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

John Ferré1, Ariel Rokem2, Elizabeth A Buffalo3

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

Biorxiv : the Preprint Server for Biology
|August 23, 2023
PubMed
Summary
This summary is machine-generated.

Non-Stationary Dynamic Mode Decomposition (NS-DMD) captures complex, time-varying behaviors in high-dimensional data. This new method models evolving spatiotemporal patterns, outperforming traditional approaches for non-stationary dynamics.

Keywords:
computational neurosciencedata-driven modelingdynamic mode decompositionmulti-variate time-seriesnon-stationary methods

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Area of Science:

  • Dynamical systems analysis
  • Computational neuroscience
  • Data science

Background:

  • Physical processes often exhibit complex, high-dimensional, time-varying dynamics.
  • Traditional Dynamic Mode Decomposition (DMD) is effective for stationary data but struggles with time-varying dynamics.
  • Analyzing spatiotemporal structure in non-stationary systems remains a significant challenge.

Approach:

  • Developed Non-Stationary Dynamic Mode Decomposition (NS-DMD), a generalization of DMD.
  • NS-DMD fits global modulations to drifting spatiotemporal modes, capturing temporal evolution.
  • Validated NS-DMD through simulations and application to neural recordings from a non-human primate.

Key Points:

  • NS-DMD accurately predicts the temporal evolution of dynamic modes.
  • The method successfully recovers results from simpler analytical techniques.
  • Demonstrated the utility of NS-DMD in analyzing complex neural activity during cognitive tasks.

Conclusions:

  • NS-DMD offers a powerful framework for analyzing non-stationary spatiotemporal dynamics.
  • This approach enhances understanding of complex systems like brain activity.
  • NS-DMD provides a robust tool for uncovering evolving patterns in time-varying data.