Ih levels roles in bursting and regular-spiking subiculum pyramidal neurons (van Welie et al 2006)

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Accession:82364
Pyramidal neurons in the subiculum typically display either bursting or regular-spiking behavior. ... Here we report that bursting neurons posses a hyperpolarization-activated cation current (Ih) that is two-fold larger (conductance: 5.3 ± 0.5 nS) than in regularspiking neurons (2.2 ± 0.6 nS), while Ih exhibits similar voltage-dependent and kinetic properties in both classes of neurons. Bursting and regular-spiking neurons display similar morphology. The difference in Ih between the two classes is not responsible for the distinct firing patterns, since neither pharmacological blockade of Ih nor enhancement of Ih using a dynamic clamp affects the qualitative firing patterns. Instead, the difference in Ih between bursting and regular-spiking neurons determines the temporal integration of evoked synaptic input from the CA1 area. In response to 50 Hz stimulation, bursting neurons, with a large Ih, show ~50% less temporal summation than regular-spiking neurons. ... A computer simulation model of a subicular neuron with the properties of either a bursting or a regular-spiking neuron confirmed the pivotal role of Ih in temporal integration of synaptic input. These data suggest that in the subicular network, bursting neurons are better suited to discriminate the content of high frequency input, such as that occurring during gamma oscillations, compared to regular-spiking neurons. See paper for more and details.
Reference:
1 . van Welie I, Remme MW, van Hooft JA, Wadman WJ (2006) Different levels of Ih determine distinct temporal integration in bursting and regular-spiking neurons in rat subiculum. J Physiol 576:203-14 [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):
Channel(s): I h;
Gap Junctions:
Receptor(s): AMPA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Coincidence Detection; Synaptic Integration;
Implementer(s):
Search NeuronDB for information about:  AMPA; I h;
TITLE I-h channel from Magee 1998

NEURON {
	SUFFIX hd
	NONSPECIFIC_CURRENT i
    RANGE ghdbar, vhalfl, ghd, i, kl
    GLOBAL qtl
}
UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)

}

PARAMETER {
	v 				(mV)
    ehd  			(mV)
	celsius 		(degC)
	ghdbar=.0001 	(mho/cm2)
    vhalfl=-81   	(mV)
	kl=-8
	vhalft=-75   	(mV)
	a0t=0.011      	(/ms)
	zetat=2.2    	(1)
	gmt=.4   		(1)
	q10=4.5
	qtl=1
}

STATE {
        l
}

ASSIGNED {
	i (mA/cm2)
	linf
	taul
	ghd
}

INITIAL {
	rate(v)
	l=linf
}


BREAKPOINT {
	SOLVE states METHOD cnexp
	ghd = ghdbar*l
	i = ghd*(v-ehd)
}

DERIVATIVE states {
        rate(v)
        l' =  (linf - l)/taul
}

FUNCTION alpt(v(mV)) {
  		alpt = exp(0.0378*zetat*(v-vhalft))
}

FUNCTION bett(v(mV)) {
  		bett = exp(0.0378*zetat*gmt*(v-vhalft))
}


PROCEDURE rate(v (mV)) {
        LOCAL a,qt
        qt=q10^((celsius-33)/10)
        a = alpt(v)
        linf = 1/(1 + exp(-(v-vhalfl)/kl))
        taul = bett(v)/(qtl*qt*a0t*(1+a))
}















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