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  Optimal noise-canceling networks

Ronellenfitsch, H., Dunkel, J., & Wilczek, M. (2018). Optimal noise-canceling networks. Physical Review Letters, 121(20): 208301. doi:10.1103/PhysRevLett.121.208301.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0002-80C5-E Version Permalink: http://hdl.handle.net/21.11116/0000-0003-C9BB-8
Genre: Journal Article

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 Creators:
Ronellenfitsch, Henrik, Author
Dunkel, Jörn, Author
Wilczek, Michael1, Author              
Affiliations:
1Max Planck Research Group Theory of Turbulent Flows, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2266693              

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 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.

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Language(s): eng - English
 Dates: 2018-11-162018-11-16
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1103/PhysRevLett.121.208301
 Degree: -

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Title: Physical Review Letters
Source Genre: Journal
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Publ. Info: Woodbury, N.Y. : American Physical Society
Pages: 6 Volume / Issue: 121 (20) Sequence Number: 208301 Start / End Page: - Identifier: ISSN: 0031-9007
CoNE: https://pure.mpg.de/cone/journals/resource/954925433406_1