The pre-Botzinger complex (pBC) is a sub-circuit of the respiratory central pattern generator. The pBC is required for eupnea and is contained in a transverse slice of the ventrolateral medulla. In the slice, pBC cells are responsible for generating the respiratory rhythm, and hypoglossal motoneurons (HMs) are responsible for transmitting the rhythm out of the brainstem to the muscles. Understanding how the transverse slice rhythm is generated and transmitted is a first step in understanding how this process occurs in vivo. To understand this network, we developed ionic current models of the individual network components and explored how the various ion channels affect single-cell firing characteristics and network dynamics. First, we used the considerable amounts of experimental data from neonatal HMs to develop an HM model. The model was used to explore the roles of ion channels in shaping the complex dynamics of the neonatal HM action potential (AP) and to investigate the age-dependent changes in HMs. We used a genetic algorithm to optimize the HM model to more closely fit experimental measures of AP shape. A comparison of feature-based and template-based fitness functions revealed that a feature-based fitness function performs best when optimizing the HM model to fit characteristics of the neonatal HM AP. Next, we used our existing pBC models to understand how different ionic currents affect rhythmogenesis in the pBC. Our results indicate that intrinsic bursters increase the robustness of rhythm generation in the pBC. Finally, we developed an improved pBC neuron model and explored how various ion channels affect bursting dynamics at the single-cell level. The HM and pBC models developed in this study will be used in future network models of the transverse slice.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/14125 |
| Date | 20 November 2006 |
| Creators | Purvis, Liston Keith |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 4276460 bytes, application/pdf |
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