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]
Citations  Citation Browser
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
ar.mod *
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 *
kdr_fs.mod *
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 Second Sodium transient current for Traub RD et al 2005

COMMENT
	This sodium current was present in Gabaergic interneurons and spiny stellate
	cells in the Traub et al 2005 model: deepaxax, deepbask, deepLTS, spinstell,
	supaxax, supbask, supLTS, nRT
	Tom Morse 3/8/2006
	Modified from the
	Implementation of naf by Maciej Lazarewicz 2003 (mlazarew@seas.upenn.edu)
	made for RD Traub, J Neurophysiol 89:909-921, 2003
ENDCOMMENT

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

UNITS { 
	(mV) = (millivolt) 
	(mA) = (milliamp) 
} 
NEURON { 
	SUFFIX naf2
	USEION na READ ena WRITE ina
	RANGE gbar, ina,m, h, df, fastNa_shift, a, b, c, d, minf, mtau
}
PARAMETER { 
	fastNa_shift = 0 : 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 hinf 	   (1)
	mtau (ms) htau 	   (ms)
	df	(mV)
} 
STATE {
	m h
}
BREAKPOINT { 
	SOLVE states METHOD cnexp
	ina = gbar * m * m * m * h * ( v - ena ) 
	df = v - ena
} 
INITIAL { 
	settables( v )
	m = minf
	m = 0
	h  = hinf
} 
DERIVATIVE states { 
	settables( v ) 
	m' = ( minf - m ) / mtau 
	h' = ( hinf - h ) / htau
}

UNITSOFF 

PROCEDURE settables(v1(mV)) {

	TABLE minf, hinf, mtau, htau  FROM -120 TO 40 WITH 641

	minf  = 1 / ( 1 + exp( ( - ( v1 + fastNa_shift ) - 38 ) / 10 ) )
	if( ( v1 + fastNa_shift ) < -30.0 ) {
		mtau = 0.0125 + 0.1525 * exp( ( ( v1 + fastNa_shift ) + 30 ) / 10 )
	} else{
		mtau = 0.02 + a + (0.145+ b) * exp( ( - ( v1 + fastNa_shift +d ) - 30 ) / (10+c) ) 
	}

	: hinf, and htau are shifted 3.5 mV comparing to the paper

	hinf  = 1 / ( 1 + exp( ( ( v1 + fastNa_shift * 0 ) + 58.3 ) / 6.7 ) )
	htau = 0.225 + 1.125 / ( 1 + exp( ( ( v1 + fastNa_shift * 0 ) + 37 ) / 15 ) )
}

UNITSON