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;
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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
                            
// shortRun.hoc is a copy of the fig6b.hoc file.
// except for the value of tstop it is identical

load_file("nrngui.hoc")

xopen("./Ri18geo.hoc")
xopen("./Ri18init.hoc")

init_params()
init_spine()
init_cell()
init_vm()
hexp()

print "Ri18 successfully built\n"

access dend1[21]	//soma

objref b, g
b = new VBox()
b.intercept(1)
g = new Graph()
g.size(300,tstop,-85,-10)
g.color(0)
g.addvar("Vm_dendrite (1000um from soma)","dend1[720].v(.5)", 2, 1)
g.addvar("Vm_soma","dend1[21].v(.5)", 1, 1)
g.xaxis(0)
g.yaxis(0)
g.begin()
g.family(1)
b.intercept(0)
b.map()

proc init() {local saveDt, i
	access dend1[21]	//soma
	finitialize(v_init)
	fcurrent()
	saveDt = dt
	dt = 1
// original	for i=1,(200/dt) advance()		//to speed up initialization
	for i=1,(50/dt) advance()		//to speed up initialization
	dt = saveDt
}

proc run (){
	while (t <= tstop) {
		advance()
	}
}

proc advance() {
	g.plot(t)
	fadvance()
	g.flush()
	doNotify()
}

t=0

tstop=50 // for short run, original value 10300
dt=0.1

nrncontrolmenu()
init()
startsw()
continuerun(50) // for short run, original continuerun(10300)

// added for shortRun:
Graph[0].exec_menu("View = plot")

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