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 Sodium persistent current for RD Traub, J Neurophysiol 89:909-921, 2003

COMMENT

	Implemented by Maciej Lazarewicz 2003 (mlazarew@seas.upenn.edu)

ENDCOMMENT

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

UNITS { 
	(mV) = (millivolt) 
	(mA) = (milliamp) 
} 
NEURON { 
	SUFFIX nap
	USEION na READ ena WRITE ina
	RANGE gbar, ina, minf, mtau, gna  :, vshift
}

PARAMETER { 
	gbar = 1e-4 	(mho/cm2)
	vshift = 0
	v ena 		(mV)  
} 
ASSIGNED { 
	ina 		(mA/cm2) 
	minf 		(1)
	mtau 		(ms) 
	gna		(mho/cm2)
} 
STATE {
	m
}

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

INITIAL { 
	settables(v-vshift) 
	m = minf
	:m = 0
} 

DERIVATIVE states { 
	settables(v-vshift) 
	m' = ( minf - m ) / mtau 
}
UNITSOFF
 
PROCEDURE settables(v) { 
	TABLE minf, mtau FROM -120 TO 40 WITH 641

	: minf  = 1 / ( 1 + exp( ( - v - 48 ) / 10 ) )
	minf  = 1 / ( 1 + exp( ( - v - 48 ) / 5 ) )
	if( v < -40.0 ) {
		mtau = 100*(0.025 + 0.14 * exp( ( v + 40 ) / 10 ))
	}else{
		mtau = 100*(0.02 + 0.145 * exp( ( - v - 40 ) / 10 ))
	}
}
UNITSON

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