New Computational Models of the Respiratory Oscillator in Mammals

Abstract

Unraveling how the brainstem respiratory oscillator generates the rhythm of breathing remains one of the fundamental problems in understanding the neural control of breathing. With the recent expansion of information on biophysical and network properties of brainstem respiratory neurons has come the understanding that a synthesis of neural processes at cellular, synaptic, and network levels will be required to solve this problem (4, 7, 9). Modeling is an essential tool for achieving such a synthesis. Computational approaches that allow modeling of biologically realistic neurons and networks, closely based on experimental data of cell biophysical properties and network architecture, will be particularly powerful in this regard (1, 6). There are now well established approaches for modeling networks of neurons incorporating the complex biophysical properties and spatiotemporal interactions that are required to describe the behavior of real neurons and networks (ibid.). Recent technical developments, including the availability of neuron/network simulation software, have made computer simulation of these more realistic types of models practical (e.g., 2, 6). In this paper we briefly describe new models for the respiratory oscillator that incorporate this approach.