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Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
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Non-Stationary Dynamic Mode Decomposition.

John Ferré1, Ariel Rokem2,3, Elizabeth A Buffalo4

  • 1Physics Department, University of Washington, Seattle, WA 98195, USA.

IEEE Access : Practical Innovations, Open Solutions
|December 11, 2023
PubMed
Summary
This summary is machine-generated.

We developed Non-Stationary Dynamic Mode Decomposition to model complex, time-varying systems. This method captures evolving spatiotemporal dynamics, outperforming traditional approaches for non-stationary data.

Keywords:
Dynamic mode decompositioncomputational neurosciencedata-driven modelingmulti-variate time-seriesnon-stationary methods

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Area of Science:

  • Dynamical systems analysis
  • Computational neuroscience
  • Data-driven modeling

Background:

  • Physical processes often exhibit complex, high-dimensional, time-varying behavior.
  • Existing methods like Dynamic Mode Decomposition (DMD) excel with stationary data but struggle with temporal variations.
  • Analyzing spatiotemporal structure in non-stationary data remains a significant challenge.

Purpose of the Study:

  • To develop a generalized method for analyzing time-varying dynamics in high-dimensional data.
  • To capture the temporal evolution of spatiotemporal modes in non-stationary systems.
  • To provide a robust tool for uncovering underlying dynamics in complex systems.

Main Methods:

  • Introduced Non-Stationary Dynamic Mode Decomposition (NS-DMD), an extension of DMD.
  • NS-DMD fits global modulations to capture drifting spatiotemporal modes.
  • Employed simulations and real-world neurophysiological data for validation.

Main Results:

  • NS-DMD accurately predicts the temporal evolution of modes in simulations.
  • The method successfully recovers known results from simpler analytical techniques.
  • Applied NS-DMD to multi-channel recordings from a non-human primate performing a cognitive task.

Conclusions:

  • Non-Stationary Dynamic Mode Decomposition offers a powerful approach for analyzing complex, time-varying systems.
  • This method enhances the understanding of spatiotemporal structures in non-stationary data.
  • NS-DMD has broad applicability in fields like neuroscience and fluid dynamics.