Key result
Computational simulations demonstrate that the respiratory central pattern generator network exhibits similar dynamical properties regardless of the conditional pacemaker node's tuning, ensuring robust performance.
Why the study?
The role of intrinsic pacemaking capabilities in respiratory neurons and how network interactions contribute to respiratory rhythm generation remain unresolved questions.
The dynamical operation of the respiratory central pattern generator is functionally highly robust with respect to the rhythmogenic state of the preBötC excitatory circuits, preserving respiratory function across varying conditions.
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Indicates robust respiratory rhythm generation across pacemaker states in models; leaves open translation to human cardiorespiratory pathophysiology.
A central issue in the study of the neural generation of respiratory rhythms is the role of the intrinsic pacemaking capabilities that some respiratory neurons exhibit. The debate on this issue has occurred in parallel to investigations of interactions among respiratory network neurons and how these contribute to respiratory behavior. In this computational study, we demonstrate how these two issues are inextricably linked. We use simulations and dynamical systems analysis to show that once a conditional respiratory pacemaker, which can be tuned across oscillatory and non-oscillatory dynamic regimes in isolation, is embedded into a respiratory network, its dynamics become masked: the network exhibits similar dynamical properties regardless of the conditional pacemaker node's tuning, and that node's outputs are dominated by network influences. Furthermore, the outputs of the respiratory central pattern generator as a whole are invariant to these changes of dynamical properties, which ensures flexible and robust performance over a wide dynamic range.
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Rubin et al. (2019) studied Respiratory rhythm generation. Computational modeling of preBötzinger Complex intrinsic dynamics vs. Alternative dynamic regimes was evaluated on Network rhythmicity and dynamical properties. Computational simulations demonstrate that the respiratory central pattern generator network exhibits similar dynamical properties regardless of the conditional pacemaker node's tuning, ensuring robust performance.
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