Regulation of firing frequency in a midbrain dopaminergic neuron model (Kuznetsova et al. 2010)

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Accession:127507
A dopaminergic (DA) neuron model with a morphologicaly realistic dendritic architecture. The model captures several salient features of DA neurons under different pharmacological manipulations and exhibits depolarization block for sufficiently high current pulses applied to the soma.
Reference:
1 . Kuznetsova AY, Huertas MA, Kuznetsov AS, Paladini CA, Canavier CC (2010) Regulation of firing frequency in a computational model of a midbrain dopaminergic neuron. J Comput Neurosci 28:389-403 [PubMed]
Citations  Citation Browser
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: Basal ganglia;
Cell Type(s): Substantia nigra pars compacta DA cell;
Channel(s): I Na,t; I L high threshold; I N; I T low threshold; I A; I K; I K,Ca; Na/K pump;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Temporal Pattern Generation; Oscillations;
Implementer(s): Huertas, Marco [huertas.marco at gmail.com];
Search NeuronDB for information about:  Substantia nigra pars compacta DA cell; I Na,t; I L high threshold; I N; I T low threshold; I A; I K; I K,Ca; Na/K pump;
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KuznetsovaEtAl2010
README.html
cabal.mod *
nabalan.mod *
newcachan.mod *
newhh3.mod
newkca.mod
newleak.mod
pump.mod *
stim.mod
dopaminergic.hoc
Fig2a2.hoc
Fig2b2.hoc
Fig2f2.hoc
Fig6a_dashed-line_trace.hoc
Fig6a_solid_trace.hoc
final.hoc
fixnseg.hoc *
mosinit.hoc
run_and_graph.ses
screenshot2a2.jpg
screenshot2b2.jpg
screenshot2f2.jpg
screenshot6a_d.jpg
screenshot6a_s.jpg
                            
/* 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  }

}