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 slow potassium channel with FUNCTION_TABLEs
: Hodgkin - Huxley potassium channel using the data given in
: US Bhalla and JM Bower, J. Neurophysiol. 69:1948-1983 (1993)
: Needs the files tabchannels.dat and tabchannels.hoc
: Andrew Davison, The Babraham Institute, 1998.


NEURON {
	SUFFIX kslowtab
	USEION k READ ek WRITE ik
	RANGE gkbar, ik
	GLOBAL ninf, kinf, ntau, ktau
}

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

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

PARAMETER {
	v (mV)
	dt (ms)
	gkbar= 0.120 (mho/cm2) <0,1e9>
	ek = -70 (mV)
}
STATE {
	n k
}
ASSIGNED {
	ik (mA/cm2)
	ninf
	kinf
	ntau (ms)
	ktau (ms)
}

INITIAL {
	rates(v)
	n = ninf
	k = kinf
}

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

DERIVATIVE states {
	rates(v)
	n' = (ninf - n)/ntau
	k' = (kinf - k)/ktau
}

FUNCTION_TABLE tabninf(v(mV))
FUNCTION_TABLE tabntau(v(mV)) (ms)
FUNCTION_TABLE tabkinf(v(mV))
FUNCTION_TABLE tabktau(v(mV)) (ms)

PROCEDURE rates(v(mV)) {
	ninf = tabninf(v)
	ntau = tabntau(v) 
	kinf = tabkinf(v)
	ktau = tabktau(v)
}

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