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


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

February 29, 2016
May 23, 2016
September 20, 2016
modular network, network evolution, denoising
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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