Parallel network simulations with NEURON (Migliore et al 2006)

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Accession:64229
The NEURON simulation environment has been extended to support parallel network simulations. The performance of three published network models with very different spike patterns exhibits superlinear speedup on Beowulf clusters.
Reference:
1 . Migliore M, Cannia C, Lytton WW, Markram H, Hines ML (2006) Parallel Network Simulations with NEURON. J Comp Neurosci 21:110-119 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
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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netmod
parbulbNet
README *
cadecay.mod *
flushf.mod *
kA.mod *
kca.mod *
kfasttab.mod *
kM.mod *
kslowtab.mod *
lcafixed.mod *
nafast.mod *
nagran.mod *
nmdanet.mod *
bulb.hoc
calcisilag.hoc *
ddi_baseline.gnu *
ddi_baseline.ses *
experiment_ddi_baseline.hoc *
experiment_odour_baseline.hoc *
granule.tem *
init.hoc *
input.hoc *
input1 *
mathslib.hoc *
mitral.tem *
modstat
mosinit.hoc *
odour_baseline.gnu *
odour_baseline.ses *
par_batch1.hoc
par_bulb.hoc
par_calcisilag.hoc
par_experiment_ddi_baseline.hoc
par_granule.tem
par_init.hoc
par_input.hoc
par_mitral.tem
par_netpar.hoc
par_notes
parameters_ddi_baseline.hoc *
parameters_odour_baseline.hoc *
screenshot.png *
tabchannels.dat *
tabchannels.hoc *
test1.sh
                            
TITLE HH fast sodium channel
: Hodgkin - Huxley sodium channel with parameters from US Bhalla and JM Bower,
: J. Neurophysiol. 69:1948-1983 (1993)
: Andrew Davison, The Babraham Institute, 1998.

NEURON {
	SUFFIX nafast
	USEION na READ ena WRITE ina
	RANGE gnabar, ina
	GLOBAL minf, hinf, mtau, htau
}

UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
}

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

PARAMETER {
	v (mV)
	dt (ms)
	gnabar = 0.120 (mho/cm2) <0,1e9>
	ena = 45 (mV)
}
STATE {
	m h
}
ASSIGNED {
	ina (mA/cm2)
	minf
	hinf
	mtau (ms)
	htau (ms)
}

INITIAL {
	rates(v)
	m = minf
	h = hinf
}

BREAKPOINT {
	SOLVE states METHOD cnexp
	ina = gnabar*m*m*m*h*(v - ena)
}

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

FUNCTION alp(v(mV),i) (/ms) {
	if (i==0) {
		alp = 0.32(/ms)*expM1(-(v *1(/mV) + 42), 4)
	}else if (i==1){
		alp = 0.128(/ms)/(exp((v *1(/mV) + 38)/18))
	}
}

FUNCTION bet(v(mV),i)(/ms) {
	if (i==0) {
		bet = 0.28(/ms)*expM1(v *1(/mV) + 15, 5)
	}else if (i==1){
		bet = 4(/ms)/(exp(-(v* 1(/mV) + 15)/5) + 1)
	}
}

FUNCTION expM1(x,y) {
	if (fabs(x/y) < 1e-6) {
		expM1 = y*(1 - x/y/2)
	}else{
		expM1 = x/(exp(x/y) - 1)
	}
}

PROCEDURE rates(v(mV)) {LOCAL a, b
	TABLE minf, hinf, mtau, htau FROM -100 TO 100 WITH 200
	a = alp(v,0)  b=bet(v,0)
	mtau = 1/(a + b)
	minf = a/(a + b)
	a = alp(v,1)  b=bet(v,1)
	htau = 1/(a + b)
	hinf = a/(a + b)
}

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