Thesis (Ph.D.)--University of Wollongong, 2005. / Typescript. Released for public view from 10 November 2008. Includes bibliographical references: leaf 136-146.

Performance motor errors trigger animals’ adaptive learning behaviors to improve the accuracy and efficiency of the movement. The cerebellum is one of the key brain centers for encoding motor performance and motor learning. Climbing fibers relay information related to motor errors to the cerebellar cortex, evoking elevation of intracellular Ca2+ signals at Purkinje cell dendrites and inducing plasticity at coactive parallel fiber synapses, ultimately recalibrating sensorimotor associations to alter behavior. Molecular layer interneurons (MLIs) inhibit Purkinje cells to modulate dendritic excitability and action potential output. How MLIs contribute to the regulation and encoding of climbing fiber-evoked adaptive movements remains poorly understood. In this dissertation, I used genetic tools to manipulate the activity of MLIs while monitoring Purkinje cell dendritic activity during a cerebellum-dependent motor learning task with different contexts to evaluate how MLIs are involved in this process. The results show that by suppressing dendritic Ca2+ signals in Purkinje cells, MLI activity coincident with climbing fiber-mediated excitation prevents the occurrence of learning when adaptation is not necessary. On the other hand, with error signals present, disinhibition onto Purkinje cells, mediated by MLI-MLI microcircuit, unlocked the ability of climbing fibers to induce plasticity and motor learning. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

Book Summary: Computational models of neural networks have proven insufficient to accurately model brain function, mainly as a result of simplifications that ignore the physical reality of neuronal structure in favor of mathematically tractable algorithms and rules. Even the more biologically based "integrate and fire" and "compartmental" styles of modeling suffer from oversimplification in the former case and excessive discretization in the second. This book introduces an integrative approach to modeling neurons and neuronal circuits that retains the integrity of the biological units at all hierarchical levels. With contributions from more than 40 renowned experts, Modeling in the Neurosciences, Second Edition is essential for those interested in constructing more structured and integrative models with greater biological insight. Focusing on new mathematical and computer models, techniques, and methods, this book represents a cohesive and comprehensive treatment of various aspects of the neurosciences from the molecular to the network level. Many state-of-the-art examples illustrate how mathematical and computer modeling can contribute to the understanding of mechanisms and systems in the neurosciences. Each chapter also includes suggestions of possible refinements for future modeling in this rapidly changing and expanding field. This book will benefit and inspire the advanced modeler, and will give the beginner sufficient confidence to model a wide selection of neuronal systems at the molecular, cellular, and network levels.

Book Summary: With contributions from more than 40 renowned experts, Modeling in the Neurosciences: From Ionic Channels to Neural Networks is essential for those interested in neuronal modeling and quantitative neiroscience. Focusing on new mathematical and computer models, techniques and methods, this monograph represents a cohesive and comprehensive treatment of various aspects of the neurosciences from the biophysical, cellular and netwrok levels. Many state-of-the-art examples are presented as to how mathematical and computer modeling can contribute to the understanding of mechanisms and systems in the neurosciences. Each chapter also includes suggestions of possible refinements for future modeling in this rapidly changing and expanding field. This book will benefit and inspire the advanced modeler, and give the beginner sufficient confidence to model a wide selection of neuronal systems at the biophysical, cellular and network levels.

Powder bed fusion (PBF) is a metal additive manufacturing process that is increasingly used in the aerospace and medical industry to build complex parts directly from computer-aided design. Due to the presence of large temperature gradients and rapid cooling rates during the processing, the PBF process is assumed to follow a rapid solidification processing route. However, the extent of deviation of the solid-liquid interface from equilibrium as a function of processing conditions has not been studied in detail for the PBF process. In this thesis, a numerical model is developed to study the interfacial characteristics as a function of processing conditions to characterize if the PBF process exhibits rapid solidification or not. The model is based on the work of Hunt et al. [1, 2, 3] and is capable of simulating cellular and dendritic growth at both low and high interface velocities. The developed model accounts for the various undercooling such as constitutional and curvature undercooling, the variation of the liquidus temperature with composition, and the partition coefficient and diffusion coefficient with temperature. Moreover, the variation of the partition coefficient and the liquidus slope with the growth velocity has also been considered in the developed model. The model is used to predict the range of primary cellular/dendritic spacing for a given set of input parameters. In addition to this, the tip undercooling, tip Péclet number and spacing Péclet numbers have also been estimated using the model to quantify the extent of deviation of the solid-liquid interface from equilibrium. A good qualitative agreement between the predicted values from the numerical model and the analytical KGT model is achieved. This new model can be used to understand the relationship between the processing conditions, material system and interfacial characteristics during the PBF process, and thus improve microstructural development during PBF processing. / Thesis / Master of Science in Materials Science and Engineering (MSMSE)

Thesis (Ph. D.)--University of Alabama at Birmingham, 2008. / Title from first page of PDF file (viewed June 10, 2008). Includes bibliographical references.

La rétraction des dendrites de cellules ganglionnaires de la rétine (CGR) est parmi les changements pathologiques qui conduisent à des déficits fonctionnels lors du glaucome. Récemment, on a montré que l’administration de l’insuline promeut une importante régénération des dendrites des cellules ganglionnaires de la rétine et rétablie les synapses. On se basant sur ces données, on a posé les questions suivantes: 1) Est ce que la réduction de la pression intraoculaire (PIO) élevée est suffisante pour promouvoir la régénération des dendrites en absence d’apport exogène de l’insuline? 2) Quels sont les mécanismes moléculaires en aval de l’insuline qui permettent la régénération des dendrites des CGR lors du glaucome? Les souris transgéniques Thy1-YFP, qui permettent la visualisation des dendrites des CGR, ont reçu une injection intra-camérale de microbilles magnétiques pour induire l’hypertension oculaire. Des gouttes journalières du brinzolamide ont été administrées pour réduire la PIO. Les CGR ont été imagés à l’aide du microscope confocal et les dendrites ont été reconstruites en 3D grâce au logiciel Imaris. Pour l’analyse des mécanismes moléculaires, les CGR ont été isolées grâce à la technique de cytométrie FACS, à partir des rétines traitées à l’insuline et au véhicule suivi par un séquençage d’ARN (ARNseq). Le brinzolamide réduit de façon effective la PIO, cependant cette réduction ne permet pas la régénération des dendrites des CGR. Le séquençage de l’ARN des rétines glaucomateuses et des rétines contrôles a aidé à identifier des voies de signalisation candidates pour participer à la régénération des dendrites des CGR incluant mTOR, Notch, glycolyse, métabolisme des acides gras, réparations d’ADN et myc-cibles. Ces données nous ont conduit à retirer les conclusions suivantes: 1) La réduction de la PIO n’est pas suffisant pour promouvoir la régénération IV des dendrites des CGR, suggérant que l’insuline endogène ne remplit pas le rôle de l’insuline exogène. 2) De nombreuses voies moléculaires sont activées pour mener l'effet régénérateur de l’insuline sur les dendrites des CGR. Ces résultats supportent le rôle de l’administration de l’insuline pour restaurer les connections et le fonctionnement de la rétine et identifient des gènes qui pourraient être de nouvelles cibles pour traiter le glaucome. / Glaucoma is the leading cause of irreversible blindness worldwide. High intraocular pressure (IOP) is the most important risk factor to develop the disease. The retraction of retinal ganglion cell (RGC) dendrites is one of the earliest pathological changes leading to substantial functional deficits. We recently demonstrated that insulin, administered after arbor retraction, promoted remarkable RGC dendrite and synapse regeneration. Here, we asked the following questions: 1) is insulin effective at promoting RGC dendrite regeneration in experimental glaucoma? 2) is reduction of IOP sufficient to promote dendrite regeneration in the absence of exogenous insulin? 3) what are the signaling components downstream of insulin that stimulate RGC dendrite regeneration in glaucoma? Thy1-YFP mice, which allow visualization of RGC dendritic arbors, received an intracameral injection of magnetic microbeads to induce ocular hypertension. RGC dendrites were imaged by confocal microscopy and arbors were 3D reconstructed. Total RGC dendritic length and complexity increased in glaucomatous eyes treated with insulin to values similar to those found in intact non-injured controls, but not in eyes treated with brinzolamide, to lower IOP, or vehicle. RGCs were isolated by Fluorescence Activated Cell Sorting (FACS) from insulin- or vehicle-treated glaucomatous retinas as well as shamoperated controls, followed by RNA sequencing analysis (RNA-seq). Our data show a global decrease in transcriptional efficiency in glaucomatous retinas. In addition, we identified a number of key regulatory pathways potentially implicated in insulin-induced RGC dendrite regeneration including: the mammalian target of rapamycin (mTOR), glycolysis, fatty acid metabolism, DNA repair, and myc-targets. These data allow us to draw the following conclusions: 1) insulin promotes robust RGC dendrite regeneration in glaucoma, 2) IOP reduction alone is not sufficient to promote dendritic regrowth, and 3) multiple molecular pathways are activated during insulin-mediated regeneration. These findings support a critical role for insulin administration to restore RGC dendritic structure, and identify differential gene expression that might reveal novel therapeutic targets for glaucoma.

In this thesis, I have studied CMOS dendrites, implemented them on a reconfigurable analog platform and modeled them using MATLAB Simulink. The dendrite model was further used to build a computational model. I implemented a Hidden Markov Model (HMM) classifier to build a simple YES/NO wordspotter. I also discussed the inter-relation between neural systems, CMOS transistors and HMM networks. The physical principles behind the operation of silicon devices and biological structures are similar. Hence silicon devices can be used to emulate biological structures like dendrites. Dendrites are a branched, conductive medium which connect a neurons synapses to its soma. Dendrites were previously believed to be like wires in neural networks. However, recent research suggests that they have computational power. We can emulate dendrites using transistors in the Field Programmable Analog Array (FPAA). Our lab has built the Reconfigurable Analog Signal Processor (RASP) family of FPAAs which was used for the experiments. I analytically compared the mathematical model of dendrites to our model in silicon. The mathematical model based on the device physics of the silicon devices was then used to simulate dendrites in Simulink. An automated tool, sim2spice was then used to convert the Simulink model into a SPICE netlist, such that it can be implemented on a FPAA. This is an easier tool to use for DSP and Neuromorphic engineers who's primary areas of expertise isn't circuit design.

Many decades ago, Carver Mead established the foundations of neuromorphic systems. Neuromorphic systems are analog circuits that emulate biology. These circuits utilize subthreshold dynamics of CMOS transistors to mimic the behavior of neurons. The objective is to not only simulate the human brain, but also to build useful applications using these bio-inspired circuits for ultra low power speech processing, image processing, and robotics. This can be achieved using reconfigurable hardware, like field programmable analog arrays (FPAAs), which enable configuring different applications on a cross platform system. As digital systems saturate in terms of power efficiency, this alternate approach has the potential to improve computational efficiency by approximately eight orders of magnitude. These systems, which include analog, digital, and neuromorphic elements combine to result in a very powerful reconfigurable processing machine.

Activity-dependent dendritic development is an important process in the maturation of neuronal circuits. The precise morphology of a neuron’s dendritic tree dictates which other cells it is able to interact with and how it will receive and process synaptic information. The aim of this Thesis was to investigate the mechanisms by which dendrites grow and, in particular, how changes in intracellular ion concentrations contribute to these mechanisms. One important activity-dependent signal is calcium as it can translate neuronal activity into morphological changes. Despite this, very little is known about calcium signalling during the period of dendritic development. Using single-cell electroporation of immature CA1 hippocampal pyramidal neurons, I characterised the spatial and temporal properties of local calcium transients in growing dendrites. This revealed a high frequency of transients at shaft filopodia and stable branchpoints, but an almost complete absence from the tips of dendritic branches. Another important factor during development is the intracellular chloride concentration because this regulates neuronal excitability. Prematurely lowering intracellular chloride by expressing the chloride co-transporter KCC2 led to less stable dendritic filopodia and stunted dendritic growth. These effects were independent of local calcium signalling and suggested that chloride regulation itself may be fundamental to normal dendritic growth. To examine this further I developed imaging techniques to measure the spatial and temporal dynamics of chloride in growing dendrites. This work revealed a somatodendritic gradient of increasing intracellular chloride, whereby the highest concentrations were found at sites of growth. Further analysis suggested a close link between local chloride regulation and morphological changes. The dendritic tips that exhibited high intracellular chloride levels and the potential to rapidly modulate these levels, also exhibited the greatest morphological dynamics. These findings have important implications for understanding the mechanisms of dendritic growth and establish the spatiotemporal regulation of chloride as a key parameter.