Optimal Noise-Canceling Networks
Name
PhysRevLett.121.208301.pdf
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634.18 KB
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Checksum (MD5)
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Author(s)
Wilczek, Michael
Ronellenfitsch, Henrik Michael
Dunkel, Joern
Date Issued
November 2018
Journal
Physical Review Letters
Publisher
American Physical Society
Citation
Ronellenfitsch, Henrik, et al. “Optimal Noise-Canceling Networks.” Physical Review Letters, vol. 121, no. 20, Nov. 2018. © 2018 American Physical Society
Version
Final published version
Abstract
Natural and artificial networks, from the cerebral cortex to large-scale power grids, face the challenge of converting noisy inputs into robust signals. The input fluctuations often exhibit complex yet statistically reproducible correlations that reflect underlying internal or environmental processes such as synaptic noise or atmospheric turbulence. This raises the practically and biophysically relevant question of whether and how noise filtering can be hard wired directly into a network’s architecture. By considering generic phase oscillator arrays under cost constraints, we explore here analytically and numerically the design, efficiency, and topology of noise-canceling networks. Specifically, we find that when the input fluctuations become more correlated in space or time, optimal network architectures become sparser and more hierarchically organized, resembling the vasculature in plants or animals. More broadly, our results provide concrete guiding principles for designing more robust and efficient power grids and sensor networks.
MIT Department
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1103/PhysRevLett.121.208301
