  In the present study, an amplifying neuron and attenuating neuron, which can
be easily implemented into neural networks without any significant additional
computational effort, are proposed. The activated output value is squared for
the amplifying neuron, while the value becomes its reciprocal for the
attenuating one. Theoretically, the order of neural networks increases when the
amplifying neuron is placed in the hidden layer. The performance assessments of
neural networks were conducted to verify that the amplifying and attenuating
neurons enhance the performance of neural networks. From the numerical
experiments, it was revealed that the neural networks that contain the
amplifying and attenuating neurons yield more accurate results, compared to
those without them.
