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 Comput Neurosci 21:119-29 [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 KM channel
: Hodgkin - Huxley KM channel with parameters from
: US Bhalla and JM Bower, J. Neurophysiol. 69:1948-1983 (1993)
: Andrew Davison, The Babraham Institute, 1998.

NEURON {
	SUFFIX kM
	USEION k READ ek WRITE ik
	RANGE gkbar, ik
	GLOBAL xinf, xtau
}

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

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

PARAMETER {
	v (mV)
	dt (ms)
	gkbar=.036 (mho/cm2) <0,1e9>
	ek = -70 (mV)
}
STATE {
	x
}
ASSIGNED {
	ik (mA/cm2)
	xinf
	xtau (ms)
}

INITIAL {
	rates(v)
	x = xinf
}

BREAKPOINT {
	SOLVE states METHOD cnexp
	ik = gkbar*x*(v - ek)
}

DERIVATIVE states {
	rates(v)
	x' = (xinf - x)/xtau
}

PROCEDURE rates(v(mV)) {
	TABLE xinf, xtau FROM -100 TO 100 WITH 200
	xinf = 1/(1 + exp(-(v*1(/mV) + 35)/5))
	xtau = 1000(ms)/(3.3*exp((v*1(/mV) + 35)/40) + exp(-(v*1(/mV) + 35)/20))
}


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