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Dynamic pattern generation in behavioral and neural systems.

G Schöner1, J A Kelso

  • 1Center for Complex Systems, Florida Atlantic University, Boca Raton 33431.

Science (New York, N.Y.)
|March 25, 1988
PubMed
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This study presents a mathematical framework for understanding pattern generation in complex biological systems. It maps empirical temporal patterns to simple dynamical laws, aiding the analysis of behavior, neural networks, and neurons.

Area of Science:

  • Complex biological systems
  • Theoretical biology
  • Mathematical modeling

Background:

  • Understanding pattern generation in complex biological systems is a significant challenge.
  • Existing approaches often lack a unified theoretical framework linking different scales of analysis.

Purpose of the Study:

  • To present an operational approach combining theory and experiment for pattern generation.
  • To develop a mathematical framework for analyzing temporal patterns in biological systems.
  • To bridge the understanding of dynamic patterns across multiple scales.

Main Methods:

  • Utilizing central mathematical concepts of self-organization in nonequilibrium systems.
  • Applying order parameter dynamics, stability, fluctuations, and time scales.

Related Experiment Videos

  • Mapping empirically observed temporal patterns to low-dimensional dynamical laws.

    • Main Results:

    • Demonstrated how empirical temporal patterns can be mapped onto simple, low-dimensional stochastic, nonlinear dynamical laws.
    • Developed a theoretical framework providing new observables for pattern analysis.
    • Showcased the applicability of the framework across behavioral patterns, neural networks, and individual neurons.

    Conclusions:

    • The presented framework offers a unified language and strategy for understanding dynamic patterns in biological systems.
    • It facilitates the analysis of pattern generation from lower levels of description to macroscopic behaviors.
    • This approach aids in understanding the linkage among dynamic patterns at various scales.