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
mod
alphasyndiffeq.mod
alphasynkin.mod *
alphasynkint.mod *
ampa.mod
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cad.mod *
cal.mod *
cat.mod *
cat_a.mod *
gabaa.mod
iclamp_const.mod *
k2.mod *
ka.mod *
ka_ib.mod *
kahp.mod *
kahp_deeppyr.mod *
kahp_slower.mod *
kc.mod *
kc_fast.mod *
kdr.mod *
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km.mod *
naf.mod *
naf_tcr.mod
naf2.mod *
nap.mod *
napf.mod *
napf_spinstell.mod *
napf_tcr.mod *
par_ggap.mod *
pulsesyn.mod *
rampsyn.mod *
rand.mod *
ri.mod
traub_nmda.mod
                            
TITLE Sodium persistent current for RD Traub et al 2003, 2005

COMMENT

	This persistent sodium current is based on the activation
	permissive quantity, m, from the transient sodium channel. -TMM
	modified from an
	Implementation by Maciej Lazarewicz 2003 (mlazarew@seas.upenn.edu)
	fastNashift init to 0 and removed from arg modification Tom Morse 3/8/2006
	(for Traub et al 2005)

ENDCOMMENT

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

UNITS { 
	(mV) = (millivolt) 
	(mA) = (milliamp) 
} 
NEURON { 
	SUFFIX napf
	USEION na READ ena WRITE ina
	RANGE gbar, ina,m, df, fastNa_shift, a, b, c, d, minf, mtau
}
PARAMETER { 
	fastNa_shift = 0 (mV): orig -3.5 (mV)
	a = 0 (1)
	b = 0 (1)
	c = 0 (1)
	d = 0 (1)
	gbar = 0.0 	   (mho/cm2)
	v (mV) ena 		   (mV)  
} 
ASSIGNED { 
	ina 		   (mA/cm2) 
	minf 	   (1)
	mtau 	   (ms)
	df	(mV)
} 
STATE {
	m
}
BREAKPOINT { 
	SOLVE states METHOD cnexp
	ina = gbar * m * m * m * ( v - ena ) 
	df = v - ena
} 
INITIAL { 
	settables( v )
	m = minf
	m = 0
} 
DERIVATIVE states { 
	settables( v ) 
	m' = ( minf - m ) / mtau 
}

UNITSOFF 

PROCEDURE settables(v1(mV)) {

	TABLE minf, mtau  FROM -120 TO 40 WITH 641

	minf  = 1 / ( 1 + exp( ( - ( v1 + fastNa_shift ) - 38 ) / 10 ) )
	if( ( v1 + fastNa_shift ) < -30.0 ) {
		mtau = 0.025 + 0.14 * exp( ( ( v1 + fastNa_shift ) + 30 ) / 10 )
	} else {
		mtau = 0.02 + a + (0.145+ b) * exp( ( - ( v1 + fastNa_shift +d ) - 30 ) / (10+c) ) 
	}

}

UNITSON

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