A multi-compartment model for interneurons in the dLGN (Halnes et al. 2011)

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This model for dLGN interneurons is presented in two parameterizations (P1 & P2), which were fitted to current-clamp data from two different interneurons (IN1 & IN2). The model qualitatively reproduces the responses in IN1 & IN2 under 8 different experimental condition, and quantitatively reproduces the I/O-relations (#spikes elicited as a function of injected current).
1 . Halnes G, Augustinaite S, Heggelund P, Einevoll GT, Migliore M (2011) A Multi-Compartment Model for Interneurons in the Dorsal Lateral Geniculate Nucleus PLoS Comp. Biol. 7(9):e1002160 [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): Thalamus lateral geniculate nucleus interneuron;
Channel(s): I L high threshold; I T low threshold; I CAN; I Sodium; I Mixed; I Potassium; I_AHP;
Gap Junctions:
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Active Dendrites; Detailed Neuronal Models; Rebound firing;
Implementer(s): Halnes, Geir [geir.halnes at nmbu.no];
Search NeuronDB for information about:  I L high threshold; I T low threshold; I CAN; I Sodium; I Mixed; I Potassium; I_AHP;
Cad.mod *
HH_traub.mod *
iahp.mod *
iar.mod *
ical.mod *
Ican.mod *
it2.mod *
091008A2.hoc *
fixnseg.hoc *
/* Sets nseg in each section to an odd value
   so that its segments are no longer than 
     d_lambda x the AC length constant
   at frequency freq in that section.

   Be sure to specify your own Ra and cm before calling geom_nseg()

   To understand why this works, 
   and the advantages of using an odd value for nseg,
   see  Hines, M.L. and Carnevale, N.T.
        NEURON: a tool for neuroscientists.
        The Neuroscientist 7:123-135, 2001.

// these are reasonable values for most models
freq = 100      // Hz, frequency at which AC length constant will be computed
d_lambda = 0.1

func lambda_f() { local i, x1, x2, d1, d2, lam
        if (n3d() < 2) {
                return 1e5*sqrt(diam/(4*PI*$1*Ra*cm))
// above was too inaccurate with large variation in 3d diameter
// so now we use all 3-d points to get a better approximate lambda
        x1 = arc3d(0)
        d1 = diam3d(0)
        lam = 0
        for i=1, n3d()-1 {
                x2 = arc3d(i)
                d2 = diam3d(i)
                lam += (x2 - x1)/sqrt(d1 + d2)
                x1 = x2   d1 = d2
        //  length of the section in units of lambda
        lam *= sqrt(2) * 1e-5*sqrt(4*PI*$1*Ra*cm)

        return L/lam

proc geom_nseg() {
  soma area(0.5) // make sure diam reflects 3d points
  forall { nseg = int((L/(d_lambda*lambda_f(freq))+0.9)/2)*2 + 1  }

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