UC Davis

The autonomic nervous system (ANS) plays a critical role in neurocardiovascular control. The structure of the neural circuitry and intrinsic properties of autonomic neurons are likely to play important roles in shaping the ANS control response to changes in blood demand. In this dissertation, we explore how the M-current, cardiac feedback, and recurrent neural connections shape the firing dynamics of autonomic cardiac ganglia. In chapter 2, a data-informed, idealized cell-based mathematical model of the neural circuitry in the intrinsic cardiac nervous system (ICNS) is developed that captures the firing properties of ICNS neurons. The model indicates that the M-current and cardiac-cycle dependent feedback shape the firing dynamics of ICNS neurons. In chapter 3, we explain how the M-current bestows ICNS neurons with band-pass filter and resonance properties. In chapter 4, we reduce the cell-based ICNS network model to a firing rate model, which enables extensive mathematical analysis. Analysis of the firing rate model is used to gain insight into how bistability can arise in the cell-based ICNS network model through M-current dynamics and recurrent excitation in the network. Predictions from the firing rate model are confirmed in the cell-based model. Finally, in chapter 5, we extend our ICNS network models to include the neural circuity of the sympathetic and parasympathetic autonomic tracts, the sino-atrial node, the cardiovascular system, and the baroreflex to describe the full autonomic neurocardiovascular control system. Our preliminary results suggest that the detailed structure of the autonomic neural circuitry (beyond just the baroreflex) could upregulate the magnitude and speed of the hearts response to a sudden increase in blood demand.