Spiny neuron model with dopamine-induced bistability (Gruber et al 2003)

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Accession:39949
These files implement a model of dopaminergic modulation of voltage-gated currents (called kir2 and caL in the original paper). See spinycell.html for details of usage and implementation. For questions about this implementation, contact Ted Carnevale (ted.carnevale@yale.edu)
Reference:
1 . Gruber AJ, Solla SA, Surmeier DJ, Houk JC (2003) Modulation of striatal single units by expected reward: a spiny neuron model displaying dopamine-induced bistability. J Neurophysiol 90:1095-114 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Neostriatum medium spiny direct pathway GABA cell;
Channel(s): I K,leak; I h; I A, slow;
Gap Junctions:
Receptor(s): D1; Dopaminergic Receptor;
Gene(s): Cav1.3 CACNA1D; D1 DRD1A;
Transmitter(s): Dopamine;
Simulation Environment: NEURON;
Model Concept(s): Intrinsic plasticity;
Implementer(s): Carnevale, Ted [Ted.Carnevale at Yale.edu];
Search NeuronDB for information about:  Neostriatum medium spiny direct pathway GABA cell; D1; Dopaminergic Receptor; I K,leak; I h; I A, slow; Dopamine;
// Reproduces figure 6.
// Uses different stats for the synaptic drive xi, but this isn't critical.

load_file("nrngui.hoc")
// to make it easier to take advantage of the GUI tools for setting up the afferent 
// "baseline" and "test" synaptic drive, this specifies the biological model by using 
// the NetReadyCellGUI tool's CellBuilder instead of the 
// Main Menu's simple Build / single compartment
load_file("fig6netcells.ses")  // defines the biophysical model cell and some artificial spiking cells 
                           // that are used to generate "baseline" synaptic input and "test" input
load_file("fig6net.ses")  // specifies the architecture of the "network"--a biophysical model cell
                      // that receives two inputs--one a sustained "baseline" drive,
                      // and the other a "test" input that lasts 400 ms
load_file("fig6netrig.ses")

mu = 1

C_Cell[0].soma for (x,0) { // skip the nodes at 0 and 1
	setpointer mu_caL(x), mu
	setpointer mu_kir2(x), mu
}


// final adjustments to all biophysical parameters--
// specifically temperature, ek, and ca concentrations
celsius = 20
forall {
  if (ismembrane("k_ion")) ek = -90
  if (ismembrane("ca_ion")) {
    cai0_ca_ion = 1e-5
    cai = 1e-5
    cao0_ca_ion = 2
    cao = 2
  }
}

/*
ExpSyn's gs = gmax exp(-t/tau), which has area gmax tau.
If this occurs at a mean interval of T, 
mean synaptic conductance is gmax tau/T.
If gmax is 1 pS, tau = 1 ms, T = 10 ms, 
then mean synaptic conductance is 0.1 pS.

This model has surface area of 100 um2, 
so a synaptic conductance density of 1 uS/cm2 
is equivalent to a total conductance of 
1 (uS/cm2) * 100 um2 * (1 cm2/1e8 um2) = 1e-6 uS = 1 pS.

Gruber et al. use baseline synaptic conductance density = 3 uS/cm2, 
so this model must have a mean synaptic conductance of 3 pS.
This can be done with gmax 10 pS, tau 3 ms, T 10 ms.
So Bkgd_NetStim[0] fires with mean interval of 10 ms, 
starting at 100 ms, with noise 0.2.

Gruber et al. use test stimuli that achieve 
mean synaptic conductance densities of 10-22.5 uS/cm2.
This model achieves 10 uS/cm2 with gmax 10 pS, 
tau 3 ms (ExpSyn[0].tau is 3 ms), T 3 ms.

The connections to C_Cell[0] both have weight 1e-5, 
so ExpSyn's gmax = 1e-5 uS = 10 pS.

This       connects         to
NetCon[0]  Bkgd_NetStim[0]  C_Cell[0]
NetCon[1]  Stim_NetStim[0]  C_Cell[0]
NetCon[2]  Stop_NetStim[0]  Stim_NetStim[0]
*/


// two params:  mean intervals of bkgd and test stimuli

proc setparams() {
  // the background input
  Bkgd_NetStim[0].pp.interval = $1	// ms
  Bkgd_NetStim[0].pp.number = 1e9
  Bkgd_NetStim[0].pp.start = 100	// ms
  Bkgd_NetStim[0].pp.noise = 0.2
  NetCon[0].weight = 1e-5	// uS, i.e. 10 pS
  // the test stimulus
  Stim_NetStim[0].pp.interval = $2	// ms
  Stim_NetStim[0].pp.number = 1e9
  Stim_NetStim[0].pp.start = 400	// ms
  Stim_NetStim[0].pp.noise = 0.2
  NetCon[1].weight = 1e-5	// uS, i.e. 10 pS
  // turns off the test stimulus
  Stop_NetStim[0].pp.interval = 10	// ms
  Stop_NetStim[0].pp.number = 1
  Stop_NetStim[0].pp.start = 800	// ms
  Stop_NetStim[0].pp.noise = 0
  NetCon[2].weight = -2
}


objref g
g = new Graph(0)
g.view(0, -90, 1200, 60, 786, 168, 300.48, 200.32)
g.addvar("C_Cell[0].soma.v( 0.5 )", 1, 1, 0.483706, 0.947923, 2)
graphList[0].append(g)

proc onerun() {
  g.exec_menu("Keep Lines")
  run()
  g.exec_menu("Keep Lines")
}


cvode_active(1)

gs_bkgd = 3	// uS/cm2--"baseline"
gs_mean = 0	// during test stimulus
NUMRUNS = 1	// how many for each gs_mean

proc batchrun() { local ii, gmax, tau, Tstim, Tbkgd
  g.exec_menu("Erase")

  // calc ISI for background input
  tau = ExpSyn[0].tau
  gmax = NetCon[0].weight  // this comes from the "Bkgd" cell
  Tbkgd = gmax * tau * 1e6 / gs_bkgd


  // for each test input, calc ISI and do a run
  gmax = NetCon[1].weight  // this comes from the "Stim" cell

/*
// control runs, i.e. with test input == 0 so only background is present
  Stim_NetStim[0].pp.start = 1e9  // "never"
  for ii = 0,NUMRUNS-1 onerun()
// prepare for test runs
  Stim_NetStim[0].pp.start = 300
*/

// runs with nonzero test input
  for case (&gs_mean, 10, 12.5, 15, 17.5, 20, 22.5) {
    // adjust ISI of test input so gs_mean = gs_bkgd + gs_stim reaches desired value
    if (gs_mean > gs_bkgd) {
      Tstim = gmax * tau * 1e6 / (gs_mean-gs_bkgd)
      setparams(Tbkgd, Tstim)
      for ii = 0,NUMRUNS-1 onerun()
    }
  }
}


xpanel("Fig. 6")
  xbutton("Fig. 6a","gs_bkgd=3 batchrun()")
  xbutton("Fig. 6b","gs_bkgd=10 batchrun()")
xpanel(785,435)

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