Efficient estimation of detailed single-neuron models (Huys et al. 2006)

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Accession:93390
"Biophysically accurate multicompartmental models of individual neurons ... depend on a large number of parameters that are difficult to estimate. ... We propose a statistical approach to the automatic estimation of various biologically relevant parameters, including 1) the distribution of channel densities, 2) the spatiotemporal pattern of synaptic input, and 3) axial resistances across extended dendrites. ... We demonstrate that the method leads to accurate estimations on a wide variety of challenging model data sets that include up to about 10,000 parameters (roughly two orders of magnitude more than previously feasible) and describe how the method gives insights into the functional interaction of groups of channels."
Reference:
1 . Huys QJ, Ahrens MB, Paninski L (2006) Efficient estimation of detailed single-neuron models. J Neurophysiol 96:872-90 [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:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Methods;
Implementer(s):
% MAIN.M
%
% Infer full compartmental model given only access to the voltage in the
% compartments. This code is released in conjunction with the paper 
%
%	Huys QJM, Ahrens M and Paninski L (2006): Efficient estimation of
%	detailed single-neurone models
%
% and can be downloaded from 
%
%	http://www.gatsby.ucl.ac.uk/~qhuys/code.html
%
% This script is the main script. It calls PARAM.M to set the parameters of the
% cell to be simulated and used for inference. GETDATA.M simulates the cell and
% collects all the data needed to infer the parameters of interest. It calls
% GETI.M and also GENKINETCS.M as part of a call to a differential equation
% solver. SETUPQUAD.M sets up the quadratic programme to be solved. This is
% finally solved here.  PLOTS.M plots a small-scale version of figure 8 in the
% paper, and PLOTCELL.M finally plots the cells (you'll need some additional
% software for this, see in the file). 
%
% Copyright Quentin Huys 2006

clear all;
%........................ set up things ....................................
param;			% get parameters
getdata;		% get voltage trace, synaptic input, current etc. 
setupquad;		% set up the matrices M and the vector f

%........................ solve quadratic programme ..........................
fprintf('\n.............. solve quadratic programme\n')

switch minimizer
case 'qp'
	options = optimset('Display','none','MaxIter',5000);
	warning('off','optim:quadprog:SwitchToMedScale');
	warning('off','optim:quadprog:FullAndMedScale');

	a = quadprog(A,f,-eye(size(A)),zeros(size(f)));

case 'minq'
	a = minq(0,f,A,zeros(size(f)),repmat(Inf,size(f)),0);
end

plots;			% generate the plot in the paper

if doplotcell;
	plotcell;	
end