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.
Model Type: Neuron or other electrically excitable cell
Region(s) or Organism(s): Neocortex
Cell Type(s): Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex U1 L5B pyramidal pyramidal tract GLU cell
Model Concept(s): Ion Channel Kinetics; Simplified Models; Methods; Markov-type model
Simulation Environment: NEURON; C or C++ program; Python
Implementer(s): Linaro, Daniele [daniele.linaro at unige.it]
References:
Linaro D, Storace M, Giugliano M. (2011). Accurate and fast simulation of channel noise in conductance-based model neurons by diffusion approximation. PLoS computational biology. 7 [PubMed]