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
                            
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

kca.mod

Calcium-dependent potassium channel
Based on
Pennefather (1990) -- sympathetic ganglion cells
taken from
Reuveni et al (1993) -- neocortical cells

Author: Zach Mainen, Salk Institute, 1995, zach@salk.edu
	
ENDCOMMENT

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

NEURON {
	SUFFIX kca
	USEION k READ ek WRITE ik
	USEION ca READ cai
	RANGE n, gk, gbar
	RANGE ninf, ntau
	GLOBAL Ra, Rb, caix
	GLOBAL q10, temp, tadj, vmin, vmax
}

UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
	(pS) = (picosiemens)
	(um) = (micron)
} 

PARAMETER {
	gbar = 10   	(pS/um2)	: 0.03 mho/cm2
	v 		(mV)
	cai  		(mM)
	caix = 1	
									
	Ra   = 0.01	(/ms)		: max act rate  
	Rb   = 0.02	(/ms)		: max deact rate 

	dt		(ms)
	celsius		(degC)
	temp = 23	(degC)		: original temp 	
	q10  = 2.3			: temperature sensitivity

	vmin = -120	(mV)
	vmax = 100	(mV)
} 


ASSIGNED {
	a		(/ms)
	b		(/ms)
	ik 		(mA/cm2)
	gk		(pS/um2)
	ek		(mV)
	ninf
	ntau 		(ms)	
	tadj
}
 

STATE { n }

INITIAL { 
	rates(cai)
	n = ninf
}

BREAKPOINT {
        SOLVE states METHOD cnexp
	gk = tadj*gbar*n
	ik = (1e-4) * gk * (v - ek)
} 

LOCAL nexp

DERIVATIVE states {   :Computes state variable n 
        rates(cai)      :             at the current v and dt.
        n' =  (ninf-n)/ntau

}

PROCEDURE rates(cai(mM)) {  

        

        a = Ra * cai^caix
        b = Rb

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

        ntau = 1/tadj/(a+b)
	ninf = a/(a+b)

 
:        tinc = -dt * tadj
:        nexp = 1 - exp(tinc/ntau)
}