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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
Original
ca.mod *
cadyn.mod
cal2.mod *
capool.mod *
cat.mod
currentclamp.mod *
function_TMonitor.mod *
h.mod
ICat2.mod *
Ikleaksd.mod *
im.mod
kadist.mod *
kaprox.mod
kdrca1.mod
kdrca1DA.mod
kdrinter.mod *
kdtx.mod
leak.mod *
leakDA.mod
leakinter.mod *
na.mod
na3.mod
na3DA.mod
nainter.mod *
nap.mod
nax.mod
naxDA.mod
sahp.mod
sahpNE.mod
graphics_lib.hoc *
main.hoc
main_LTO.hoc
onecompartment_template_with_osc.hoc
                            
TITLE na3
: Na current 
: from Jeff M.
:  ---------- modified -------M.Migliore may97

NEURON {
	SUFFIX na3DA
	USEION na READ ena WRITE ina
	RANGE  gbar, ar2
	GLOBAL minf, hinf, mtau, htau, sinf, taus,qinf, thinf
}

PARAMETER {
	DA_start = 100		             : D1R(Low Affinity) Dopamine Effect after 6 conditioning trials (15*4000) = 60000)
	DA_stop = 600	
	DA_t1 = -1 : -0.3 : -0.15            : Amount of DA effect- negative value decreases AP threshold / positive value increases threshold of AP
	
	gbar = 0.010   	(mho/cm2)	
								
	tha  =  -30	(mV)		: v 1/2 for act	
	qa   = 7.2	(mV)		: act slope (4.5)		
	Ra   = 0.4	(/ms)		: open (v)		
	Rb   = 0.124 	(/ms)		: close (v)		

	thi1  = -45	(mV)		: v 1/2 for inact 	
	thi2  = -45 	(mV)		: v 1/2 for inact 	
	qd   = 1.5	(mV)	        : inact tau slope
	qg   = 1.5      (mV)
	mmin=0.02	
	hmin=0.5			
	q10=2
	Rg   = 0.01 	(/ms)		: inact recov (v) 	
	Rd   = .03 	(/ms)		: inact (v)	
	qq   = 10        (mV)
	tq   = -55      (mV)

	thinf  = -50 	(mV)		: inact inf slope	
	qinf  = 4 	(mV)		: inact inf slope 

        vhalfs=-60	(mV)		: slow inact.
        a0s=0.0003	(ms)		: a0s=b0s
        zetas=12	(1)
        gms=0.2		(1)
        smax=10		(ms)
        vvh=-58		(mV) 
        vvs=2		(mV)
        ar2=1		(1)		: 1=no inact., 0=max inact.
	ena		(mV)            : must be explicitly def. in hoc
	celsius
	v 		(mV)
}


UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
	(pS) = (picosiemens)
	(um) = (micron)
} 

ASSIGNED {
	ina 		(mA/cm2)
	thegna		(mho/cm2)
	minf 		hinf 		
	mtau (ms)	htau (ms) 	
	sinf (ms)	taus (ms)
	tha1	
}
 

STATE { m h s}

BREAKPOINT {
        SOLVE states METHOD cnexp
        thegna = gbar*m*m*m*h*s
	ina = thegna * (v - ena)
} 

INITIAL {
	trates(v,ar2)
	m=minf  
	h=hinf
	s=sinf
}


FUNCTION alpv(v(mV)) {
         alpv = 1/(1+exp((v-vvh)/vvs))
}
        
FUNCTION alps(v(mV)) {  
  alps = exp(1.e-3*zetas*(v-vhalfs)*9.648e4/(8.315*(273.16+celsius)))
}

FUNCTION bets(v(mV)) {
  bets = exp(1.e-3*zetas*gms*(v-vhalfs)*9.648e4/(8.315*(273.16+celsius)))
}

LOCAL mexp, hexp, sexp

DERIVATIVE states {   
        trates(v,ar2)      
        m' = (minf-m)/mtau
        h' = (hinf-h)/htau
        s' = (sinf - s)/taus
}

PROCEDURE trates(vm,a2) {  
        LOCAL  a, b, c, qt
        qt=q10^((celsius-24)/10)
		tha1 = tha + DA1(t)
	a = trap0(vm,tha1,Ra,qa)
	b = trap0(-vm,-tha1,Rb,qa)
	mtau = 1/(a+b)/qt
        if (mtau<mmin) {mtau=mmin}
	minf = a/(a+b)

	a = trap0(vm,thi1,Rd,qd)
	b = trap0(-vm,-thi2,Rg,qg)
	htau =  1/(a+b)/qt
        if (htau<hmin) {htau=hmin}
	hinf = 1/(1+exp((vm-thinf)/qinf))
	c=alpv(vm)
        sinf = c+a2*(1-c)
        taus = bets(vm)/(a0s*(1+alps(vm)))
        if (taus<smax) {taus=smax}
}

FUNCTION trap0(v,th,a,q) {
	if (fabs(v-th) > 1e-6) {
	        trap0 = a * (v - th) / (1 - exp(-(v - th)/q))
	} else {
	        trap0 = a * q
 	}
}	
FUNCTION DA1(t) {
	    if (t >= DA_start && t <= DA_stop){DA1 = DA_t1} 									: During conditioning
		else  {DA1 = 0}
	}

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