Abstract

The general objectives of our research, presented in this series of papers, were to develop a computational model of the brain stem respiratory neural network and to explore possible neural mechanisms that provide the genesis of respiratory oscillations and the specific firing patterns of respiratory neurons. The present paper describes models of single respiratory neurons that have been used as the elements in our network models of the central respiratory pattern generator presented in subsequent papers. The models of respiratory neurons were developed in the Hodgkin-Huxley style employing both physiological and biophysical data obtained from brain stem neurons in mammals. Two single respiratory neuron models were developed to match the two distinct firing behaviors of respiratory neurons described in vivo: neuron type I shows an adapting firing pattern in response to synaptic excitation, and neuron type II shows a ramp firing pattern during membrane depolarization after a period of synaptic inhibition. We found that a frequency ramp firing pattern can result from intrinsic membrane properties, specifically from the combined influence of calcium-dependent K(AHP)(Ca), low-threshold Ca(T) and K(A) channels. The neuron models with these ionic channels (type II) demonstrated ramp firing patterns similar to those recorded from respiratory neurons in vivo. Our simulations show that K(AHP)(Ca) channels in combination with high-threshold Ca(L) channels produce spike frequency adaptation during synaptic excitation. However, in combination with low-threshold Ca(T) channels, they cause a frequency ramp firing response after release from inhibition. This promotes a testable hypothesis that the main difference between the respiratory neurons that adapt (for example, early inspiratory, postinspiratory, and decrementing expiratory) and those that show ramp firing patterns (for example, ramp inspiratory and augmenting expiratory) consists of a ratio between the two types of calcium channels: Ca(L) channels predominate in the former and Ca(T) channels in the latter respiratory neuron types. We have analyzed the dependence of adapting and ramp firing patterns on maximal conductances of different ionic channels and values of synaptic drive. The effect of adjusting specific membrane conductances and synaptic interactions revealed plausible neuronal mechanisms that may underlie modulatory effects on respiratory neuron firing patterns and network performances. The results of computer simulation provide useful insight into functional significance of specific intrinsic membrane properties and their interactions with phasic synaptic inputs for a better understanding of respiratory neuron firing behavior.

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