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

" ... 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. ..."

Model Type: Neuron or other electrically excitable cell

Cell Type(s): Dentate gyrus granule GLU cell; Squid axon

Currents: I Na,t; I K

Simulation Environment: NEURON; Python

Implementer(s): Orio, Patricio [patricio.orio at]


Pezo D, Soudry D, Orio P. (2014). Diffusion approximation-based simulation of stochastic ion channels: which method to use? Frontiers in computational neuroscience. 8 [PubMed]

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