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

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Accession:136808
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.
Reference:
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 V1 L6 pyramidal corticothalamic GLU cell; Neocortex V1 L2/6 pyramidal intratelencephalic 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:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: GENESIS; MATLAB;
Model Concept(s): Parameter Fitting; Simplified Models; Parameter sensitivity;
Implementer(s): Lepora, Nathan [n.lepora at shef.ac.uk];
Search NeuronDB for information about:  Neocortex V1 L6 pyramidal corticothalamic GLU cell; Neocortex V1 L2/6 pyramidal intratelencephalic GLU cell; I Na,t; I L high threshold; I T low threshold; I K; I M;
//===================================================
// Channels for minimum HH models in Popischill 2008
// modified from genesis/scripts/neuron/channels.g
// note: used l'hopital rule on tabchannel defs 
// remove singularity to prevent runtime error 
//===================================================

float ECa       = 0.120
float Vx 		= 2e-3    

//=================================================
//           CaL CHANNEL (Reuveni 1993)
//=================================================
function make_CaL
    create tabchannel CaL
    setfield ^ Ek {ECa} Gbar {1} Xpower 2 Ypower 1 Zpower 0

	int   xdivs=5000
    float xmin=-0.100, xmax=0.050, dx={(xmax-xmin)/xdivs}
    call CaL TABCREATE X {xdivs} {xmin} {xmax}
	call CaL TABCREATE Y {xdivs} {xmin} {xmax}

	float valX_A, valX_B, valY_A, valY_B, Vm
	int i		 
    for (i=0; i<={xdivs}; i=i+1)
        Vm = xmin + i*dx
        valX_A = -0.055e6*(Vm+Vx+27e-3)/({exp {(Vm+Vx+27e-3)/-3.8e-3}}-1)
        valX_B = 0.94e3*{exp {(Vm+Vx+75e-3)/-17e-3}} 
        valY_A = 0.000457e3*{exp {(Vm+Vx+13e-3)/-50e-3}} 
        valY_B = 0.0065e3/({exp {(Vm+Vx+15e-3)/-28e-3}}+1)
        if (({exp {(Vm+Vx+27e-3)/-3.8e-3}}-1)==0)
            valX_A = -0.055e6/(1/-3.8e-3*{exp {(Vm+Vx+27e-3)/-3.8e-3}})
        end
        setfield CaL X_A->table[{i}] {valX_A} X_B->table[{i}] {valX_B}
        setfield CaL Y_A->table[{i}] {valY_A} Y_B->table[{i}] {valY_B}
    end
	tweakalpha CaL X
	tweakalpha CaL Y
end

// make channel
make_CaL


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