Electrotonic transform and EPSCs for WT and Q175+/- spiny projection neurons (Goodliffe et al 2018)

 Download zip file   Auto-launch 
Help downloading and running models
Accession:236310
This model achieves electrotonic transform and computes mean inward and outward attenuation from 0 to 500 Hz input; and randomly activates synapses along dendrites to simulate AMPAR mediated EPSCs. For electrotonic analysis, in Elec folder, the entry file is MSNelec_transform.hoc. For EPSC simulation, in Syn folder, the entry file is randomepsc.hoc. Run read_EPSCsims_mdb_alone.m next with the simulated parameter values specified to compute the mean EPSC.
Reference:
1 . Goodliffe JW, Song H, Rubakovic A, Chang W, Medalla M, Weaver CM, Luebke JI (2018) Differential changes to D1 and D2 medium spiny neurons in the 12-month-old Q175+/- mouse model of Huntington's Disease. PLoS One 13:e0200626 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse;
Brain Region(s)/Organism: Striatum;
Cell Type(s): Neostriatum spiny neuron;
Channel(s):
Gap Junctions:
Receptor(s): AMPA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Detailed Neuronal Models; Membrane Properties; Electrotonus; Synaptic-input statistic;
Implementer(s):
Search NeuronDB for information about:  AMPA;
/
GoodliffeEtAl2018
Elec
tau_tables
kir.mod *
actionPotentialPlayer.hoc *
all_tau_vecs.hoc
analyticFunctions.hoc *
aux_procs.hoc
baseline_values.txt
basic_procs.hoc
colorDendrites.hoc
electro_procs.hoc *
fixnseg.hoc *
load_scripts.hoc *
measureMeanAtten.hoc
MSN_fixDiams.hoc
MSNelect.hoc
MSNelect_transform.hoc
Nov3IR3a.hoc
Nov9IR2a_spine.hoc
readcell.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() { local freq, d_lambda
  freq = $1
  d_lambda = $2
  forall {
    nseg = int((L / (d_lambda * lambda_f(freq)) + 0.9) / 2) * 2 + 1
  }
}

Loading data, please wait...