published_or_final_version / Physics / Master / Master of Philosophy

Engineers often view variability as undesirable and seek to minimize it, such as when they employ transistor-matching techniques to improve circuit and system performance. Biology, however, makes no discernible attempt to avoid this variability, which is particularly evident in biological nervous systems whose neurons exhibit marked variability in their cellular properties. In previous studies, this heterogeneity has been shown to have mixed consequences on network rhythmicity, which is essential to locomotion and other oscillatory neural behaviors. The systems that produce and control these stereotyped movements have been optimized to be energy efficient and dependable, and one particularly well-studied rhythmic network is the central pattern generator (CPG), which is capable of generating a coordinated, rhythmic pattern of motor activity in the absence of phasic sensory input. Because they are ubiquitous in biological preparations and reveal a variety of physiological behaviors, these networks provide a platform for studying a critical set of biological control paradigms and inspire research into engineered systems that exploit these underlying principles. We are directing our efforts toward the implementation of applicable technologies and modeling to better understand the combination of these two concepts---the role of heterogeneity in rhythmic networks of neurons. The central engineering theme of our work is to use digital and analog platforms to design and build Hodgkin--Huxley conductance-based neuron models that will be used to implement a half-center oscillator (HCO) model of a CPG. The primary scientific question that we will address is to what extent this heterogeneity affects the rhythmicity of a network of neurons. To do so, we will first analyze the locations, continuities, and sizes of bursting regions using single-neuron models and will then use an FPGA model neuron to study parametric and topological heterogeneity in a fully-connected 36-neuron HCO. We found that heterogeneity can lead to more robust rhythmic networks of neurons, but the type and quantity of heterogeneity and the population-level metric that is used to analyze bursting are critical in determining when this occurs.