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]
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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
                            
: Suffix syn2

COMMENT
synaptic current with exponential rise and decay conductance defined by
        i = g * (v - e)      i(nanoamps), g(micromhos);
        where
         g = 0 for t < onset and
         g=amp*((1-exp(-(t-onset)/tau0))-(1-exp(-(t-onset)/tau1)))
          for t > onset
plus a little bulletproofing and stuff to make it run a mite faster
and make it compatible with cvode
ENDCOMMENT

NEURON {
	POINT_PROCESS syn2
	RANGE onset, tau0, tau1, gmax, e, i
	NONSPECIFIC_CURRENT i
}

UNITS {
	(nA) = (nanoamp)
	(mV) = (millivolt)
	(uS) = (microsiemens)
}

PARAMETER {
	onset=0  (ms)
	tau0=0.5 (ms) <1e-3,1e6>
	tau1=5.0 (ms) <1e-3,1e6>
	gmax=0 	 (uS) <0,1e9>
	e=0	 (mV)
}

ASSIGNED {
	v (mV)
	i (nA)
	g (uS)
	factor
	a0
	a1
}

INITIAL { LOCAL tpeak
	if (tau0/tau1 > 0.9999) {
		tau0 = 0.9999*tau1
	}
	tpeak=tau0*tau1*log(tau0/tau1)/(tau0-tau1)
	factor=1/((1-exp(-tpeak/tau0))-(1-exp(-tpeak/tau1)))
}

BREAKPOINT {
	if (gmax) { at_time(onset) }
	g = gmax*cond(t-onset)
	i = g*(v - e)
}

FUNCTION cond(x (ms)) { LOCAL temp
	if (x < 0) {
		cond = 0
	}else{
		temp = x/tau0
		if (temp > 10) {
			a0 = 1
		} else {
			a0=1-exp(-temp)
		}
		temp = x/tau1
		if (temp > 10) {
			a1 = 1
		} else {
			a1=1-exp(-temp)
		}
		cond = factor*(a0-a1)
	}
}