ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Displaying 1 - 3 of 3
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- All Subjects: Neurosciences
- Creators: Baer, Steven
Description
Cell morphology and the distribution of voltage gated ion channels play a major role in determining a neuron's firing behavior, resulting in the specific processing of spatiotemporal synaptic input patterns. Although many studies have provided insight into the computational properties arising from neuronal structure as well as from channel kinetics, no comprehensive theory exists which explains how the interaction of these features shapes neuronal excitability. In this study computational models based on the identified Drosophila motoneuron (MN) 5 are developed to investigate the role of voltage gated ion channels, the impact of their densities and the effects of structural features.
First, a spatially collapsed model is used to develop voltage gated ion channels to study the excitability of the model neuron. Changing the channel densities reproduces different in situ observed firing patterns and induces a switch from resonator to integrator properties. Second, morphologically realistic multicompartment models are studied to investigate the passive properties of MN5. The passive electrical parameters fall in a range that is commonly observed in neurons, MN5 is spatially not compact, but for the single subtrees synaptic efficacy is location independent. Further, different subtrees are electrically independent from each other. Third, a continuum approach is used to formulate a new cable theoretic model to study the output in a dendritic cable with many subtrees, both analytically and computationally. The model is validated, by comparing it to a corresponding model with discrete branches. Further, the approach is demonstrated using MN5 and used to investigate spatially distributions of voltage gated ion channels.
First, a spatially collapsed model is used to develop voltage gated ion channels to study the excitability of the model neuron. Changing the channel densities reproduces different in situ observed firing patterns and induces a switch from resonator to integrator properties. Second, morphologically realistic multicompartment models are studied to investigate the passive properties of MN5. The passive electrical parameters fall in a range that is commonly observed in neurons, MN5 is spatially not compact, but for the single subtrees synaptic efficacy is location independent. Further, different subtrees are electrically independent from each other. Third, a continuum approach is used to formulate a new cable theoretic model to study the output in a dendritic cable with many subtrees, both analytically and computationally. The model is validated, by comparing it to a corresponding model with discrete branches. Further, the approach is demonstrated using MN5 and used to investigate spatially distributions of voltage gated ion channels.
ContributorsBerger, Sandra (Author) / Crook, Sharon (Thesis advisor) / Baer, Steven (Committee member) / Hamm, Thomas (Committee member) / Smith, Brian (Committee member) / Arizona State University (Publisher)
Created2014
Description
A general continuum model for simulating the flow of ions in the salt baths that surround and fill excitable neurons is developed and presented. The ion densities and electric potential are computed using the drift-diffusion equations. In addition, a detailed model is given for handling the electrical dynamics on interior membrane boundaries, including a model for ion channels in the membranes that facilitate the transfer of ions in and out of cells. The model is applied to the triad synapse found in the outer plexiform layer of the retina in most species. Experimental evidence suggests the existence of a negative feedback pathway between horizontal cells and cone photoreceptors that modulates the flow of calcium ions into the synaptic terminals of cones. However, the underlying mechanism for this feedback is controversial and there are currently three competing hypotheses: the ephaptic hypothesis, the pH hypothesis and the GABA hypothesis. The goal of this work is to test some features of the ephaptic hypothesis using detailed simulations that employ rigorous numerical methods. The model is first applied in a simple rectangular geometry to demonstrate the effects of feedback for different extracellular gap widths. The model is then applied to a more complex and realistic geometry to demonstrate the existence of strictly electrical feedback, as predicted by the ephaptic hypothesis. Lastly, the effects of electrical feedback in regards to the behavior of the bipolar cell membrane potential is explored. Figures for the ion densities and electric potential are presented to verify key features of the model. The computed steady state IV curves for several cases are presented, which can be compared to experimental data. The results provide convincing evidence in favor of the ephaptic hypothesis since the existence of feedback that is strictly electrical in nature is shown, without any dependence on pH effects or chemical transmitters.
ContributorsJones, Jeremiah (Author) / Gardner, Carl (Committee member) / Baer, Steven (Committee member) / Crook, Sharon (Committee member) / Kostelich, Eric (Committee member) / Ringhofer, Christian (Committee member) / Arizona State University (Publisher)
Created2013
Description
It is increasingly common to see machine learning techniques applied in conjunction with computational modeling for data-driven research in neuroscience. Such applications include using machine learning for model development, particularly for optimization of parameters based on electrophysiological constraints. Alternatively, machine learning can be used to validate and enhance techniques for experimental data analysis or to analyze model simulation data in large-scale modeling studies, which is the approach I apply here. I use simulations of biophysically-realistic cortical neuron models to supplement a common feature-based technique for analysis of electrophysiological signals. I leverage these simulated electrophysiological signals to perform feature selection that provides an improved method for neuron-type classification. Additionally, I validate an unsupervised approach that extends this improved feature selection to discover signatures associated with neuron morphologies - performing in vivo histology in effect. The result is a simulation-based discovery of the underlying synaptic conditions responsible for patterns of extracellular signatures that can be applied to understand both simulation and experimental data. I also use unsupervised learning techniques to identify common channel mechanisms underlying electrophysiological behaviors of cortical neuron models. This work relies on an open-source database containing a large number of computational models for cortical neurons. I perform a quantitative data-driven analysis of these previously published ion channel and neuron models that uses information shared across models as opposed to information limited to individual models. The result is simulation-based discovery of model sub-types at two spatial scales which map functional relationships between activation/inactivation properties of channel family model sub-types to electrophysiological properties of cortical neuron model sub-types. Further, the combination of unsupervised learning techniques and parameter visualizations serve to integrate characterizations of model electrophysiological behavior across scales.
ContributorsHaynes, Reuben (Author) / Crook, Sharon M (Thesis advisor) / Gerkin, Richard C (Committee member) / Zhou, Yi (Committee member) / Baer, Steven (Committee member) / Armbruster, Hans D (Committee member) / Arizona State University (Publisher)
Created2020