Accurate and fast simulation of channel noise in conductance-based model neurons (Linaro et al 2011)

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Accession:127992
We introduce and operatively present a general method to simulate channel noise in conductance-based model neurons, with modest computational overheads. Our approach may be considered as an accurate generalization of previous proposal methods, to the case of voltage-, ion-, and ligand-gated channels with arbitrary complexity. We focus on the discrete Markov process descriptions, routinely employed in experimental identification of voltage-gated channels and synaptic receptors.
Reference:
1 . Linaro D, Storace M, Giugliano M (2011) Accurate and fast simulation of channel noise in conductance-based model neurons by diffusion approximation. PLoS Comput Biol 7:e1001102 [PubMed]
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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: Neocortex;
Cell Type(s): Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex U1 L5B pyramidal pyramidal tract GLU cell;
Channel(s): I Na,t; I K;
Gap Junctions:
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Simulation Environment: NEURON; C or C++ program; Python;
Model Concept(s): Ion Channel Kinetics; Simplified Models; Methods; Markov-type model;
Implementer(s): Linaro, Daniele [daniele.linaro at unige.it];
Search NeuronDB for information about:  Neocortex U1 L5B pyramidal pyramidal tract GLU cell; Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; I Na,t; I K;
 
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