Using watertight polygon meshes for neuronal morphology representation

I will report on the progress of my work to use polygon meshes as a way to represent neuronal morphologies. The presentation is composed of two parts. The first part discusses the acquisition of neuronal morphology data from microscopy and the generation of computer representations using the SWC format. It is intended to provide a background on the second part of the presentation, which focuses on using watertight polygon meshes as a way to represent neuronal morphology. We shall cover the advantages of this approach and the difficulties faced. In this context shall discuss the paper by McDougal et al. [1]

[1] McDougal, Robert A., Michael L. Hines, and William W. Lytton. "Water-tight membranes from neuronal morphology files." Journal of neuroscience methods 220.2 (2013): 167-178.

Date: 29/04/2016
Time: 16:00
Location: LB252

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Associative memory olfaction Deep learning Artificial intelligence Studentship Neurons Evolutionary algorithms PCA cerebellum Neuronal Morphology Purkinje cell Software development computational neuroscience Convolutional Neural Networks Structural plasticity Persistent homology Neuromorphic hardware Computer Science optimization Homoeostasis robotics Dendritic computation computational modelling neural networks machine learning neuroscience synaptic plasticity Network connectivity COVID-19 Open position