Constructed Tessellated Neuronal Geometries (CTNG) (McDougal et al. 2013)

We present an algorithm to form watertight 3D surfaces consistent with the point-and-diameter based neuronal morphology descriptions widely used with spatial electrophysiology simulators. ... This (point-and-diameter) representation is well-suited for electrophysiology simulations, where the space constants are larger than geometric ambiguities. However, the simple interpretations used for pure electrophysiological simulation produce geometries unsuitable for multi-scale models that also involve three-dimensional reaction–diffusion, as such models have smaller space constants. ... Although one cannot exactly reproduce an original neuron's full shape from point-and-diameter data, our new constructive tessellated neuronal geometry (CTNG) algorithm uses constructive solid geometry to define a plausible reconstruction without gaps or cul-de-sacs. CTNG then uses “constructive cubes” to produce a watertight triangular mesh of the neuron surface, suitable for use in reaction–diffusion simulations. ..."

Model Type: Neuron or other electrically excitable cell

Model Concept(s): Methods

Simulation Environment: NEURON; C or C++ program; Python; Cython

Implementer(s): McDougal, Robert [robert.mcdougal at]


McDougal RA, Hines ML, Lytton WW. (2013). Water-tight membranes from neuronal morphology files. Journal of neuroscience methods. 220 [PubMed]

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