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Realistic Computational Models of Respiratory Neurons and Networks

  • Jeffrey C. Smith

Abstract

Breathing movements in mammals result from networks of neurons in the central nervous system that produce a complex spatial and temporal pattern of rhythmic neural activity. The underlying mechanisms are not fully understood, and computational modeling is becoming an essential tool for achieving a mechanistic understanding. It is now recognized that the activity of respiratory neurons results from a complex dynamic interaction of biophysical properties of individual neurons and network mechanisms that arise from the interconnections of cells. These interactions of cellular and network processes remain difficult to investigate experimentally, however, and computational approaches that permit modeling of biologically realistic neurons and networks, in particular, provide a powerful modeling approach. Methods for modeling networks of realistic neurons incorporating biophysical properties and synaptic interactions have been developed in computational ne uroscience over the past several decades[1]–[4], and the availability of software has now made computer simulation of these realistic types of models practical [4]–[5]. In this chapter, I outline this approach and provide examples of our simulations with a first generation of realistic models of respiratory neurons and networks. These models are a significant departure from earlier models [6]–[9] of the respiratory network that have lacked many of the biophysi- cal and synaptic properties of neurons that are required to replicate the behavior of real networks. The approach outlined here can be applied to model any aspect of respiratory neural function where neurobiological realism is sought. The models I present below are designed mainly to explore mechanisms in the brainstem involved in the generation of the rhythm and pattern of neuron activity underlying the respiratory cycle. I first consider features of the organization and properties of brainstem networks that must be incorporated in the realistic models.

Keywords

Synaptic Conductance Respiratory Neuron Pacemaker Neuron Network Mechanism Somal Compartment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Plenum Press 1996

Authors and Affiliations

  • Jeffrey C. Smith
    • 1
  1. 1.Laboratory of Neural Control National Institute of Neurological Disorders and StrokeNational Institutes of HealthBethesda

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