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
                            
COMMENT

ca.mod
Uses fixed eca instead of GHK eqn

HVA Ca current
Based on Reuveni, Friedman, Amitai and Gutnick (1993) J. Neurosci. 13:
4609-4621.

Author: Zach Mainen, Salk Institute, 1994, zach@salk.edu

ENDCOMMENT

INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}

NEURON {
	SUFFIX ca
	USEION ca READ eca WRITE ica
	RANGE m, h, gca, gbar
	RANGE minf, hinf, mtau, htau
	GLOBAL q10, temp, tadj, vmin, vmax, vshift
}

PARAMETER {
	gbar = 0.1   	(pS/um2)	: 0.12 mho/cm2
	vshift = 0	(mV)		: voltage shift (affects all)

	cao  = 2.5	(mM)	        : external ca concentration
	cai		(mM)
						
	temp = 23	(degC)		: original temp 
	q10  = 2.3			: temperature sensitivity

	v 		(mV)
	dt		(ms)
	celsius		(degC)
	vmin = -120	(mV)
	vmax = 100	(mV)
}


UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
	(pS) = (picosiemens)
	(um) = (micron)
	FARADAY = (faraday) (coulomb)
	R = (k-mole) (joule/degC)
	PI	= (pi) (1)
} 

ASSIGNED {
	ica 		(mA/cm2)
	gca		(pS/um2)
	eca		(mV)
	minf 		hinf
	mtau (ms)	htau (ms)
	tadj
}
 

STATE { m h }

INITIAL { 
	trates(v+vshift)
	m = minf
	h = hinf
}

BREAKPOINT {
        SOLVE states
        gca = tadj*gbar*m*m*h
	ica = (1e-4) * gca * (v - eca)
} 

LOCAL mexp, hexp

PROCEDURE states() {
        trates(v+vshift)      
        m = m + mexp*(minf-m)
        h = h + hexp*(hinf-h)
	VERBATIM
	return 0;
	ENDVERBATIM
}


PROCEDURE trates(v) {  
                      
        LOCAL tinc
        TABLE minf, mexp, hinf, hexp
	DEPEND dt, celsius, temp
	
	FROM vmin TO vmax WITH 199

	rates(v): not consistently executed from here if usetable == 1

        tadj = q10^((celsius - temp)/10)
        tinc = -dt * tadj

        mexp = 1 - exp(tinc/mtau)
        hexp = 1 - exp(tinc/htau)
}


PROCEDURE rates(vm) {  
        LOCAL  a, b

	a = 0.055*(-27 - vm)/(exp((-27-vm)/3.8) - 1)
	b = 0.94*exp((-75-vm)/17)
	
	mtau = 1/(a+b)
	minf = a*mtau

		:"h" inactivation 

	a = 0.000457*exp((-13-vm)/50)
	b = 0.0065/(exp((-vm-15)/28) + 1)

	htau = 1/(a+b)
	hinf = a*htau
}

FUNCTION efun(z) {
	if (fabs(z) < 1e-4) {
		efun = 1 - z/2
	}else{
		efun = z/(exp(z) - 1)
	}
}

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