Publication Date
2021
Document Type
Thesis
Committee Members
Michael Raymer, Ph.D. (Committee Chair); Kathy Engisch, Ph.D. (Committee Co-Chair); T.K. Prasad, Ph.D. (Committee Member); Thomas Wischgoll, Ph.D. (Committee Member)
Degree Name
Master of Science (MS)
Abstract
Homeostatic synaptic plasticity is the process by which neurons alter their activity in response to changes in network activity. Neuroscientists attempting to understand homeostatic synaptic plasticity have developed three different mathematical methods to analyze collections of event recordings from neurons acting as a proxy for neuronal activity. These collections of events are from control data and treatment data, referring to the treatment of neuron cultures with pharmacological agents that augment or inhibit network activity. If the distribution of control events can be functionally mapped to the distribution of treatment events, a better understanding of the biological processes underlying homeostatic synaptic plasticity can be achieved. The aim of this project was to create a tool that allows researchers to quickly process, visualize, and then analyze the homeostatic synaptic plasticity data using the three analysis methods, as well as evaluate the viability of a fourth method.
Page Count
99
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2021
Copyright
Copyright 2021, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
