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]
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:
Receptor(s):
Gene(s):
Transmitter(s):
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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HHcn
matlab
computeReliabilityAndPrecision.m
rasterplot.m
readfiletocells.m
                            
function rasterplot(spktimes,varargin)
% RASTERPLOT plots a raster plot of spike times.
% 
% rasterplot(spktimes,...)
% 
% Arguments:
%   spktimes - the times at which spikes were fired. It must be an array
%   of cells, where each cell contains the spike times of a single neuron
%   or trial.
% 
% Additional (optional) arguments will be passed unchanged to the function 
% plot.
% 

% 
% Author: Daniele Linaro - September 2009.
% 

hold on;
for ii=1:length(spktimes)
    for jj=1:length(spktimes{ii})
        plot([spktimes{ii}(jj),spktimes{ii}(jj)],[ii-1,ii],varargin{:});
    end
end

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