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]
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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
                            
load_file("nrngui.hoc")
objectvar save_window_, rvp_
objectvar scene_vector_[5]
objectvar ocbox_, ocbox_list_, scene_, scene_list_
{ocbox_list_ = new List()  scene_list_ = new List()}
{pwman_place(0,0,0)}
{
save_window_ = new Graph(0)
save_window_.size(0,1500,-80,40)
scene_vector_[2] = save_window_
{save_window_.view(0, -80, 1500, 120, 393, 80, 650.88, 200.32)}
graphList[0].append(save_window_)
save_window_.save_name("graphList[0].")
save_window_.addvar("mit[0][0].soma.v( 0.5 )", 2, 1, 0.8, 0.9, 2)
}
{
save_window_ = new Graph(0)
save_window_.size(0,1500,-80,40)
scene_vector_[3] = save_window_
{save_window_.view(0, -80, 1500, 120, 393, 341, 653.76, 200.32)}
graphList[0].append(save_window_)
save_window_.save_name("graphList[0].")
save_window_.addvar("gran[0][6].periph.v( 0.5 )", 3, 1, 0.8, 0.9, 2)
}
{
save_window_ = new Graph(0)
save_window_.size(0,1500,-3.50,0)
scene_vector_[4] = save_window_
{save_window_.view(0, 0, 1500, 3.5, 393, 602, 651.84, 200.32)}
graphList[1].append(save_window_)
save_window_.save_name("graphList[1].")
save_window_.addvar("mit[3][3].GABAA.i", 2, 1, 0.8, 0.9, 2)
}
{
xpanel("RunControl", 0)
v_init = -65
xvalue("Init","v_init", 1,"stdinit()", 1, 1 )
xbutton("Init & Run","run()")
xbutton("Stop","stoprun=1")
runStopAt = 5
xvalue("Continue til","runStopAt", 1,"{continuerun(runStopAt) stoprun=1}", 1, 1 )
runStopIn = 1
xvalue("Continue for","runStopIn", 1,"{continuerun(t + runStopIn) stoprun=1}", 1, 1 )
xbutton("Single Step","steprun()")
t = 1500
xvalue("t","t", 2 )
tstop = 1500
xvalue("Tstop","tstop", 1,"tstop_changed()", 0, 1 )
dt = 0.05
xvalue("dt","dt", 1,"setdt()", 0, 1 )
steps_per_ms = 10
xvalue("Points plotted/ms","steps_per_ms", 1,"setdt()", 0, 1 )
xcheckbox("Quiet",&stdrun_quiet,"")
realtime = 511
xvalue("Real Time","realtime", 0,"", 0, 1 )
xpanel(107,288)
}
objectvar scene_vector_[1]
{doNotify()}