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 pyramidal corticothalamic L6 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 pyramidal corticothalamic L6 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
26 Ago 2002 Modification of original channel to allow variable time step and to correct an initialization error.
    Done by Michael Hines(michael.hines@yale.e) and Ruggero Scorcioni(rscorcio@gmu.edu) at EU Advance Course in Computational Neuroscience. Obidos, Portugal

ca.mod
Uses fixed eca instead of GHK eqn

HVA Ca current
Based on Reuveni, Friedman, Amitai and Gutnick (1993) J. Neurosci. 13:
4609-4621.

Author: Zach Mainen, Salk Institute, 1994, zach@salk.edu

ENDCOMMENT

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

NEURON {
	SUFFIX ca
	USEION ca READ eca WRITE ica
	RANGE m, h, gca, gbar
	RANGE minf, hinf, mtau, htau
	GLOBAL q10, temp, tadj, vmin, vmax, vshift
}

PARAMETER {
	gbar = 0.1   	(pS/um2)	: 0.12 mho/cm2
	vshift = 0	(mV)		: voltage shift (affects all)

	cao  = 2.5	(mM)	        : external ca concentration
	cai		(mM)
						
	temp = 23	(degC)		: original temp 
	q10  = 2.3			: temperature sensitivity

	v 		(mV)
	dt		(ms)
	celsius		(degC)
	vmin = -120	(mV)
	vmax = 100	(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 METHOD cnexp
        gca = tadj*gbar*m*m*h
	ica = (1e-4) * 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
:}

DERIVATIVE states {
        trates(v+vshift)      
        m' =  (minf-m)/mtau
        h' =  (hinf-h)/htau
}

PROCEDURE trates(v) {  
                      
        
        TABLE minf, hinf, mtau, htau 
	DEPEND  celsius, temp
	
	FROM vmin TO vmax WITH 199

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

:        tinc = -dt * tadj

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


PROCEDURE rates(vm) {  
        LOCAL  a, b

        tadj = q10^((celsius - temp)/10)

	a = 0.055*(-27 - vm)/(exp((-27-vm)/3.8) - 1)
	b = 0.94*exp((-75-vm)/17)
	
	mtau = 1/tadj/(a+b)
	minf = a/(a+b)

		:"h" inactivation 

	a = 0.000457*exp((-13-vm)/50)
	b = 0.0065/(exp((-vm-15)/28) + 1)

	htau = 1/tadj/(a+b)
	hinf = a/(a+b)
}

FUNCTION efun(z) {
	if (fabs(z) < 1e-4) {
		efun = 1 - z/2
	}else{
		efun = z/(exp(z) - 1)
	}
}

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