<< Many real-world systems undergo abrupt changes in dynamics as they move across critical points, often with dramatic and irreversible consequences. >>️
AA << aim to develop noise-robust indicators of the distance to criticality (DTC) for systems affected by dynamical noise in two cases: when the noise amplitude is either fixed or is unknown and variable across recordings. (They) present a highly comparative approach to this problem that compares the ability of over 7000 candidate time-series features to track the DTC in the vicinity of a supercritical Hopf bifurcation. >>️
<< in the variable-noise setting, where these conventional indicators perform poorly, (AA) highlight new types of high-performing time-series features and show that their success is accomplished by capturing the shape of the invariant density (which depends on both the DTC and the noise amplitude) relative to the spread of fast fluctuations (which depends on the noise amplitude). >>
AA << introduce a new high-performing time-series statistic, the rescaled autodensity (RAD), that combines these two algorithmic components. >>️
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Brendan Harris, Leonardo L. Gollo, Ben D. Fulcher. Tracking the Distance to Criticality in Systems with Unknown Noise. Phys. Rev. X 14, 031021. Aug 8, 2024.
Also: noise, brain, in https://www.inkgmr.net/kwrds.html
Keywords: gst, noise, brain, mouse visual cortex
