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

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Accession:146950
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. ..."
Reference:
1 . McDougal RA, Hines ML, Lytton WW (2013) Water-tight membranes from neuronal morphology files. J Neurosci Methods 220:167-78 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; C or C++ program; Python; Cython;
Model Concept(s): Methods;
Implementer(s): McDougal, Robert [robert.mcdougal at yale.edu];
 
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ctng
geometry3d
readme.txt
ctng.py
                            
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