Distinct current modules shape cellular dynamics in model neurons (Alturki et al 2016)

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Accession:223649
" ... We hypothesized that currents are grouped into distinct modules that shape specific neuronal characteristics or signatures, such as resting potential, sub-threshold oscillations, and spiking waveforms, for several classes of neurons. For such a grouping to occur, the currents within one module should have minimal functional interference with currents belonging to other modules. This condition is satisfied if the gating functions of currents in the same module are grouped together on the voltage axis; in contrast, such functions are segregated along the voltage axis for currents belonging to different modules. We tested this hypothesis using four published example case models and found it to be valid for these classes of neurons. ..."
Reference:
1 . Alturki A, Feng F, Nair A, Guntu V, Nair SS (2016) Distinct current modules shape cellular dynamics in model neurons. Neuroscience 334:309-331 [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: Hippocampus; Amygdala;
Cell Type(s): Abstract single compartment conductance based cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Simplified Models; Activity Patterns; Oscillations; Methods; Olfaction;
Implementer(s):
/
AlturkiEtAl2016
4_LA
Segregated
cadyn.mod *
capool.mod *
currentclamp.mod *
h.mod *
im.mod *
kaprox.mod *
kdrca1.mod *
kdrca1DA.mod *
leak.mod *
leakDA.mod *
na3.mod *
na3DA.mod *
nap.mod *
nat.mod *
sahp.mod *
sahpNE.mod *
graphics_lib.hoc *
main.hoc
main_HTO.hoc
main_LTO.hoc
onecompartment_template_with_osc.hoc
                            
: passive leak current

NEURON {
	SUFFIX leakDA
	NONSPECIFIC_CURRENT il
	RANGE il, el, glbar
}

UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
}

PARAMETER {
	tone_period = 4000    
	DA_period = 500	
	DA_start = 36000		             : D2R(High Affinity) Dopamine Effect after 6 conditioning trials (15*4000) = 60000)
	DA_stop = 96000
	DA_ext1 = 196000
	DA_ext2 = 212000	
	DA_t1 = 0.8 : 0.9 : 1 :  1 : 0.9           : Amount(%) of DA effect- negative value decreases AP threshold / positive value increases threshold of AP
	DA_period2 = 100
	DA_start2 = 36000		   			: shock Dopamine Effect during shock after 1 conditioning trial
	DA_t2 = .9           				: Amount(%) of DA effect- negative value decreases AP threshold / positive value increases threshold of AP	
	
	glbar = 2.857142857142857e-05  :3.333333e-5 (siemens/cm2) < 0, 1e9 >
	el = -75 (mV)
}

ASSIGNED {
	v (mV)
	il (mA/cm2)
}

BREAKPOINT { 
	il = glbar*(v - el)*DA1(t)*DA2(t)
}
FUNCTION DA1(t) {
	    if (t >= DA_start && t <= DA_stop){ 									: During conditioning
			if ((t/tone_period-floor(t/tone_period)) >= (1-DA_period/tone_period)) {DA1 = DA_t1}
			else if ((t/tone_period-floor(t/tone_period)) == 0) {DA1 = DA_t1}
			else {DA1 = 1}}
		else if (t >= DA_ext1 && t <= DA_ext2){								: During 4trials of Extinction
			if ((t/tone_period-floor(t/tone_period)) >= (1-DA_period/tone_period)) {DA1 = DA_t1}
			else if ((t/tone_period-floor(t/tone_period)) == 0) {DA1 = DA_t1}
			else {DA1 = 1}}		
		else  {DA1 = 1}
	}
FUNCTION DA2(t) {
	    if (t >= DA_start2 && t <= DA_stop){
			if((t/tone_period-floor(t/tone_period)) >= (1-DA_period2/tone_period)) {DA2 = DA_t2}
			else if ((t/tone_period-floor(t/tone_period)) == 0) {DA2 = DA_t2}
			else  {DA2 = 1}}
		else  {DA2 = 1}
	}

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