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Optimal noise-canceling networks

Title data

Ronellenfitsch, Henrik ; Dunkel, Jörn ; Wilczek, Michael:
Optimal noise-canceling networks.
In: Physical Review Letters. Vol. 121 (2018) Issue 20 . - No. 208301.
ISSN 1079-7114
DOI: https://doi.org/10.1103/PhysRevLett.121.208301

Abstract in another language

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.

Further data

Item Type: Article in a journal
Refereed: Yes
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Physics > Chair Theoretical Physics I > Chair Theoretical Physics I - Univ.-Prof. Dr. Michael Wilczek
Profile Fields > Advanced Fields > Nonlinear Dynamics
Result of work at the UBT: No
DDC Subjects: 500 Science > 530 Physics
Date Deposited: 23 Feb 2022 08:34
Last Modified: 23 Feb 2022 08:34
URI: https://eref.uni-bayreuth.de/id/eprint/67567