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];
/
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
                            
// granule.tem
// Template for three-compartment granule cell model
// Andrew Davison, The Babraham Institute, 2000

begintemplate Gran
  public soma, periph, deep, AMPAr, NMDAr, spiketimes, spikecount
  create soma, periph, deep, s2d, s2p
  objref AMPAr, NMDAr, spiketimes, spikecount

  proc init() { local Len, Erest, RM, p, q, Atotal, gsp, gsd, AMPAtau, NMDAalpha, NMDAbeta, Erev, rsd, rsp
    create soma, periph, deep, s2d, s2p

    spiketimes = new Vector()
    lastspikecount = 0

    Erest		= -65		// mV
    Atotal		= 8353		// um2
    gsp			= 3.08e-10	// S/cm2
    gsd			= 4.34e-10
    RM			= 120000	// ohm.cm2
    Len			= 50
    p			= 0.0136
    q			= 0.308
    rsd 		= 1/(gsd*Atotal)
    rsp 		= 1/(gsp*Atotal)
    NMDAalpha		= 0.0163	// ms-1
    NMDAbeta		= 0.00292	// ms-1
    AMPAtau		= 5.5		// ms
    Erev		= 0		// mV

    soma {
      L 		= Len
      diam 		= p*Atotal/(PI*Len)
      Ra 		= PI/(4*Len*Atotal)
      insert pas
      e_pas 		= Erest		// reversal potential mV
      g_pas 		= 1/RM 		// membrane conductance S/cm2
      insert nagrantab
      insert kslowtab
      insert kM
      insert kA
      gnabar_nagrantab 	= 0.1611	// S/cm2
      gkbar_kslowtab 	= 0.1313
      gkbar_kM 		= 0.1334
      gkbar_kA 		= 0.0088
    }
    periph {
      L 		= Len
      diam 		= q*Atotal/(PI*Len)
      Ra 		= PI/(4*Len*Atotal)
      insert pas
      e_pas 		= Erest
      g_pas 		= 1/RM
      insert nagrantab
      insert kslowtab
      gnabar_nagrantab 	= 0.1355
      gkbar_kslowtab 	= 0.0243
      AMPAr = new ExpSyn(0.5)
      AMPAr.tau 	= AMPAtau
      AMPAr.e 		= Erev
      NMDAr = new NMDA(0.5)
      NMDAr.Alpha	= NMDAalpha
      NMDAr.Beta	= NMDAbeta
      NMDAr.e		= Erev
      spikecount = new APCount(0.5)
      spikecount.thresh = -30
      spikecount.record(spiketimes)
    }
    deep {
      L 		= Len
      diam 		= (1-p-q)*Atotal/(PI*Len)
      Ra 		= PI/(4*Len*Atotal)
      insert pas
      e_pas 		= Erest
      g_pas 		= 1/RM
    }
    s2d { 
      diam 		= 1
      Ra = PI*diam*diam/(4*Len*Atotal) * ( 1/gsd )
      L 		= 1
    }
    s2p { 
      diam 		= 1
      Ra = PI*diam*diam/(4*Len*Atotal) * ( 1/gsp )
      L 		= 1
    }

    soma connect s2p(0), 0
    s2p connect periph(0), 1
    soma connect s2d(0), 1
    s2d connect deep(0), 1

    // set reversal potentials, etc.
    forall if (ismembrane("na_ion")) {
      ena = 45	// mV
    }
    forall if (ismembrane("k_ion")) {
      ek  = -70	//  mV
    }

  }

endtemplate Gran

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