Determinants of the intracellular and extracellular waveforms in DA neurons (Lopez-Jury et al 2018)

 Download zip file   Auto-launch 
Help downloading and running models
Accession:244599
To systematically address the contribution of AIS, dendritic and somatic compartments to shaping the two-component action potentials (APs), we modeled APs of male mouse and rat dopaminergic neurons. A parsimonious two-domain model, with high (AIS) and lower (dendro-somatic) Na+ conductance, reproduced the notch in the temporal derivatives, but not in the extracellular APs, regardless of morphology. The notch was only revealed when somatic active currents were reduced, constraining the model to three domains. Thus, an initial AIS spike is followed by an actively generated spike by the axon-bearing dendrite (ABD), in turn followed mostly passively by the soma. Larger AISs and thinner ABD (but not soma-to-AIS distance) accentuate the AIS component.
Reference:
1 . López-Jury L, Meza RC, Brown MTC, Henny P, Canavier CC (2018) Morphological and Biophysical Determinants of the Intracellular and Extracellular Waveforms in Nigral Dopaminergic Neurons: A Computational Study. J Neurosci 38:8295-8310 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Extracellular; Dendrite; Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Basal ganglia;
Cell Type(s): Substantia nigra pars compacta DA cell;
Channel(s): I Calcium; I K,Ca; Na/K pump; I L high threshold; I T low threshold; I A; I N; I Na,t; I K;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Action Potential Initiation; Pacemaking mechanism; Temporal Pattern Generation; Oscillations; Extracellular Fields;
Implementer(s): Lopez-Jury, Luciana [lucianalopezjury at gmail.com]; Canavier, CC;
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; I Calcium; Na/K pump;
/
DAnotch_AIS
images
README.html
cabal.mod *
nabalan.mod *
newcachan.mod *
newkca.mod *
newleak.mod
pump.mod *
slowhh3.mod
xtra.mod
anatscale.hoc *
calcrxc.hoc
cellrig.ses
field.hoc *
fixnseg.hoc *
iclampc.ses
initxrec.hoc
interpxyz.hoc *
morph_mouse.hoc
morph_rat.hoc
mosinit.hoc
msgic.hoc
parameters.hoc
rigc.ses
setpointers.hoc *
varstep.ses
vrecc.ses
                            
/* 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  }

}



Loading data, please wait...