Skip to main navigation Skip to search Skip to main content

Variability in respiratory rhythm generation: In vitro and in silico models

Research output: Contribution to journalArticlepeer-review

Abstract

The variability inherent in physiological rhythms is disruptive in extremis (too great or too little) but may also serve a functional and important role in homeostatic systems. Here we focus on the neural control of respiration which is critical for survival in many animals. The overall respiratory control system is comprised of multiple nuclei, each of which may have different contributions to rhythm variability. We focused on the pre-Bötzinger complex (preBötC) which is unique in that it can be studied in vitro as an isolated nucleus with autorhythmic behavior. The in vitro results show a bounded range of variability in which the upper and lower limits are functions of the respiratory rate. In addition, the correlation between variability and respiratory rate changes during development. We observed a weaker correlation in younger animals (0-3 days old) as compared to older animals (4-5 days old). Based on experimental observations, we developed a computational model that can be embedded in more comprehensive models of respiratory and cardiovascular autonomic control. Our simulation results successfully reproduce the variability we observed experimentally. The in silico model suggests that age-dependent variability may be due to a developmental increase in mean synaptic conductance between preBötC neurons. We also used simulations to explore the effects of stochastic spiking in sensory relay neurons. Our results suggest that stochastic spiking may actually stabilize modulation of both respiratory rate and its variability when the rate changes due to physiological demand.

Original languageEnglish
Pages (from-to)158-168
Number of pages11
JournalCommunications in Nonlinear Science and Numerical Simulation
Volume32
DOIs
StatePublished - Mar 2016

ASJC Scopus Subject Areas

  • Numerical Analysis
  • Modeling and Simulation
  • Applied Mathematics

Keywords

  • Central pattern generator
  • Noise
  • Pre-Bötzinger
  • Variability

Cite this