Date of Graduation
5-2016
Document Type
Thesis
Degree Name
Master of Science in Electrical Engineering (MSEE)
Degree Level
Graduate
Department
Electrical Engineering
Advisor/Mentor
Jing Yang
Committee Member
Woodrow Shew
Second Committee Member
Jingxian Wu
Keywords
Pure sciences, Biological sciences, Applied sciences, Adaptation, Criticality, Signal processing, Turtle, Visual cortex
Abstract
Previous studies, both theoretical and experimental, of network level dynamics in the cerebral cortex show evidence for a statistical phenomenon called criticality; a phenomenon originally studied in the context of phase transitions in physical systems and that is associated with favorable information processing in the context of the brain. The focus of this thesis is to expand upon past results with new experimentation and modeling to show a relationship between criticality and the ability to detect and discriminate sensory input. A line of theoretical work predicts maximal sensory discrimination as a functional benefit of criticality, which can then be characterized using mutual information between sensory input, visual stimulus, and neural response,. The primary finding of our experiments in the visual cortex in turtles and neuronal network modeling confirms this theoretical prediction. We show that sensory discrimination is maximized when visual cortex operates near criticality. In addition to presenting this primary finding in detail, this thesis will also address our preliminary results on change-point-detection in experimentally measured cortical dynamics.
Citation
Clawson, W. P. (2016). Experimental and Computational Studies of Cortical Neural Network Properties Through Signal Processing. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/1548