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Modeling the electrical behavior of anatomically complex neurons using a network analysis program: excitable

B Bunow, I Segev, J W Fleshman

    Biological Cybernetics
    |January 1, 1985
    PubMed
    Summary
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    We developed methods using SPICE to model nerve cell electrical activity, approximating Hodgkin-Huxley equations with circuit elements for efficient simulation of neuronal behavior.

    Area of Science:

    • Computational Neuroscience
    • Biophysics
    • Electrical Engineering

    Background:

    • The Hodgkin-Huxley model describes neuronal action potentials using complex differential equations.
    • Simulating these models traditionally requires significant computational resources.
    • General-purpose circuit analysis programs offer potential for more accessible modeling.

    Purpose of the Study:

    • To adapt the Hodgkin-Huxley model for simulation using the SPICE circuit analysis program.
    • To develop efficient methods for implementing neuronal kinetics in electrical circuit models.
    • To enable neurobiologists to create detailed, anatomically realistic nerve cell models.

    Main Methods:

    • Implemented the four non-linear partial differential equations of the Hodgkin-Huxley model using SPICE electrical circuit elements.

    Related Experiment Videos

  • Approximated Hodgkin-Huxley rate constants (alpha and beta) with polynomial functions for SPICE efficiency.
  • Detailed the process of coding the sodium conductance and provided a complete code listing in the Appendix.

    • Main Results:

    • Successfully simulated Hodgkin-Huxley action potentials, validating models against established results.
    • SPICE models of branched axons accurately predicted propagation in inhomogeneous axons.
    • Constructed new models of Ia input to alpha-motoneurons and simulated dendritic spine interactions.

    Conclusions:

    • SPICE provides an efficient platform for creating detailed computational models of nerve cells.
    • The polynomial approximation of rate constants enhances simulation efficiency.
    • These methods facilitate the exploration of complex neuronal morphology and physiology.