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 [T varargout] = readfiletocells(filename)
% READFILETOCELLS reads a text file in which every line contains the times
% at which a neuron fired.
%
% T = readfiletocells(filename)
% [T N] = readfiletocells(filename)
% [T N Tlim] = readfiletocells(filename)
%
% Arguments:
%   filename - the name of the file.
% 
% Returns:
%   T - an array of cells containing the spike times. The number of cells
%   is equal to the number of lines in the file.
%   N - an array containing the number of spikes fired by each neuron.
%   Tlim - an array containing the times of the last spike fired by each
%   neuron.
% 

%
%   Author: Daniele Linaro - August 2009
%

fid = fopen(filename,'r');
T = {};
row = 1;
l = fgetl(fid);
while l ~= (-1)
    [t,r] = strtok(l);
    if isempty(t)
        break;
    end
    ind = 1;
    T{row}(ind) = str2double(t);
    ind = ind + 1;
    while ~ isempty(r)
        [t,r] = strtok(r);
        if isempty(t)
            break;
        end
        T{row}(ind) = str2double(t);
        ind = ind + 1;
    end
    row = row+1;
    l = fgetl(fid);
end
fclose(fid);
T = T(:);

if nargout == 2
    N = zeros(length(T),1);
    for ii=1:length(T)
        N(ii) = length(T{ii});
    end
    varargout{1} = N;
end

if nargout == 3
    Tlim = [1e20 -1e20];
    for ii=1:length(T)
        if T{ii}(1) < Tlim(1)
            Tlim(1) = T{ii}(1);
        end
        if T{ii}(end) > Tlim(2)
            Tlim(2) = T{ii}(end);
        end
    end
    varargout{2} = Tlim;
end


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