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