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
3_Mitral
Original
readme.txt *
cadecay.mod *
Ih.mod *
INaP.mod
kA.mod
kca3.mod
kfasttab.mod *
kO.mod *
kslowtab.mod *
lcafixed.mod *
nafast.mod
kfast_k.inf *
kfast_k.tau *
kfast_k.txt *
kfast_n.inf *
kfast_n.tau *
kfast_n.txt *
kslow_k.inf *
kslow_k.tau *
kslow_k.txt *
kslow_n.inf *
kslow_n.tau *
kslow_n.txt *
mitral.hoc
mosinit.hoc *
tabchannels.hoc *
                            
TITLE HH fast sodium channel
: Implemented in Rubin and Cleland (2006) J Neurophysiology
: Parameters from Bhalla and Bower (1993) J Neurophysiology
: Adapted from /usr/local/neuron/demo/release/nachan.mod - squid
:   by Andrew Davison, The Babraham Institute  [Brain Res Bulletin, 2000]

NEURON {
	SUFFIX nafast
	USEION na READ ena WRITE ina
	RANGE gnabar, ina, i
	GLOBAL minf, hinf, mtau, htau
}

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

INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
PARAMETER {
	v (mV)
	dt (ms)
	gnabar = 0.120 (mho/cm2) <0,1e9>
	ena = 45 (mV)
}
STATE {
	m h
}
ASSIGNED {
	ina (mA/cm2)
	i   (mA/cm2)
	minf
	hinf
	mtau (ms)
	htau (ms)
}

INITIAL {
	rates(v)
	m = minf
	h = hinf
}

BREAKPOINT {
	SOLVE states METHOD cnexp
	ina = gnabar*m*m*m*h*(v - ena)
	i = ina
}

DERIVATIVE states {
	rates(v)
	m' = (minf - m)/mtau
	h' = (hinf - h)/htau
}

FUNCTION alp(v(mV),i) (/ms) {
	if (i==0) {
		alp = 0.32(/ms)*expM1(-(v *1(/mV) + 42), 4)
	}else if (i==1){
		alp = 0.128(/ms)/(exp((v *1(/mV) + 38)/18))
	}
}

FUNCTION bet(v(mV),i)(/ms) {
	if (i==0) {
		bet = 0.28(/ms)*expM1(v *1(/mV) + 15, 5)
	}else if (i==1){
		bet = 4(/ms)/(exp(-(v* 1(/mV) + 15)/5) + 1)
	}
}

FUNCTION expM1(x,y) {
	if (fabs(x/y) < 1e-6) {
		expM1 = y*(1 - x/y/2)
	}else{
		expM1 = x/(exp(x/y) - 1)
	}
}

PROCEDURE rates(v(mV)) {LOCAL a, b
	:TABLE minf, hinf, mtau, htau FROM -100 TO 100 WITH 200
	a = alp(v,0)  b=bet(v,0)
	mtau = 1/(a + b)
	minf = a/(a + b)      :::: v1/2 = -29.03 ::: k = 7.412
	a = alp(v,1)  b=bet(v,1)
	htau = 1/(a + b)
	hinf = a/(a + b)      :::: v1/2 = -33.32 ::: k = -4.041
}

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