Olfactory Bulb Network (Davison et al 2003)

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Accession:2730
A biologically-detailed model of the mammalian olfactory bulb, incorporating the mitral and granule cells and the dendrodendritic synapses between them. The results of simulation experiments with electrical stimulation agree closely in most details with published experimental data. The model predicts that the time course of dendrodendritic inhibition is dependent on the network connectivity as well as on the intrinsic parameters of the synapses. In response to simulated odor stimulation, strongly activated mitral cells tend to suppress neighboring cells, the mitral cells readily synchronize their firing, and increasing the stimulus intensity increases the degree of synchronization. For more details, see the reference below.
Reference:
1 . Davison AP, Feng J, Brown D (2003) Dendrodendritic inhibition and simulated odor responses in a detailed olfactory bulb network model. J Neurophysiol 90:1921-1935 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Olfactory bulb;
Cell Type(s): Olfactory bulb main mitral cell; Olfactory bulb main interneuron granule MC cell;
Channel(s): I Na,t; I L high threshold; I A; I K; I K,leak; I M; I K,Ca; I Sodium; I Calcium; I Potassium;
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Oscillations; Synchronization; Spatio-temporal Activity Patterns; Olfaction;
Implementer(s): Davison, Andrew [Andrew.Davison at iaf.cnrs-gif.fr];
Search NeuronDB for information about:  Olfactory bulb main mitral cell; Olfactory bulb main interneuron granule MC cell; GabaA; AMPA; NMDA; I Na,t; I L high threshold; I A; I K; I K,leak; I M; I K,Ca; I Sodium; I Calcium; I Potassium; Gaba; Glutamate;
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bulbNet
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 *
mosinit.hoc *
odour_baseline.connect
odour_baseline.gnu *
odour_baseline.ses *
parameters_ddi_baseline.hoc *
parameters_odour_baseline.hoc *
screenshot.png *
tabchannels.dat *
tabchannels.hoc *
                            
// mathslib.hoc
// Collection of miscellaneous maths functions
// Andrew Davison, The Babraham Institute, 2000.

// =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
// mod(x,y)
// Returns a number between 0 and y-
// e.g. mod(7,4) returns 3

func mod() { local x	// 2 args - $1 mod $2
  x = $1
  while (x < 0) {
    x = x+$2
  }
  while (x >= $2) {
    x = x-$2
  }
  return x
}


// =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
// arraymax(M) and arraymin(M)
// Return maximum and minimum elements of a matrix object

objref mymaths_work
mymaths_work = new Vector()

func arraymax() { local i, max // arg - matrix object
  max = -1e10
  for i = 0,$o1.nrow-1 {
    mymaths_work = $o1.getrow(i)
    if (mymaths_work.max() > max) { max = mymaths_work.max() }
  }
  return max
}

func arraymin() { local i, min // arg - matrix object
  min = 1e10
  for i = 0,$o1.nrow-1 {
    mymaths_work = $o1.getrow(i)
    if (mymaths_work.min() < min) { min = mymaths_work.min() }
  }
  return min
}


// =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
// nint(x)
// Returns nearest integer
// e.g. nint(3.6) returns 4

func nint() {
  if ( abs($1 - int($1)) <= 0.5) {
    return int($1)
  } else {
    if ($1 < 0) { return int($1)-1 }
    if ($1 >= 0) { return int($1)+1 }
  }
}


// =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
// invabs(x)
// Returns absolute value of the inverse of the argument
// e.g. invabs(-2) returns 0.5

func invabs() {
  if ($1 == 0) {
    print "Error in invabs(): divide by zero"
    return 1e3
  } else {
    return abs(1.0/$1)
  }
}


// =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=

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