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
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 Calcium low threshold T type current version a for RD Traub 2005

COMMENT
	This current is found in the model deepLTS cells
	Traub made this model modifying Huguenard and Prince 1992 as
	appeared on Destexhe et al 1996 (in vivo in vitro ...)
	Modification by Tom Morse for Traub et al 2005
	modified from
	Implementation by Maciej Lazarewicz 2003 (mlazarew@seas.upenn.edu)
	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 cat_a
	NONSPECIFIC_CURRENT i   : not causing [Ca2+] influx
	RANGE gbar, i, m, h, alphah, betah 	: m,h, alphah, betah for comparison with FORTRAN
}

PARAMETER { 
	gbar = 0.0 	(mho/cm2)
	v 		(mV)  
}
 
ASSIGNED { 
	i 		(mA/cm2) 
	minf hinf 	(1)
	mtau (ms) htau 	(ms) 
	alphah (/ms) betah	(/ms)
}
 
STATE {
	m h
}

BREAKPOINT { 
	SOLVE states METHOD cnexp 
	i = gbar * m * m * h * ( v - 125 ) 
	alphah = hinf/htau
	betah = 1/htau - alphah
}
 
INITIAL { 
	settables(v) 
:	m  = minf
	h  = hinf
	m  = 0
} 

DERIVATIVE states { 
	settables(v) 
	m' = ( minf - m ) / mtau 
	h' = ( hinf - h ) / htau
}

UNITSOFF 

PROCEDURE settables(v(mV)) { 
	TABLE minf, mtau,hinf, htau FROM -120 TO 40 WITH 641
        minf  = 1 / ( 1 + exp( ( -v - 52 ) / 7.4 ) )
        mtau  = 1 + .33 / ( exp( ( v + 27.0 ) / 10.0 ) + exp( ( - v - 102 ) / 15.0 ) )

        hinf  = 1 / ( 1 + exp( ( v + 80 ) / 5 ) )
        htau = 28.30 +.33 / (exp(( v + 48.0)/ 4.0) + exp( ( -v - 407.0) / 50.0 ) )

}

UNITSON

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