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JACIII Vol.20 No.5 pp. 705-711
doi: 10.20965/jaciii.2016.p0705
(2016)

Paper:

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Evolution of Modular Networks Under Selection for Non-Linearly Denoising

Yusuke Ikemoto* and Kosuke Sekiyama**

*Department of Mechanical Engineering, Meijo University
1-501 Shiogamaguchi, Tempaku, Nagoya, Japan

**Department of Micro-Nano Systems Engineering, Nagoya University
Furo-cho, Chikusa-ku, Nagoya, Japan

Received:
February 29, 2016
Accepted:
May 23, 2016
Published:
September 20, 2016
Creative Commons License
Keywords:
modular network, network evolution, denoising
Abstract
Many biological and artifact networks often represent modular structures in which the network can be decomposed into several subnetworks. Here, we propose a simple model for the modular network evolution based on the nonlinear denoising in node activities. This model suggests that modular networks can evolve under certain conditions — if the stipulated goals for the networks or the input and target output pairs involve modular features, or if the signal transfer in a node is carried out in a nonlinear manner with respect to the saturation at the upper and lower bounds. Our model highlights the positive role played by noise in modular network evolution.
Cite this article as:
Y. Ikemoto and K. Sekiyama, “Evolution of Modular Networks Under Selection for Non-Linearly Denoising,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.5, pp. 705-711, 2016.
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