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A network model of respiratory rhythmogenesis
M D Ogilvie1, A Gottschalk, K Anders
1Center for Sleep and Respiratory Neurobiology, University of Pennsylvania, Philadelphia 19104.
The American Journal of Physiology
|October 1, 1992
Summary
This study develops a mathematical model of the respiratory network, confirming its ability to replicate neural activity and respiratory rhythms. The model supports the network oscillator theory for generating breathing patterns.
Area of Science:
- Neuroscience
- Computational Biology
- Physiology
Background:
- The respiratory network's complex dynamics have been studied using mathematical models.
- Richter et al. proposed a three-phase respiratory network model in 1986.
Purpose of the Study:
- To develop and examine a mathematical model of the three-phase respiratory network.
- To validate the model against experimental data on neuronal membrane potentials and phase-resetting behavior.
- To investigate the model's capacity to reproduce various respiratory rhythms and their termination.
Main Methods:
- Developed a mathematical model of the three-phase respiratory network.
- Simulated neuronal membrane potential trajectories.
- Applied stepwise parameter changes to alter rhythm dynamics.
- Introduced perturbing stimuli to assess phase-resetting behavior.
- Simulated input from the superior laryngeal nerve.
Main Results:
- The model accurately reproduced experimentally determined membrane potential trajectories for five distinct neuron types.
- Parameter alterations generated two-phase rhythms, apnea, or complex firing patterns.
- Model phase-resetting behavior closely matched experimental data.
- Phase singularity properties made rhythm termination by perturbation nearly impossible.
- Simulated superior laryngeal nerve input locked the rhythm in the postinspiratory phase.
Conclusions:
- The model's results align with experimental observations.
- The findings support the concept of a network oscillator as the source of the respiratory rhythm.
- The model serves as a valuable tool for understanding respiratory control mechanisms.