Parameter estimation for Hodgkin-Huxley based models of cortical neurons (Lepora et al. 2011)

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Simulation and fitting of two-compartment (active soma, passive dendrite) for different classes of cortical neurons. The fitting technique indirectly matches neuronal currents derived from somatic membrane potential data rather than fitting the voltage traces directly. The method uses an analytic solution for the somatic ion channel maximal conductances given approximate models of the channel kinetics, membrane dynamics and dendrite. This approach is tested on model-derived data for various cortical neurons.
1 . Lepora NF, Overton PG, Gurney K (2012) Efficient fitting of conductance-based model neurons from somatic current clamp. J Comput Neurosci 32:1-24 [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:
Cell Type(s): Neocortex L5/6 pyramidal GLU cell; Neocortex L2/3 pyramidal GLU cell; Neocortex fast spiking (FS) interneuron; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron;
Channel(s): I Na,t; I L high threshold; I T low threshold; I K; I M;
Gap Junctions:
Simulation Environment: GENESIS; MATLAB;
Model Concept(s): Parameter Fitting; Simplified Models; Parameter sensitivity;
Implementer(s): Lepora, Nathan [n.lepora at];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; Neocortex L2/3 pyramidal GLU cell; I Na,t; I L high threshold; I T low threshold; I K; I M;
%% ============================
% load simulated IV parameters
function [t,Vm,Iinj,len,dia,link] = load_IV(dir_output)

%% read VI simulation data 
data_VI = load([dir_output,'/data_IV.dat']);
ncpt = size(data_VI,2)-2; rcpt = 1:ncpt;

% extract data (vector format)
t1 = data_VI(:,1);
Iinj1 = data_VI(:,2);
Vm1 = data_VI(:,2+rcpt);

% change data to array format (recogise cols from t==0)
n = [find(t1==0); length(t1)+1]; 
nt = min(diff(n))-1; t = t1(1:nt); 
nsim = length(n)-1; 

Iinj = nan*zeros(nt,nsim); Vm = nan*zeros(nt,nsim,ncpt);
for j= 1:nsim
    Iinj(:,j) = Iinj1(n(j):n(j)+nt-1);
    Vm(:,j,:) = Vm1(n(j):n(j)+nt-1,rcpt);

%% read parameters
npar = 6+ncpt; rpar = npar*(rcpt-1);
[ignore object par val] = textread([dir_output,'/data_IV.dat'],'%s%s%s%f',npar*ncpt);
len = val(rpar+5); dia = val(rpar+6);
link = nan*zeros(ncpt); for i = 1:ncpt; link(i,:) = val(rpar+6+i); end


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