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A systems theoretic approach to the study of CNS function.

R J Sclabassi1, D N Krieger, T W Berger

  • 1Department of Neurological Surgery, University of Pittsburgh, PA 15260.

Annals of Biomedical Engineering
|January 1, 1988
PubMed
Summary

This study introduces a systems theory framework to analyze central nervous system (CNS) function. It models neuronal populations and their interactions, revealing nonlinear system properties through functional power series and state-variable approaches.

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Area of Science:

  • Neuroscience
  • Systems Theory
  • Computational Neuroscience

Background:

  • Central nervous system (CNS) function is complex, involving interactions between neuronal populations.
  • Understanding neuronal dynamics requires robust theoretical frameworks.
  • Existing models may not fully capture the nonlinear properties of neural systems.

Purpose of the Study:

  • To present a general systems theoretic paradigm for studying CNS function.
  • To model the linear and nonlinear properties of neuronal systems.
  • To characterize input/output relationships and internal system dynamics.

Main Methods:

  • Conceptualizing neuronal systems as interacting populations with distinct dynamic properties.
  • Employing a functional power series to characterize system input/output behavior.

Related Experiment Videos

  • Utilizing a state-variable approach to analyze internal system structure and physiological variables.

    • Main Results:

    • Investigated the cat somatosensory system and rabbit hippocampal formation.
    • Demonstrated nonlinear properties in response to electrical stimulation.
    • Characterized nonlinearities using high-order kernels of the functional power series.

    Conclusions:

    • The functional power series and state-variable approaches provide a powerful framework for CNS analysis.
    • Nonlinear properties are significant in neural system responses.
    • State-variable models can map input/output characteristics to internal system models.