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Computational Models and Emergent Properties of Respiratory Neural Networks

  1. Bruce G. Lindsey1,
  2. Ilya A. Rybak2,
  3. Jeffrey C. Smith3

Published Online: 1 JUL 2012

DOI: 10.1002/cphy.c110016

Comprehensive Physiology

Comprehensive Physiology

How to Cite

Lindsey, B. G., Rybak, I. A. and Smith, J. C. 2012. Computational Models and Emergent Properties of Respiratory Neural Networks. Comprehensive Physiology. 2:1619–1670.

Author Information

  1. 1

    Department of Molecular Pharmacology and Physiology and Neuroscience Program, University of South Florida College of Medicine, Tampa, Florida

  2. 2

    Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, Pennsylvania

  3. 3

    Cellular and Systems Neurobiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland

Publication History

  1. Published Online: 1 JUL 2012

Abstract

Computational models of the neural control system for breathing in mammals provide a theoretical and computational framework bringing together experimental data obtained from different animal preparations under various experimental conditions. Many of these models were developed in parallel and iteratively with experimental studies and provided predictions guiding new experiments. This data-driven modeling approach has advanced our understanding of respiratory network architecture and neural mechanisms underlying generation of the respiratory rhythm and pattern, including their functional reorganization under different physiological conditions. Models reviewed here vary in neurobiological details and computational complexity and span multiple spatiotemporal scales of respiratory control mechanisms. Recent models describe interacting populations of respiratory neurons spatially distributed within the Bötzinger and pre-Bötzinger complexes and rostral ventrolateral medulla that contain core circuits of the respiratory central pattern generator (CPG). Network interactions within these circuits along with intrinsic rhythmogenic properties of neurons form a hierarchy of multiple rhythm generation mechanisms. The functional expression of these mechanisms is controlled by input drives from other brainstem components, including the retrotrapezoid nucleus and pons, which regulate the dynamic behavior of the core circuitry. The emerging view is that the brainstem respiratory network has rhythmogenic capabilities at multiple levels of circuit organization. This allows flexible, state-dependent expression of different neural pattern-generation mechanisms under various physiological conditions, enabling a wide repertoire of respiratory behaviors. Some models consider control of the respiratory CPG by pulmonary feedback and network reconfiguration during defensive behaviors such as cough. Future directions in modeling of the respiratory CPG are considered. Published 2012. Compr Physiol 2:1619-1670, 2012.