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;
// 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 EK        = -0.090
float V_T  		= -65e-3

//           Kd CHANNEL (Traub, Miles 1991)
function make_Kd 
    create tabchannel Kd
    setfield ^ Ek {EK} Gbar {50} Xpower 4 Ypower 0 Zpower 0

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

	float valX_A, valX_B, Vm
	int i		 
	for (i=0; i<={xdivs}; i=i+1)
		Vm = xmin + i*dx
		valX_A = -0.032e6*(Vm-V_T-15e-3)/({exp {(Vm-V_T-15e-3)/-5e-3}}-1)
		valX_B = 0.5e3*{exp {(Vm-V_T-10e-3)/-40e-3}} 
		if ({({exp {(Vm-V_T-15e-3)/-5e-3}}-1)}==0)
	    	 valX_A = -0.032e6/(1/-5e-3*{exp {(Vm-V_T-15e-3)/-5e-3}})
		setfield Kd X_A->table[{i}] {valX_A} X_B->table[{i}] {valX_B}
	tweakalpha Kd X

// make channel

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