Comparison of DA-based Stochastic Algorithms (Pezo et al. 2014)

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" ... Here we review and test a set of the most recently published DA (Langevin-based Diffusion Approximation) implementations (Goldwyn et al., 2011; Linaro et al., 2011; Dangerfield et al., 2012; Orio and Soudry, 2012; Schmandt and Galán, 2012; Güler, 2013; Huang et al., 2013a), comparing all of them in a set of numerical simulations that asses numerical accuracy and computational efficiency on three different models: the original Hodgkin and Huxley model, a model with faster sodium channels, and a multi-compartmental model inspired in granular cells. ..."
1 . Pezo D, Soudry D, Orio P (2014) Diffusion approximation-based simulation of stochastic ion channels: which method to use? Front Comp Neurosci 8:139 [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): Dentate gyrus granule cell; Squid axon;
Channel(s): I Na,t; I K;
Gap Junctions:
Simulation Environment: NEURON; Python;
Model Concept(s):
Implementer(s): Orio, Patricio [patricio.orio at];
Search NeuronDB for information about:  Dentate gyrus granule cell; I Na,t; I K;
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