平成23年度 電気関係学会四国支部連合大会

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Investigation of Neural Network
Repeating Two Kinds of Sleeps
Tatsuya IDE

Chihiro IKUTA Yoko UWATE(Tokushima University)

1. Introduction

3. Simulation

In general, a sleep is important for the human’s life.
Human repeats two kinds of sleeps which are Rapid Eye
Movement sleep (REM sleep) and Non Rapid Eye Movement sleep (non-REM sleep). During the REM sleep, thebrain is waking and eyes usually move. On the other
hand, the brain takes deep rest during the non-REM sleep.
These two states are important for ordinary brain function.
In this study, we propose the Multi-Layer Perceptron(MLP) with repeating REM sleep and non-REM sleep. In
the proposed MLP, the learning data are inputted withadding random noise during the REM sleep. On the otherhand, the learning data are not inputted to the MLP at allduring the non-REM sleep but only the random noise isinputted. We show the propose MLP has better learning
performance than the conventional MLP by the simulation.

In this simulation, we use the three layer’s MLP whichis composed (1–5–1). The MLP learns a step function.
We obtain learning result from the 100 trials. We definethat the ratio of between the waking part and the sleep
part is 2 : 1. In the sleep part, the ratio of between thenon-REM sleep and REM sleep is 2 : 1. Moreover, thenon-REM sleep and the REM sleep are repeated 6 times
during one sleeping. These conditions are similar to theratio in real human. First we show the statistic results on
Table 1. From this table, the proposed MLP has a better
learning performance than the other MLPs. Moreover, wecan see that the two kinds of sleep parts improve the MLP
learning performance.

2. REM and non-REM sleeps
The proposed MLP has a waking part, the REM sleeppart, and the non-REM sleep part. In the waking part,the proposed MLP learns true learning data. After while,
the MLP makes the shift to the sleep part. First, theMLP goes into the non-REM sleep. During the non-REM
sleep, the input connections are cut and random noise areinputted to the neurons on the input layer. After that,
the MLP goes into the REM sleep. In this sleep part,the learning data with adding noise are inputted to theneurons on the input layer. The MLP makes the shift to
the waking state after repeating the two sleeping states
several times.
In this simulation, the neuron updating rule is describedby Eq. (1).

( n∑)Table 1: Learning performance.
Conventional
Proposed
Only non-REM
Only REM    which y is an output, f is sigmoidal function, w is a weightparameter, x is an input, and θ is a threshold. In the sleeppart, the neuron updating rule of input layer changes. In
the case of the non-REM sleep, the neuron updating ruleof the input layer is given by Eq. (2).

wij (t)xj (t) − θi (t),

 

j=1

yi (t + 1)=f( n∑wij (t)ϕ(t) − θi (t),(2)

=f

j=1

                              

Figure 1: Example of learning curves.

4. Conclusions

where ϕ is a uniformed random noise. In the REM sleep,
it is given by Eq. (3).
yi (t + 1)

      ! " # $  # % &" # ' (   )j=1( n∑Max.
0.14860.14910.15410.1474  fMin.
0.00060.00700.01630.0007    =Avg. Err.
0.04340.00850.02640.0519Next, we show an example of the learning curves ofthe MLPs in Fig. 1. From this figure, we can say thatthe proposed MLP converges earlier than the others. In
the case of only non-REM sleep, the leaning curve makes
oscillations. Because the random noise is given to theinput layer during the sleep part.

(1)

yi (t + 1)

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Yoshifumi NISHIO)
wij (t){(xj (t)ϕ(t)} − θi (t). (3)In this study, we have proposed the MLP with repeating
the REM sleep and the non-REM sleep. The proposed
MLP has two kinds of sleep parts. We confirmed that thetwo kinds of sleep parts improve the learning performanceof the MLP.