VTA neurons: Morphofunctional alterations in acute opiates withdrawal (Enrico et al. 2016)

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Accession:185330
" ... Here we present a biophysical model of a DA VTA neuron based on 3D morphological reconstruction and electrophysiological data, showing how opiates withdrawal-driven morphological and electrophysiological changes could affect the firing rate and discharge pattern...."
Reference:
1 . Enrico P, Migliore M, Spiga S, Mulas G, Caboni F, Diana M (2016) Morphofunctional alterations in ventral tegmental area dopamine neurons in acute and prolonged opiates withdrawal. A computational perspective. Neuroscience 322:195-207 [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): Ventral tegmental area dopamine neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Addiction;
Implementer(s): Enrico, Paolo [enrico at uniss.it];
/
EnricoEtAl2016
README.html
cabal.mod *
cachan.mod
capump.mod *
dop.mod *
hh3.mod
IhDA.mod
kca.mod
nabalan.mod
netstimd.mod
newleak.mod
nmdanet.mod *
pump.mod *
CellDef.hoc
ctrlfiring_cnt.txt
DA_release.ses
DA_release_final.hoc
DA_release_withdrawal_final.hoc
DA_release_withdrawal_final_noGLU.hoc
firing_cnt.txt
fixnseg.hoc *
mosinit.hoc
screenshot.png
screenshot2.png
                            
/* 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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