The physical architecture of neural circuits is thought to underlie the computations that give rise to higher order feature sensitivity in the neocortex. Recent technological breakthroughs have allowed the structural and functional investigation of the basic computational units of neural circuits; individual synaptic connections. However, it remains unclear how cortical neurons sample and integrate the thousands of synaptic inputs, supplied by different brain structures, to achieve feature selectivity. Here, I first describe how visual cortical circuits transform the elementary inputs supplied by the periphery into highly diverse, but well-organized, feature representations. By combining and optimizing newly developed techniques to map the functional synaptic connections with defined sources of inputs, I show that the intersection between columnar architecture and dendritic sampling strategies can lead to the selectivity properties of individual neurons: First, in the canonical feedforward circuit, the basal dendrites of a pyramidal neuron utilize unique strategies to sample ON (light increment) and OFF (light decrement) inputs in orientation columns to create the distinctive receptive field structure that is responsible for basic sensitivity to visual spatial location, orientation, spatial frequency, and phase. Second, for long-range horizontal connections, apical dendrites unbiasedly integrate functionally specialized and spatially targeted inputs in different orientation columns, which generates specific axial surround modulation of the receptive field. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

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Dendritic shapes are commonplace in the natural world such as trees, lichens, coral and lightning. Models of dendritic shapes are widely needed in many areas. Because of their branching fractal and erratic structures modeling dendritic shapes is a tricky task. Existing methods for modeling dendritic shapes are slow and complicated.<p>In this thesis we present a procedural algorithm of using path planning to model dendritic shapes. We generate a dendrite by finding the least-cost paths from multiple endpoints to a common generator and use the dendrite to build the geometric model. With the control handles of endpoint placement, fractal shape, edge weights distribution and path width, we create different shapes of dendrites that simulate different kinds of dendritic shapes very well. Compared with some existing methods, our algorithm is fast and simple.

Dendritic shapes are commonplace in the natural world such as trees, lichens, coral and lightning. Models of dendritic shapes are widely needed in many areas. Because of their branching fractal and erratic structures modeling dendritic shapes is a tricky task. Existing methods for modeling dendritic shapes are slow and complicated.<p>In this thesis we present a procedural algorithm of using path planning to model dendritic shapes. We generate a dendrite by finding the least-cost paths from multiple endpoints to a common generator and use the dendrite to build the geometric model. With the control handles of endpoint placement, fractal shape, edge weights distribution and path width, we create different shapes of dendrites that simulate different kinds of dendritic shapes very well. Compared with some existing methods, our algorithm is fast and simple.

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The establishment of neuronal polarity constitutes a central phase in neuronal development and synaptogenesis. In my thesis, I study factors that regulate the development of neuronal polarity and its relationship with neurotransmitter receptor expression. For my experiments, I have investigated the development of sensory neurons from neonatal rat nodose ganglia in culture. Sensory neurons have a pseudo-unipolar morphology, do not extend dendrites, and are devoid of synaptic connections on their somata. However, nodose neurons form synapses de novo in cultures, and I show that the neurons have retained the ability to extend dendrites. Extrinsic factors control dendrite extension by these neurons: the ganglionic satellite cells inhibit the growth of dendrites and induce the neurons to develop a unipolar morphology. In the absence of satellite cells, nodose neurons establish a new multipolar morphology and, in response to nerve growth factor (NGF), extend several dendrites. However, brain-derived neurotrophic factor (BDNF) and neurotrophin-3 (NT-3) do not induce the neurons to extend dendrites, but promote the expression of properties typical of nodose neurons in vivo. / As nodose neurons acquire a new dendrite-axonal polarity in the presence of NGF, they increase the density of functional neuronal nicotinic acetylcholine receptors (nAChRs) on their somato-dendritic domains. To learn more about the relationship between dendrites extension and nAChR gene expression, I have examined the changes in transcript levels of nAChR subunits in neonatal rat sympathetic neurons developing in culture. I show that the developmental pattern of nAChR subunit expression in the cultured neurons follows closely that of sympathetic neurons developing in vivo, with the exception of one specific subunit $ alpha sb7$. I show that the increase in $ alpha sb3$ mRNA levels correlates well with an increase in the density of functional nAChRs on the neurons. In addition, my results suggest that these increases are regulated by mechanisms intrinsic to neonatal sympathetic neurons. On the other hand, the changes in $ alpha sb7$ gene expression, which correlate with changes in $ alpha$-bungarotoxin binding, are activity-dependent and regulated by a calcium/calmodulin-dependent protein kinase pathway. The results presented in this thesis provide insights on how neurons are influenced in their extension of dendrites and how this extension affects neurotransmitter receptor expression.

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Dendrites are tree-like topological spaces, and in this thesis, the physical characteristics of various random fractal versions of this type of set are investigated. This work will contribute to the development of analysis on fractals, an area which has grown considerably over the last twenty years. First, a collection of random self-similar dendrites is constructed, and their Hausdorff dimension is calculated. Previous results determining this quantity for random self-similar structures have often relied on the scaling factors being bounded uniformly away from zero. However, using a percolative argument, and taking advantage of the tree-like structure of the sets considered here, it is shown that this condition is not necessary; a simple condition on the tail of the distribution of the scaling factors at zero is all that is assumed. The scaling factors of these recursively defined structures form what is known as a multiplicative cascade, and results about the height of this random object are also obtained. With important physical and probabilistic applications, the heat equation has justifiably received a substantial amount of attention in a variety of settings. For certain types of fractals, it has become clear that a key factor in estimating the heat kernel is the volume growth with respect to the resistance metric on the space. In particular, uniform polynomial volume growth, which occurs for many deterministic self-similar fractals, immediately implies uniform (on-diagonal) heat kernel behaviour. However, in the random fractal setting, this is frequently not the case, and volume fluctuations are often observed. Motivated by this, an analysis of how volume fluctuations lead to corresponding heat kernel fluctuations for measure-metric spaces equipped with a resistance form is conducted here. These results apply to the aforementioned random self-similar dendrites, amongst other examples. The continuum random tree (CRT) of Aldous is an important random example of a measure-metric space, and fits naturally into the framework of the previous paragraph. In this thesis, quenched (almost-sure) volume growth asymptotics for the CRT are deduced, which show that the behaviour in almost-every realisation is not uniform. Applying the results introduced above, these yield heat kernel bounds for the CRT, demonstrating that heat kernel fluctuations occur almost-surely. Finally, a new representation of the CRT as a random self-similar dendrite is presented.

Thesis (M.S.)--University of Nevada, Reno, 2008. / "January 2008." Includes bibliographical references (leaves 41-43). Online version available on the World Wide Web.