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Nowadays, Virtual Reality (VR) applications and researches have been intensively applied to neuroscience studies, since VR can assist neuroscientists to experiment and analyze simulations results. This paper proposes a virtual reality environment to visualize morphologically realistic neural networks simulation, including features such as plasticit...
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Neuron cell are built from a myriad of axon and dendrite structures. It transmits electrochemical signals between the brain and the nervous system. Three-dimensional visualization of neuron structure could help to facilitate deeper understanding of neuron and its models. An accurate neuron model could aid understanding of brain's functionalities, diagnosis and knowledge of entire nervous system. Existing neuron models have been found to be defective in the aspect of realism. Whereas in the actual biological neuron, there is continuous growth as the soma extending to the axon and the dendrite; but, the current neuron visualization models present it as disjointed segments that has greatly mediated effective realism. In this research, a new reconstruction model comprising of the Bounding Cylinder, Curve Interpolation and Gouraud Shading is proposed to visualize neuron model in order to improve realism. The reconstructed model is used to design algorithms for generating neuron branching from neuron SWC data. The Bounding Cylinder and Curve Interpolation methods are used to improve the connected segments of the neuron model using a series of cascaded cylinders along the neuron's connection path. Three control points are proposed between two adjacent neuron segments. Finally, the model is rendered with Gouraud Shading for smoothening of the model surface. This produce a near-perfection model of the natural neurons with attended realism. The model is validated by a group of bioinformatics analysts' responses to a predefined survey. The result shows about 82% acceptance and satisfaction rate.
This study is focused on Gouraud Shading’s approach to improve appearance of neuron visualization. Neuron visualization is a computational tool that is able to describe, generate, store and render large set of three-dimensional neuronal morphology in a format that is compact, quantitative, and readily accessible to the neuroscientists. This tool enlightens its ability as a powerful computational modeling of neuronal morphology to explore greater understanding in neuron developmental processes and structure-function relationships. However, after a thorough investigation, one of the problems discovered in neuron structure prediction is related to misleading in generating digitalized neuron raw data toward realistic neuron morphology visualization. For that reason, many approaches have been proposed in previous studies in order to perform such visualization based on stochastic sampling data of morphological measures from digital reconstructions of real neuron cells. Therefore, comparison among these approaches has been conducted to recognize a suitable approach. It is still at a
