Previous experimental results have suggested the existence of a local cardiac reflex in the rat. In this study, the putative role of such a local reflex in cardiovascular regulation is quantitatively analyzed. A model for the local reflex is developed from anatomical experimental results and physiological data in the literature. Using this model, a systems-level analysis is conducted. Simulation results indicate that the neuromodulatory mechanism of the local reflex attenuates the nonlinearity of the relationship between cardiac vagal drive and arterial pressure. This behavior is characterized through coherence analysis. Furthermore, the modulation of phase-related characteristics of the cardiovascular system is suggested as a plausible mechanism for the nonlinear attenuation. Based on these results, it is plausible that the functional role of the local reflex is highly robust nonlinear compensation at the heart, which results in less complex dynamics in other parts of the reflex.

A simple neuronal network model of the baroreceptor reflex is analyzed. From a control perspective, the analysis suggests a dynamic scheduled control mechanism by which the baroreflex may perform regulation of the blood pressure. The main objectives of this work are to investigate the static and dynamic response characteristics of the single neurons and the network, to analyze the neuromimetic dynamic scheduled control function of the model, and to apply the algorithm to nonlinear process control problems. The dynamic scheduling activity of the network is exploited in two control architectures. Control structure I is drawn directly from the present model of the baroreceptor reflex. An application of this structure for level control in a conical tank is described. Control structure II employs an explicit set point to determine the feedback error. The performance of this control structure is illustrated on a nonlinear continuous stirred tank reactor with van de Vusse kinetics. The two case studies validate the dynamic scheduled control approach for nonlinear process control applications.