Cell splitting in neural networks extends strong scaling (Hines et al. 2008)

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Accession:97917
Neuron tree topology equations can be split into two subtrees and solved on different processors with no change in accuracy, stability, or computational effort; communication costs involve only sending and receiving two double precision values by each subtree at each time step. Application of the cell splitting method to two published network models exhibits good runtime scaling on twice as many processors as could be effectively used with whole-cell balancing.
Reference:
1 . Hines ML, Eichner H, Schürmann F (2008) Neuron splitting in compute-bound parallel network simulations enables runtime scaling with twice as many processors. J Comput Neurosci 25:203-10 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Generic;
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Methods;
Implementer(s): Hines, Michael [Michael.Hines at Yale.edu];
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splitcell
nrntraub
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alphasyndiffeq.mod
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gabaa.mod
iclamp_const.mod *
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TITLE Potasium Type A current for RD Traub et al 2005

COMMENT
	A current for tuftIB (Intrinsic Bursting) cell.
	Modified by Tom Morse from below with a 2.6 times htau
	Implemention 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 ka_ib
	USEION k READ ek WRITE ik
	RANGE gbar, ik, m, h, alphah, betah, alpham, betam, mtau, htau
}
PARAMETER { 
	gbar = 0.0 	(mho/cm2)
	v (mV) ek 		(mV)  
} 
ASSIGNED { 
	ik 		(mA/cm2) 
	minf hinf 	(1)
	mtau (ms) htau 	(ms) 
	alphah (/ms) betah	(/ms)
	alpham (/ms) betam	(/ms)
} 
STATE {
	m h
}
BREAKPOINT { 
	SOLVE states METHOD cnexp
	ik = gbar * m * m * m * m * h * ( v - ek ) 
:	debugging:
	alphah = hinf/htau
	betah = 1/htau - alphah
	alpham = minf/mtau
	betam = 1/mtau - alpham
} 
INITIAL { 
	settables(v) 
	m  = minf
	m  = 0
	h  = hinf
} 
DERIVATIVE states { 
	settables(v) 
	m' = ( minf - m ) / mtau 
	h' = ( hinf - h ) / htau
}

UNITSOFF 

PROCEDURE settables(v(mV)) { 
	TABLE minf, hinf, mtau, htau  FROM -120 TO 40 WITH 641

	minf  = 1 / ( 1 + exp( ( - v - 60 ) / 8.5 ) )
	mtau = 0.185 + 0.5 / ( exp( ( v + 35.8 ) / 19.7 ) + exp( ( - v - 79.7 ) / 12.7 ) )
	hinf  = 1 / ( 1 + exp( ( v + 78 ) / 6 ) )
	if( v <= -63 ) {
		htau = 0.5 / ( exp( ( v + 46 ) / 5 ) + exp( ( - v - 238 ) / 37.5 ) )
	}else{
		htau = 9.5
	}
	htau = htau * 2.6
}

UNITSON

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