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
                            
// experiment_odour_baseline.hoc
// Olfactory bulb network model: experiment file
//   for odour input.
// Andrew Davison, The Babraham Institute, 2000.

xopen("calcisilag.hoc")

// Redefine run() procedure
proc run() {
  running_ = 1
  set_no_input()
  add_uniform_input(-0.1,0.3)
  stdinit()
  continuerun(ttrans)
  add_fixed_input(inputnumber,maxinput)
  continuerun(tstop)
  show_results()
}
print "Note: run() procedure redefined."

// Process data and print out results
proc print_results() {
  print_raster(fileroot)
  print_gran_raster(fileroot)
  print_smooth_hist(minisi()/4,fileroot)
  print_gran_smooth_hist(minisi()/4,fileroot)
  print_isi_stats(fileroot)
  print_si(fileroot)
  print "Results printed to files."
}

// Show results on screen
objref gResults[4], yvec
proc show_results() { local i,j
  yvec = new Vector()
  for i = 0,3 {
    gResults[i] = new Graph(0)
  }
  for i = 0, nmitx-1 {
    for j = 0, nmity-1 {
      k = i*nmity + j
      yvec.resize(mit[i][j].spiketimes.size())
      yvec.fill(k)
      yvec.mark(gResults[0],mit[i][j].spiketimes,"|",3)
    }
  }
  gResults[0].label(0.3,0.9,"Mitral cell spike plot")
  gResults[0].view(0,-0.1*nmitx*nmity,tstop,nmitx*nmity*1.2,400,0,500,150)
  calc_smooth_hist(minisi()/4)
  hist.plot(gResults[1])
  gResults[1].label(0.3,0.9,"Mitral cell spike time histogram")
  gResults[1].view(0,-0.1*hist.max(),tstop,hist.max()*1.2,400,220,500,150)
  for i = 0, ngranx-1 {
    for j = 0, ngrany-1 {
      k = i*ngrany + j
      yvec.resize(gran[i][j].spiketimes.size())
      yvec.fill(k)
      yvec.mark(gResults[2],gran[i][j].spiketimes,"|",1)
    }
  }
  gResults[2].label(0.3,0.9,"Granule cell spike plot")
  gResults[2].view(0,-0.1*ngranx*ngrany,tstop,ngranx*ngrany*1.2,400,440,500,150)
  calc_gran_smooth_hist(minisi()/4)
  hist.plot(gResults[3])
  gResults[3].label(0.3,0.9,"Granule cell spike time histogram")
  gResults[3].view(0,-0.1*hist.max(),tstop,hist.max()*1.2,400,660,500,150)
}

xopen("odour_baseline.ses")

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