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;
// synapses on apical dendrites

objref syn_, ns_, nc_, AMPA_synlist, AMPA_nslist, AMPA_nclist
AMPA_synlist=new List()
AMPA_nslist=new List()
AMPA_nclist=new List()

proc syns(){
	total_AMPA=0
	no_of_AMPA=0
	forsec apical_list {
		for(x) if(x > 0 && x < 1 && distance(x)>100) { // synapses distributed from 100 um from soma
			no_of_AMPA = int(area(x)*1/100) // synapses distributed per um^2
			AMPA_syn(x,no_of_AMPA)
			total_AMPA+=no_of_AMPA
		}
	}
}

proc AMPA_syn() {local i
	syn_ = new Exp2Syn($1)  AMPA_synlist.append(syn_)
		syn_.e = 0
		syn_.tau1 = .5
		syn_.tau2 = 3

	for i=0, $2-1{
		ns_nc(AMPA_nslist,syn_,AMPA_nclist)
	}
}

proc ns_nc(){
		bar ns_ = new NetStim(.5) $o1.append(ns_)
			ns_.interval = 100
			ns_.number = 5
			ns_.start = 10
			ns_.noise = 0

		nc_ = new NetCon(ns_,$o2) $o3.append(nc_)
			nc_.weight = 0.0003 // Maximal conductances in uS
			nc_.delay = 0
    		nc_.threshold = 0
}


AMPA_rate	= 20	// Firing rates in Hz
ns_frac  	= 0.3	// fraction of synapses activated

proc ns_stim() {
	if (ns_frac<=0) ns_frac = 1e-5
	for i=0, AMPA_nslist.count()-1 {AMPA_nslist.object[i].number = 0}
	for(i=0; i <= AMPA_nslist.count() - 1; i+=1/ns_frac) {
		AMPA_nslist.object[i].number = 5
	}
	for i=0, AMPA_nslist.count() - 1 {
		AMPA_nslist.object[i].start = 200
		AMPA_nslist.object[i].noise = 0
		AMPA_nslist.object[i].interval=1000/AMPA_rate
	}
}

syns()

ns_stim(AMPA_nslist,AMPA_rate)


// graphs

objref plts
plts = new Shape(0)
plts.view(-230, -600, 1665, 920, 20, 120, 360, 200)

objref vg
vg = new Graph(0)
vg.view(0, -70, 800, 20, 536, 122, 300, 200)
graphList[0].append(vg)
vg.exec_menu("Keep Lines")

proc gui(){
	xpanel("Simulation")
		xbutton("regular - 20 Hz","reg_20()")
		xbutton("regular - 50 Hz","reg_50()")
		xbutton("burster - 20 Hz","burst_20()")
		xbutton("burster - 50 Hz","burst_50()")
		xbutton("clear graph","vg.erase_all()")
	xpanel(1000,100)
}


proc reg_20(){
	vg.addvar("regular - 20 Hz","v(.5)", 1, 1, 0.7, 0.9, 2)
	ghd=0.45e-5
	h_density()
	AMPA_rate=20
	ns_stim()
	run()
}

proc reg_50(){
	vg.addvar("regular - 50 Hz","v(.5)", 2, 1, 0.7, 0.9, 2)
	ghd=0.45e-5
	h_density()
	AMPA_rate=50
	ns_stim()
	run()
}

proc burst_20(){
	vg.addvar("burster - 20 Hz","v(.5)", 3, 1, 0.7, 0.9, 2)
	ghd=1.2e-5
	h_density()
	AMPA_rate=20
	ns_stim()
	run()
}

proc burst_50(){
	vg.addvar("burster - 50 Hz","v(.5)", 4, 1, 0.7, 0.9, 2)
	ghd=1.2e-5
	h_density()
	AMPA_rate=50
	ns_stim()
	run()
}

gui()

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