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]
Citations  Citation Browser
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;
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 L5/6 pyramidal GLU cell; I h;
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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
                            
COMMENT

changed from (AS Oct0899)
ca.mod to lead to thalamic ca current inspired by destexhe and huguenrd
Uses fixed eca instead of GHK eqn

LVA Ca?

ENDCOMMENT

INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}

NEURON {
	SUFFIX it2
	USEION ca READ eca WRITE ica
	RANGE m, h, gca, gcabar
	RANGE minf, hinf, mtau, htau, inactF, actF
	GLOBAL  vshift,vmin,vmax, v12m, v12h, vwm, vwh, am, ah, vm1, vm2, vh1, vh2, wm1, wm2, wh1, wh2
}

PARAMETER {
	gcabar = 0.0008 (mho/cm2)	: 0.12 mho/cm2
	vshift = 0	(mV)		: voltage shift (affects all)

	cao  = 2.5	(mM)	        : external ca concentration
	cai		(mM)
						 
	v 		(mV)
	dt		(ms)
	celsius		(degC)
	vmin = -120	(mV)
	vmax = 100	(mV)

	v12m=50         	(mV)
	v12h=78         	(mV)
	vwm =7.4         	(mV)
	vwh=5.0         	(mV)
	am=3         	(mV)
	ah=85         	(mV)
	vm1=25         	(mV)
	vm2=100         	(mV)
	vh1=46         	(mV)
	vh2=405         	(mV)
	wm1=20         	(mV)
	wm2=15         	(mV)
	wh1=4         	(mV)
	wh2=50         	(mV)


}


UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
	(pS) = (picosiemens)
	(um) = (micron)
	FARADAY = (faraday) (coulomb)
	R = (k-mole) (joule/degC)
	PI	= (pi) (1)
} 

ASSIGNED {
	ica 		(mA/cm2)
	gca		(pS/um2)
	eca		(mV)
	minf 		hinf
	mtau (ms)	htau (ms)
	tadj
}
 

STATE { m h }

INITIAL { 
	trates(v+vshift)
	m = minf
	h = hinf
}

BREAKPOINT {
        SOLVE states
        gca = gcabar*m*m*h
	ica = gca * (v - eca)
} 

LOCAL mexp, hexp

PROCEDURE states() {
        trates(v+vshift)      
        m = m + mexp*(minf-m)
        h = h + hexp*(hinf-h)
	VERBATIM
	return 0;
	ENDVERBATIM
}


PROCEDURE trates(v) {  
                      
        LOCAL tinc
        TABLE minf, mexp, hinf, hexp
	DEPEND dt	
	FROM vmin TO vmax WITH 199

	rates(v): not consistently executed from here if usetable == 1

        tinc = -dt 

        mexp = 1 - exp(tinc/mtau)
        hexp = 1 - exp(tinc/htau)
}


PROCEDURE rates(v_) {  
        LOCAL  a, b

	minf = 1.0 / ( 1 + exp(-(v_+v12m)/vwm) )
	hinf = 1.0 / ( 1 + exp((v_+v12h)/vwh) )

	mtau = ( am + 1.0 / ( exp((v_+vm1)/wm1) + exp(-(v_+vm2)/wm2) ) ) 
	htau = ( ah + 1.0 / ( exp((v_+vh1)/wh1) + exp(-(v_+vh2)/wh2) ) ) 
}