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
                            
COMMENT
alphasynkint.mod
Alpha Synapse Traub-like implemented with Kinetic Scheme as per 
Chapter 10 NEURON book
Used to return peak conductance of 1, however now it is set so that 
a peak conductance of tau2*exp(-1) is reached because that's what
the Traub alpha function (t-t_0)*exp(-(t-t_0)/tau) reaches..
ENDCOMMENT
NEURON {
	POINT_PROCESS AlphaSynKinT : ending T is for Traub, see notes
	RANGE tau, e, i
	NONSPECIFIC_CURRENT i
}

UNITS {
	(nA) = (nanoamp)
	(mV) = (millivolt)
	(uS) = (microsiemens)
}

PARAMETER {
	tau = 0.1 (ms) <1e-9,1e9>
	e = 0	(mV)
}

ASSIGNED {
	v (mV)
	i (nA)
}

STATE { a (microsiemens) g (uS) }

INITIAL {
	g=0
}

BREAKPOINT {
	SOLVE state METHOD sparse
	i = g*(v - e)
}

KINETIC state {
	~ a <-> g (1/tau, 0)
	~ g -> (1/tau)
}

NET_RECEIVE(weight (uS)) {
:	a = a + weight*exp(1) * (tau*exp(-1))
: the above last factor changes peak conductance to from
: 1 to tau*exp(-1) so formula becomes:
	a = a + weight*tau*1(/ms)
}

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