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 ENa       =  0.050
float V_T  		= -65e-3

//=================================================
//           NA CHANNEL (Traub, Miles 1991)
//=================================================
function make_Na
     create tabchannel Na
     setfield ^ Ek {ENa} Gbar {500} Xpower 3 Ypower 1 Zpower 0
    
	 int   xdivs=5000
	 float xmin=-0.100, xmax=0.050, dx={(xmax-xmin)/xdivs}
	 call Na TABCREATE X {xdivs} {xmin} {xmax}
	 call Na 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 = -0.32e6*(Vm-V_T-13e-3)/({exp {(Vm-V_T-13e-3)/-4e-3}}-1)
		 valX_B = 0.28e6*(Vm-V_T-40e-3)/({exp {(Vm-V_T-40e-3)/5e-3}}-1)
		 valY_A = 0.128e3*{exp {(Vm-V_T-17e-3)/-18e-3}} 
		 valY_B = 4e3/({exp {(Vm-V_T-40e-3)/-5e-3}}+1)
		 if ({({exp {(Vm-V_T-13e-3)/-4e-3}}-1)}==0)
	    	 valX_A = -0.32e6/(1/-4e-3*{exp {(Vm-V_T-13e-3)/-4e-3}})
		 end
		 if ({({exp {(Vm-V_T-40e-3)/5e-3}}-1)}==0)
	    	 valX_B = 0.28e6/(1/5e-3*{exp {(Vm-V_T-40e-3)/5e-3}})
		 end
		 setfield Na X_A->table[{i}] {valX_A} X_B->table[{i}] {valX_B}
		 setfield Na Y_A->table[{i}] {valY_A} Y_B->table[{i}] {valY_B}
	 end
	 tweakalpha Na X
	 tweakalpha Na Y
end

// make channel
make_Na

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