UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
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Item Collective dynamics of astrocyte and cytoskeletal systems(2024) Mennona, Nicholas John; Losert, Wolfgang; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Advances in imaging and biological sample preparations now allow researchersto study collective behavior in cellular networks with unprecedented detail. Imaging the electrical signaling of neuronal networks at the cellular level has generated exciting insights into the multiscale interactions within the brain. This thesis aims at a complementary view of the general information processing of the brain, focusing on other modes of non-electrical information. The modes discussed are the collective, dynamical characteristics of non-electrically active, non-neuronal brain cells, and mechanical systems. Astrocytes are the studied non-neuronal brain cells, and the cytoskeleton is the studied dynamic, mechanical system consisting of various filamentous networks. The two filamentous networks studied herein are the actin cytoskeleton and the microtubule network. Techniques from calcium imaging and cell mechanics are adapted to measure these often overlooked information channels, which operate at length scales and timescales distinct from electrical information transmission. Structural, astrocyte actin images, microtubule structural image sequences, and the calcium signals of collections of astrocytes are analyzed using computer vision and information theory. Filamentous alignment of actin with nearby boundaries reveals that stellate astrocytes have more perpendicularly oriented actin than undifferentiated astrocytes. Harnessing the larger length scale and slower dynamical time scale of microtubule filaments relative to actin filaments led to the creation of a computer vision tool to measure lateral filamentous fluctuations. Finally, we adapt information theory to the analog calcium (Ca2+) signals within astrocyte networks classified according to subtype. We find that, despite multiple physiological differences between immature and injured astrocytes, stellate (healthy) astrocytes have the same speed of information transport as these other astrocyte subtypes. This uniformity in speed persists when either the cytoskeleton (Latrunculin B) or energy state (ATP) is perturbed. Astrocytes, regardless of physiological subtype, tend to behave similarly when active under normal conditions. However, these healthy astrocytes respond most significantly to energy perturbation, relative to immature and injured astrocytes, as viewed through cross-correlation, mutual information, and partitioned entropy. These results indicate the value of drawing information from structure and dynamics. We developed and adapted tools across scales from nanometer scale alignment of actin filaments to hundreds of microns scale information dynamics in astrocyte networks. Including all potential modalities of information within complex biological systems, such as the collective dynamics of astrocytes and the cytoskeleton in brain networks is a step toward a fuller characterization of brain functioning and cognition.Item Extensions of the Kuramoto model: from spiking neurons to swarming drones(2020) Chandra, Sarthak; Girvan, Michelle; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The Kuramoto model (KM) was initially proposed by Yoshiki Kuramoto in 1975 to model the dynamics of large populations of weakly coupled phase oscillators. Since then, the KM has proved to be a paradigmatic model, demonstrating dynamics that are complex enough to model a wide variety of nontrivial phenomena while remaining simple enough for detailed mathematical analyses. However, as a result of the mathematical simplifications in the construction of the model, the utility of the KM is somewhat restricted in its usual form. In this thesis we discuss extensions of the KM that allow it to be utilized in a wide variety of physical and biological problems. First, we discuss an extension of the KM that describes the dynamics of theta neurons, i.e., quadratic-integrate-and-fire neurons. In particular, we study networks of such neurons and derive a mean-field description of the collective neuronal dynamics and the effects of different network topologies on these dynamics. This mean-field description is achieved via an analytic dimensionality reduction of the network dynamics that allows for an efficient characterization of the system attractors and their dependence not only on the degree distribution but also on the degree correlations. Then, motivated by applications of the KM to the alignment of members in a two-dimensional swarm, we construct a Generalized Kuramoto Model (GKM) that extends the KM to arbitrary dimensions. Like the KM, the GKM in even dimensions continues to demonstrate a transition to coherence at a positive critical coupling strength. However, in odd dimensions the transition to coherence occurs discontinuously as the coupling strength is increased through 0. In contrast to the unique stable incoherent equilibrium for the KM, we find that for even dimensions larger than 2 the GKM displays a continuum of different possible pretransition incoherent equilibria, each with distinct stability properties, leading to a novel phenomenon, which we call `Instability-Mediated Resetting.' To aid the analysis of such systems, we construct an exact dimensionality reduction technique with applicability to not only the GKM, but also other similar systems with high-dimensional agents beyond the GKM.