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 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;
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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
                            
TITLE K-A channel from Klee Ficker and Heinemann
: modified to account for Dax A Current --- M.Migliore Jun 1997
: modified to be used with cvode  M.Migliore 2001

UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)

}

PARAMETER {
	v (mV)
	celsius		(degC)
	gkabar=.008 (mho/cm2)
     	vhalfn=11   (mV)
     	vhalfl=-56   (mV)
     	a0l=0.05      (/ms)
     	a0n=0.05    (/ms)
     	zetan=-1.5    (1)
     	zetal=3    (1)
     	gmn=0.55   (1)
     	gml=1   (1)
	lmin=2  (mS)
	nmin=0.1  (mS)
	pw=-1    (1)
	tq=-40
	qq=5
	q10=5
	qtl=1
	ek
}


NEURON {
	SUFFIX kap
	USEION k READ ek WRITE ik
        RANGE gkabar,gka
        GLOBAL ninf,linf,taul,taun,lmin
}

STATE {
	n
        l
}

ASSIGNED {
	ik (mA/cm2)
        ninf
        linf      
        taul
        taun
        gka
}

INITIAL {
	rates(v)
	n=ninf
	l=linf
}


BREAKPOINT {
	SOLVE states METHOD cnexp
	gka = gkabar*n*l
	ik = gka*(v-ek)

}


FUNCTION alpn(v(mV)) {
LOCAL zeta
  zeta=zetan+pw/(1+exp((v-tq)/qq))
  alpn = exp(1.e-3*zeta*(v-vhalfn)*9.648e4/(8.315*(273.16+celsius))) 
}

FUNCTION betn(v(mV)) {
LOCAL zeta
  zeta=zetan+pw/(1+exp((v-tq)/qq))
  betn = exp(1.e-3*zeta*gmn*(v-vhalfn)*9.648e4/(8.315*(273.16+celsius))) 
}

FUNCTION alpl(v(mV)) {
  alpl = exp(1.e-3*zetal*(v-vhalfl)*9.648e4/(8.315*(273.16+celsius))) 
}

FUNCTION betl(v(mV)) {
  betl = exp(1.e-3*zetal*gml*(v-vhalfl)*9.648e4/(8.315*(273.16+celsius))) 
}

DERIVATIVE states {     : exact when v held constant; integrates over dt step
        rates(v)
        n' = (ninf - n)/taun
        l' =  (linf - l)/taul
}

PROCEDURE rates(v (mV)) { :callable from hoc
        LOCAL a,qt
        qt=q10^((celsius-24)/10)
        a = alpn(v)
        ninf = 1/(1 + a)
        taun = betn(v)/(qt*a0n*(1+a))
	if (taun<nmin) {taun=nmin}
        a = alpl(v)
        linf = 1/(1+ a)
	taul = 0.26*(v+50)/qtl
	if (taul<lmin/qtl) {taul=lmin/qtl}
}