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Related Experiment Video

Updated: Oct 17, 2025

Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR
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Convergent cross sorting for estimating dynamic coupling.

Leo Breston1, Eric J Leonardis2, Laleh K Quinn2

  • 1Program in Neurosciences, University of California, San Diego, La Jolla, CA, 92093, USA. lbreston@ucsd.edu.

Scientific Reports
|October 14, 2021
PubMed
Summary
This summary is machine-generated.

Convergent Cross Sorting (CCS) enhances dynamic coupling analysis from time series data. This novel algorithm improves upon Convergent Cross Mapping (CCM), especially for short or noisy datasets like neural systems.

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Area of Science:

  • Complex Systems Science
  • Dynamical Systems Theory
  • Computational Neuroscience

Background:

  • Natural systems display complex behaviors arising from intricate interactions.
  • Characterizing these dynamic couplings is crucial for understanding system behavior.
  • Existing methods like Convergent Cross Mapping (CCM) have limitations with short or noisy time series data.

Purpose of the Study:

  • Introduce Convergent Cross Sorting (CCS), a novel algorithm to estimate dynamic coupling from time series data.
  • Enhance the identification of coupling existence, strength, and directionality.
  • Improve performance over CCM, particularly for challenging datasets.

Main Methods:

  • Developed Convergent Cross Sorting (CCS), an extension of Convergent Cross Mapping (CCM).
  • CCS utilizes relative ranking of distances within state-space reconstructions.
  • Algorithm validated on simulated data and real electrophysiological recordings.

Main Results:

  • CCS demonstrates superior performance compared to CCM, especially with very short time series.
  • The algorithm effectively identifies coupling in continuous dynamical systems with synchronous behavior.
  • CCS accurately uncovers temporal and directional relationships in systems with rapid dynamic switches, such as neural systems.

Conclusions:

  • Convergent Cross Sorting (CCS) offers a robust method for analyzing dynamic coupling in complex systems.
  • The algorithm's improvements are significant for analyzing short, noisy, or rapidly changing time series data.
  • CCS shows promise for applications in neuroscience and other fields studying interacting systems.