Optimal noise-canceling networks

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 of question 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.

6 pages, 3 figures, supplementary material

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Cited by in corpus (19)

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• Propagation of wind-power-induced fluctuations in power grids
• Inverse Design of Discrete Mechanical Metamaterials
• Enhancing synchronization by optimal correlated noise
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• Network desynchronization by non-Gaussian fluctuations
• Data-Driven Interaction Analysis of Line Failure Cascading in Power Grid Networks
• Network extraction by routing optimization
• Intelligent mechanical metamaterials towards learning static and dynamic behaviors
• Layered Complex Networks as Fluctuation Amplifiers
• Geometric unfolding of synchronization dynamics on networks
• Deterministic joint remote state preparation with a non-maximally entangled channel
• Reconstructing Network Structures from Partial Measurements
• Large and small fluctuations in oscillator networks from heterogeneous and correlated noise
• Resilience of the slow component in timescale separated synchronized oscillators
• Evolution of robustness in growing random networks
• Predicting the response of structurally altered and asymmetrical networks
• Data-Driven Reconstruction and Characterization of Stochastic Dynamics via Dynamical Mode Decomposition