Stochastic Ih and Na-channels in pyramidal neuron dendrites (Kole et al 2006)

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Accession:64195
The hyperpolarization-activated cation current (Ih) plays an important role in regulating neuronal excitability, yet its native single-channel properties in the brain are essentially unknown. Here we use variance-mean analysis to study the properties of single Ih channels in the apical dendrites of cortical layer 5 pyramidal neurons in vitro. ... In contrast to the uniformly distributed single-channel conductance, Ih channel number increases exponentially with distance, reaching densities as high as approximately 550 channels/microm2 at distal dendritic sites. These high channel densities generate significant membrane voltage noise. By incorporating a stochastic model of Ih single-channel gating into a morphologically realistic model of a layer 5 neuron, we show that this channel noise is higher in distal dendritic compartments and increased threefold with a 10-fold increased single-channel conductance (6.8 pS) but constant Ih current density. ... These data suggest that, in the face of high current densities, the small single-channel conductance of Ih is critical for maintaining the fidelity of action potential output. See paper for more and details.
Reference:
1 . Kole MH, Hallermann S, Stuart GJ (2006) Single Ih channels in pyramidal neuron dendrites: properties, distribution, and impact on action potential output. J Neurosci 26:1677-87 [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;
Channel(s): I h;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Ion Channel Kinetics; Active Dendrites;
Implementer(s): Hallermann, Stefan [hallermann at medizin.uni-leipzig.de];
Search NeuronDB for information about:  Neocortex V1 L6 pyramidal corticothalamic GLU cell; I h;
/
Stochastic
Stochastic_Na
README.txt
ca.mod *
cad.mod *
caT.mod
ih_stochastic.mod
ka.mod
kca.mod *
km.mod *
kv.mod *
na.mod
syn.mod *
fig6B.hoc
fig7D.hoc
mosinit.hoc
Ri18geo.hoc *
Ri18init.hoc
shortRun.hoc
                            
// -------------------------------------------
//initialize user-defined membrane parameters 
//-------------------------------------------

proc init_params() {
	celsius=35
	Ri=100    
	Cm=1     
	Rm=15000 
	v_init=-89	 	// JP corrected RMP under ZD 7288 		
	spinescale=2.0  	// scale total area to account for spines 
	hfactor = 0.000429 // for 2.5 mM K+
	hinvtau = 0.003078  // for 2.5 mM K+
}


//-------------------------------------------------------------------------
// initalise passive properties and add spines by scaling Rm and Cm, NOT scaled for spines: axon, soma, 1st 100 um of apical dend, the 1st 20 um of basals 
//----------------------------------------------------------------------


proc init_spine() {

	forall {insert pas g_pas=1/(Rm/spinescale) cm=Cm*spinescale Ra=Ri 
	}

	axon {g_pas=0.02 }
	soma {g_pas=1/Rm cm=Cm}

	for i=0,14 {
		 dend1[i]  {g_pas=1/Rm cm=Cm}
	}
	for i=15,27 {
		 dend1[i]  {g_pas=1/Rm cm=Cm}
	}
	for i=28,32 {
		 dend1[i]  {g_pas=1/Rm cm=Cm}
	}
	for i=43,47 {
		 dend1[i]  {g_pas=1/Rm cm=Cm}
	}
	for i=71,72 {
		 dend1[i]  {g_pas=1/Rm cm=Cm}
	}
	for i=777,778 {
		 dend1[i]  {g_pas=1/Rm cm=Cm}
	}
 	for i=808,810 {
		 dend1[i]  {g_pas=1/Rm cm=Cm}
	}
	for i=923,924 {
		 dend1[i]  {g_pas=1/Rm cm=Cm}
	}
	for i=957,959 {
		 dend1[i]  {g_pas=1/Rm cm=Cm}
	}
	for i=1006,1007 {
		 dend1[i]  {g_pas=1/Rm cm=Cm}
	}
	for i=1055,1058 {
		 dend1[i]  {g_pas=1/Rm cm=Cm}
	}
	for i=1059,1060 {
		 dend1[i]  {g_pas=1/Rm cm=Cm}
	}
	for i=1067,1069 {
		 dend1[i]  {g_pas=1/Rm cm=Cm}
	}
}

//-------------------------------------------------
// Insert active mod.mechanisms
// --------------------------------------------------

proc init_cell() {

	gna_dend = 27*spinescale 
	gna_node = 30000   
	gna_soma = 54

	vshift_na=-1
 
	gkv_axon = 400
	gkv_soma = 200
	gkv_dend = 25*spinescale 

	gca_dend = .8*spinescale 
	gkm_dend =.05*spinescale
	gkca_dend= 1.5*spinescale
	git2_dend = 0.0005*spinescale
	gkap_dend = 0.008*spinescale 		

	gca_soma = .3 
	gkm_soma = 0.1 
	gkca_soma = 3 

	// the reconstructed axon 
	axon {insert na gbar_na = gna_node
		insert kv gbar_kv = gkv_axon}

	soma {insert na gbar_na = gna_node
 		insert kv gbar_kv = gkv_axon}

	for i=0,13 {
		dend1[i]  {
			insert na gbar_na=gna_node
			insert kv gbar_kv = gkv_axon
		}
	}

	// all dendrites 
	for i=28,1090 {
		dend1[i] {
			insert na 		gbar_na = gna_dend
			insert km 		gbar_km = gkm_dend
		 	insert kca 		gbar_kca = gkca_dend
			insert kv 		gbar_kv = gkv_dend
			insert cad 
			insert ca 		gbar_ca = gca_dend
			insert it2 		gcabar_it2 = git2_dend
			insert kap 		gkabar_kap = gkap_dend
		}
	}

	// specific soma 
	for i=14,27 {
		dend1[i] {
			insert na 		gbar_na = gna_soma
			insert kv 		gbar_kv = gkv_soma
			insert km 		gbar_km = gkm_soma
			insert ca 		gbar_ca = gca_soma
			insert kca 		gbar_kca = gkca_soma
			insert cad
			insert it2
			insert kap 
		} 
	}

	forall {
		ena = 60
	 	ek = -90
	}

	//the caix increased from 1 -> 4, Ra 0.01 -> 0.05 and Rb 0.02 -> 0.1, according to Schaefer et al, 2003 to allow BAC firing
	forall { 	
		caix_kca  = 4
 		Ra_kca    = 0.05
   		Rb_kca    = 0.1
		taur_cad = 100
	}
}


//--------------------------------------------------
// initialise resting Vm
//----------------------------------------------------


proc init_vm() { 
	forall {
		e_pas=v_init
	}
}



// --------------------------------------------------------------------------
// exponential Distribution, identical to patch data (Kole et al., 2006)
// --------------------------------------------------------------------------

proc hexp() {
	forall {insert ih
			gamma_ih=6.8e-12
			gh_ih = -0.0002
			ehd_ih=-45	
			} 
	
	access dend1[21]
	area(0.5)
	distance()
	printf("distributes Ih exponentially with dis from soma\n")
	for i=0,1090 {
	access dend1[i]
	ghdbar_ih=gh_ih+(hfactor*(exp(hinvtau*distance(.5))))
	ghdbar_ih=ghdbar_ih*spinescale	
	}


	// the reconstructed axon 
	axon { ghdbar_ih=0 }
	soma { ghdbar_ih=0 }
	for i=0,13 {
		dend1[i] { ghdbar_ih=0 }
	}


	// the real soma 
	for i=14,27 {
		dend1[i] { ghdbar_ih=ghdbar_ih/spinescale }
	}


	// the 1st 100 um of the apical trunk 
	for i=28,32 {
		dend1[i] { ghdbar_ih=ghdbar_ih/spinescale }
	}
	for i=43,47 {
		dend1[i] { ghdbar_ih=ghdbar_ih/spinescale }
	}
	for i=71,72 {
		dend1[i] { ghdbar_ih=ghdbar_ih/spinescale }
	}


	// the 1st 20 um of the basals 
	for i=777,778 {
		dend1[i] { ghdbar_ih=ghdbar_ih/spinescale }
	}
	for i=808,810 {
		dend1[i] { ghdbar_ih=ghdbar_ih/spinescale }
	}
	for i=923,924 {
		dend1[i] { ghdbar_ih=ghdbar_ih/spinescale }
	}
	for i=957,959 {
		dend1[i] { ghdbar_ih=ghdbar_ih/spinescale }
	}
	for i=1006,1007 {
		dend1[i] { ghdbar_ih=ghdbar_ih/spinescale }
	}
	for i=1055,1058 {
		dend1[i] { ghdbar_ih=ghdbar_ih/spinescale }
	}
	for i=1059,1060 {
		dend1[i] { ghdbar_ih=ghdbar_ih/spinescale }
	}
	for i=1067,1069 {
		dend1[i] { ghdbar_ih=ghdbar_ih/spinescale}
	
	}
}

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