diﬃcult to say that it was suitable for allpatterns. It will be necessary to verify using more data setsin the future.
Second, we used that it is secretion amount changes
within one day. Secretion tends to decrease throughout theday. As the result, the learning function declines and you
feel sleepy. For using this characteristic, we proposed themethod to change to dropout. As the results of this study,
it is possible to reduce the error rate as compared with theconventional system and get out the local solution.
We believe that the versatility of the neural networks canbe 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-Aidfor Scientific Research 16K06357.
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