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A cycle-linear approach to modeling action potentials.

P Ye1, E Entcheva, S A Smolka

  • 1Comput. Sci. Dept., Stony Brook Univ., NY 11790, USA.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|October 20, 2007
PubMed
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We introduce cycle-linear hybrid automata (CLHA) to model complex nonlinear systems. This method provides an efficient, linear approximation for pseudo-periodic dynamical systems like excitable cell action potentials.

Area of Science:

  • Dynamical Systems Theory
  • Computational Neuroscience
  • Hybrid Automata

Background:

  • Nonlinear dynamical systems often exhibit pseudo-periodic behavior.
  • Modeling these systems requires capturing complex, mode-dependent dynamics.
  • Existing methods may struggle with efficiency and accuracy for such systems.

Purpose of the Study:

  • Introduce cycle-linear hybrid automata (CLHA) for efficient modeling.
  • Develop a method for approximating nonlinear, pseudo-periodic dynamical systems.
  • Demonstrate CLHA's utility in biological system modeling.

Main Methods:

  • Define CLHA based on operating modes and linear system dynamics.
  • Incorporate memory units and deformation coefficients for cycle-dependent parameters.

Related Experiment Videos

  • Utilize CLHA to approximate the phase space of nonlinear systems.

    • Main Results:

    • CLHA provide an intuitive, linear approximation of nonlinear system phase spaces.
    • The model efficiently captures pseudo-periodic behavior.
    • Successfully modeled action potentials in excitable cells and their pacing adaptation.

    Conclusions:

    • CLHA offer an efficient and accurate approach for modeling nonlinear, pseudo-periodic systems.
    • The framework is particularly suitable for biological systems like excitable cells.
    • CLHA advance the computational modeling of complex dynamical behaviors.