Model of repetitive firing in Grueneberg ganglion olfactory neurons (Liu et al., 2012)

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Accession:151817
This model is constructed based on properties of Na+ and K+ currents observed in whole-cell patch clamp recordings of mouse Grueneberg ganglion neurons in acute slices. Two distinct Na+ conductances representing the TTX-sensitive and TTX-resistant currents and one delayed rectifier K+ currrent are included. By modulating the maximal conductances of Na+ currents, one can reproduce the regular, phasic, and sporadic patterns of repetitive firing found in the patch clamp experiments.
Reference:
1 . Liu CY, Xiao C, Fraser SE, Lester HA, Koos DS (2012) Electrophysiological characterization of Grueneberg ganglion olfactory neurons: spontaneous firing, sodium conductance, and hyperpolarization-activated currents. J Neurophysiol 108:1318-34 [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): Olfactory receptor GLU cell; Grueneberg ganglion neuron;
Channel(s): I K; I K,leak; I Sodium;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Action Potential Initiation; Temporal Pattern Generation; Action Potentials; Rebound firing; Recurrent Discharge; Olfaction;
Implementer(s): Liu, Cambrian [camliu at chla.usc.edu];
Search NeuronDB for information about:  Olfactory receptor GLU cell; I K; I K,leak; I Sodium;
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GG
readme.txt
code_holder.hoc
elvis.ses
elvism10.ses
elvisp10.ses
graphs.hoc
gui_controller.hoc
hvas.ses
iclamp.hoc
init.hoc
kchannels.ses
leak.ses
mosinit.hoc *
runner.hoc
soma.hoc
                            
tstop = 150
v_init = -55
stim_low = 0.0005
stim_high = 1
stim_inc = 0.0001
Vm_low = -91
Vm_high = -49
Vm_inc = 1
config = 0
resolution = 100
framelen = 10
compConduct = 0
compLeak = 0
baseLeak = 0
conduct_config = 0
percent_step = 10
max_percent = 200
conduct_scale = 30
conduct_stim = 0.001

objref vec, fSR, fR, fS, fK, fStim, fPlay, fWriter
vec = new Vector()
vec.record(&soma.v(0.5))
fSR = new File()
fS = new File()
fR = new File()
fK = new File()
fStim = new File()
fPlay = new File()
fWriter = new File()


func INa() {
	/* return (gmax_TTXS*(minfS($1))^3*hinfS($1)*($1-e_TTXS)+gmax_TTXR*(minfR($1))^3*hinfR($1)*($1-e_TTXR)) */
	finitialize($1)
	return (i_TTXR + i_TTXS)
}

func IK() {
	/* return (gmax_K*(ninf($1))^4*($1-e_K)) */
	finitialize($1)
	return (i_K)
}

proc adjust_leak() {
	ss_curr = -1*INa(v_init)-IK(v_init)
/*	print v_init
	print ss_curr
	print "e_TTXS ", e_TTXS
	print "e_TTXR ", e_TTXR
	print "minfS ", minfS(v_init)
	print "hinfS ", hinfS(v_init)
	print "minfR ", minfR(v_init)
	print "hinfR ", hinfR(v_init)
	print "ninf ", ninf(v_init) */
	if (ss_curr > 0) {
		e_leak = v_init - 5
	} else {
		e_leak = v_init + 5
	}
	gmax_leak = (-1*INa(v_init)-IK(v_init))/(v_init-e_leak)
}


proc start_sim() {
	vmstore = v_init
	v_init = -55
	adjust_leak()
	v_init = vmstore
	stdinit()
	run()
	g.exec_menu("View = plot")
}

proc adj_start_sim() {
	adjust_leak()
	stdinit()
	run()
	g.exec_menu("View = plot")
	fWriter.wopen ("writer.txt")
	vec.printf(fWriter,"%5.1f\t")
	fWriter.close()
}

proc run_batch_stims() {
	
	fSR.wopen("SR.txt")
	gmax_TTXR = 0.00987
	gmax_TTXS = 0.00987
	stim_mag = stim_low
	for (i=stim_low; i <= stim_high; i=i+stim_inc) {
		stim.amp = stim_mag
		adj_start_sim()
		vec.printf(fSR,"%5.1f\t")
		stim_mag = stim_mag + stim_inc
	}
	fSR.close()
	
	fS.wopen("S.txt")
	gmax_TTXR = 0
	gmax_TTXS = 0.00987
	stim_mag = stim_low
	for (i=stim_low; i <= stim_high; i=i+stim_inc) {
		stim.amp = stim_mag
		adj_start_sim()
		vec.printf(fS,"%5.1f\t")
		stim_mag = stim_mag + stim_inc
	}
	fS.close()
	
	fR.wopen("R.txt")
	gmax_TTXR = 0.00987
	gmax_TTXS = 0
	stim_mag = stim_low
	for (i=stim_low; i <= stim_high; i=i+stim_inc) {
		stim.amp = stim_mag
		adj_start_sim()
		vec.printf(fR,"%5.1f\t")
		stim_mag = stim_mag + stim_inc
	}
	fR.close()
	
	fK.wopen("K.txt")
	gmax_TTXR = 0
	gmax_TTXS = 0
	stim_mag = stim_low
	for (i=stim_low; i <= stim_high; i=i+stim_inc) {
		stim.amp = stim_mag
		adj_start_sim()
		vec.printf(fK,"%5.1f\t")
		stim_mag = stim_mag + stim_inc
	}
	fK.close()	
	
	gmax_TTXR = 0.00987
	gmax_TTXS = 0.00987
}

proc run_batch_Vms() {

	fSR.wopen("SR.txt")
	gmax_TTXR = 0.00987
	gmax_TTXS = 0.00987
	v_init = Vm_low
	for (i=Vm_low; i <= Vm_high; i=i+Vm_inc) {
		adj_start_sim()
		vec.printf(fSR,"%5.1f\t")
		v_init = v_init + Vm_inc
	}
	fSR.close()
	
	fS.wopen("S.txt")
	gmax_TTXR = 0
	gmax_TTXS = 0.00987
	v_init = Vm_low
	for (i=Vm_low; i <= Vm_high; i=i+Vm_inc) {
		adj_start_sim()
		vec.printf(fS,"%5.1f\t")
		v_init = v_init + Vm_inc
	}
	fS.close()
	
	fR.wopen("R.txt")
	gmax_TTXR = 0.00987
	gmax_TTXS = 0
	v_init = Vm_low
	for (i=Vm_low; i <= Vm_high; i=i+Vm_inc) {
		adj_start_sim()
		vec.printf(fR,"%5.1f\t")
		v_init = v_init + Vm_inc
	}
	fR.close()
	
	fK.wopen ("K.txt")
	gmax_TTXR = 0
	gmax_TTXS = 0
	v_init = Vm_low
	for (i=Vm_low; i <= Vm_high; i=i+Vm_inc) {
		adj_start_sim()
		vec.printf(fK,"%5.1f\t")
		v_init = v_init + Vm_inc
	}
	fK.close()
	
	gmax_TTXR = 0.00987
	gmax_TTXS = 0.00987

}

func run_with_current() {

	multiplier = compConduct + 1
	
	storeTTXR = gmax_TTXR
	storeTTXS = gmax_TTXS
	
	if (config == 1) {
		gmax_TTXR = 0
		gmax_TTXS = storeTTXS * multiplier
	} 
	if (config == 2) {
		gmax_TTXR = storeTTXR * multiplier
		gmax_TTXS = 0
	}
	if (config == 3) {
		gmax_TTXR = 0
		gmax_TTXS = 0
	}
	
	adj_start_sim()
	
	foundAP = 0
	iStart = stim.del*steps_per_ms
	iEnd = (stim.del+framelen)*steps_per_ms
	for (i=iStart; i < iEnd; i=i+1) {
		if (vec.x[i] > -20) {
			foundAP = 1
		}
	}
	print stim.amp, foundAP
	
	gmax_TTXR = storeTTXR
	gmax_TTXS = storeTTXS
	
	return foundAP
}

func calc_base_leak() {
	storeTTXR = gmax_TTXR
	storeTTXS = gmax_TTXS
	storeK = gmax_K
	
	gmax_TTXR = 0.00987
	gmax_TTXS = 0.00987
	gmax_K = 0.00571
	
	baseLeak = -1*INa(v_init)-IK(v_init)
	
	gmax_TTXR = storeTTXR
	gmax_TTXS = storeTTXS
	gmax_K = storeK
}

proc run_find_min_current() {
	
	leakCurrent = -1*INa(v_init)-IK(v_init)
	adjustedStim = compLeak*(leakCurrent-baseLeak)
	print adjustedStim
	
	stim_size = stim_high
	stim.amp = stim_size + adjustedStim
	
	boundHigh = stim_high
	boundLow = stim_high
	
	foundAP = run_with_current()
	while (foundAP == 1) {
		boundHigh = stim_size
		stim_size = stim_size / 2
		stim.amp = stim_size + adjustedStim
		foundAP = run_with_current()
	}
	boundLow = stim_size
	
	stim_inc = boundLow / resolution
	
	stim_size = boundLow
	stim.amp = stim_size
	foundAP = run_with_current()
	while (foundAP == 0 && stim_size <= boundHigh) {
		stim_size = stim_size + stim_inc
		stim.amp = stim_size + adjustedStim
		foundAP = run_with_current()
	}
	min_stim = stim_size
	fStim.printf("%5.1f\t",v_init)
	fStim.printf("%1.8f\n",stim_size)

}

proc make_excitability_curve() {
	fStim.wopen("stim.txt")
	v_init = Vm_low
	for (j=Vm_low; j <= Vm_high; j=j+Vm_inc) {
		run_find_min_current()
		print j
		v_init = v_init + Vm_inc
	}
	fStim.close()
}

proc run_conductance_changes() {
	fPlay.wopen ("play.txt")
	stim.amp = conduct_stim
	scaledTTXR = gmax_TTXR * conduct_scale
	scaledTTXS = gmax_TTXS * conduct_scale
	for (j=0; j <= max_percent; j=j+percent_step) {
		if (conduct_config == 0) {
			gmax_TTXR = scaledTTXR * 0.01 * j
			gmax_TTXS = scaledTTXS
		} else {
			gmax_TTXS = scaledTTXS * 0.01 * j
			gmax_TTXR = scaledTTXR
		}
		print gmax_TTXR, gmax_TTXS, j
		adj_start_sim()
		fPlay.printf("%5.1f\t",j)
		vec.printf(fPlay,"%5.1f\t")
	}
	fPlay.close()
	gmax_TTXR = 0.002438
	gmax_TTXS = 0.002438
}

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