Frontier reviewHomeostasis of exercise hyperpnea and optimal sensorimotor integration: The internal model paradigm
Introduction
The mechanism underlying the seeming constancy of arterial , and pH (, , pHa) from rest to moderate exercise (reviewed in Dempsey et al., 1995, Mateika and Duffin, 1995, Ward, 2000) has been a subject of continuing controversy (Eldridge et al., 2006, Secher et al., 2006, Waldrop et al., 2006). At the heart of the impasse is the enigma of homeostasis (Bernard, 1878–1979, Cannon, 1932), which pervades a host of similar physiological problems (Schmidt-Nielsen, 1994, Keesey and Hirvonen, 1997, Skott, 2003, McKinley and Johnson, 2004, Osborn et al., 2005, Boulant, 2006). At the root of this widespread conundrum is a wholesale and deep-seated reductionist view which predicates a singular, linear and static explanation of all biological phenomena including homeostasis (Ahn et al., 2006a, Ahn et al., 2006b). Here, we highlight a preponderance of counter-evidence, which points to an emerging ‘internal model’ paradigm for respiratory control – and homeostatic regulation and sensorimotor integration in general – that is far more elaborate than conventional wisdom prescribes.
Homeostatic regulation is as much an open physiological problem as an engineering challenge. Designing control algorithms that match up to the ‘wisdom of the body’ – as evidenced by the precision, robustness, versatility and reliability of brain control – is a holy grail in engineering (Wiener, 1948) and a far cry from the highly oversimplified schemes popularized in the biomedical literature. The internal model paradigm inspired by respiratory control suggests a novel principle of nonlinear adaptive control that is potentially applicable to a wide class of intelligent control problems in physiology and engineering.
Section snippets
Feedback, feedforward, and set-point models
The dilemma of exercise hyperpnea is that the supposedly homeostatic CO2 “set point” (Oren et al., 1981) is readily abolished by CO2 inhalation, which elicits a hypercapnic chemoreflex response instead. Similar set-point theories for other homeostatic systems (Keesey and Hirvonen, 1997, Osborn et al., 2005, Boulant, 2006) have also been variously challenged (Selye, 1973, Cecchini et al., 1981, Harris, 1990, Poon, 1996b, Romanovsky, 2004).
Another explanation of exercise hyperpnea is by
Synthesis as a rediscovered roadmap for physiology
The post-genomic renaissance of physiology research enlightens that, where ‘naïve reductionism’ ends, synthesis begins (Cherniack et al., 2001, Strange, 2005). M. Tenney once exhorted (Remmers, 2005): “The physiologist keeps the whole always in mind. He accepts the tactical necessity of reductionism to understand the parts, but, once done, it is for him only the beginning, never the end. Synthesis is his overriding strategy”.
The exercise hyperpnea controversy is reminiscent of an archaic debate
Optimization models of respiratory sensorimotor integration
The demonstrated interactions of the respiratory controller with perturbations in the chemical and mechanical plants call for a paradigm shift from traditional hardwired, additive feedback/feedforward (afferents only) models to ones that conform to these sensorimotor integration (afferents–efferents) properties. The optimization model of ventilatory control first conceived in 1982 (Poon, 1983) holds promise for this purpose.
Internal model paradigm for respiratory sensorimotor integration
The above optimal sensorimotor integration models suggest a parsimonious and unified approach to synthesizing a vast array of respiratory phenomena. To put this in perspective, we compare these optimization models with feedback/feedforward models of ventilatory control and propose a novel conceptual framework which opens new and exciting avenues of hypothesis testing for systematic experimental elucidation of the underlying mechanisms.
A grand challenge
A general theory of internal model self-tuning respiratory control for optimal sensorimotor (afferents–efferents) integration has been proposed vis-à-vis the classical feedback/feedforward (afferents only) control theory. Both these top-down theories are compatible with the exercise hyperpnea and chemoreflex responses. The allure of feedback/feedforward models lies in their direct suggestion of simple, bottom-up “testable” hypotheses regarding possible neural–humoral correlates of the putative
Acknowledgments
We dedicate this article to the late Dr. Fred S. Grodins, whose pioneering work on respiratory control system modeling (Yamashiro et al., 1991) provided early inspirations to CSP. This article is based on lectures given by CSP for a special session “Plasticity and Adaptation” of the Xth Oxford Conference on Modeling and Control of Breathing held at Lake Louise, Alberta, Canada, September 19–24, 2006, and for a symposium “Sensorimotor Integration: The Internal Model Paradigm” held at the Society
References (181)
Ventilation and respiratory mechanics during exercise in younger subjects breathing CO2 or HeO2
Respir. Physiol.
(1997)Respiratory control at exercise onset: an integrated systems perspective
Respir. Physiol. Neurobiol.
(2006)- et al.
Rapid increases in ventilation accompany the transition from passive to active movement
Respir. Physiol. Neurobiol.
(2006) Breathing pattern in humans: diversity and individuality
Respir. Physiol.
(2000)- et al.
Set-point: is it a distinct structural entity in biological control?
J. Theor. Biol.
(1981) - et al.
Ventrolateral pons mediates short-term depression of respiratory frequency after brief hypoxia
Respir. Physiol.
(2000) - et al.
Effects of chronic lung denervation on breathing pattern and respiratory gas exchanges during hypoxia, hypercapnia and exercise
Respir. Physiol.
(1982) Theories on the nature of the coupling between ventilation and gas exchange during exercise
Respir. Physiol. Neurobiol.
(2006)- et al.
Modulation of the ventilatory increase at the onset of exercise in humans
Respir. Physiol.
(1997) Bases and implications of learning in the cerebellum – adaptive control and internal model mechanism
Prog. Brain Res.
(2005)