Factors contribution to GDP-induced [Cl-]i transients (Lombardi et al 2019)

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Accession:253369
This models are used to evaluate which factors influence the GDP (giant depolarizing potential) induced [Cl-]I transients based on a initial model of P. Jedlicka
Reference:
1 . Lombardi A, Jedlicka P, Luhmann HJ, Kilb W (2019) Interactions Between Membrane Resistance, GABA-A Receptor Properties, Bicarbonate Dynamics and Cl-Transport Shape Activity-Dependent Changes of Intracellular Cl- Concentration Int J of Mol Sci [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Dendrite; Synapse;
Brain Region(s)/Organism: Mouse; Hippocampus;
Cell Type(s): Hippocampus CA3 pyramidal GLU cell;
Channel(s):
Gap Junctions:
Receptor(s): GabaA;
Gene(s):
Transmitter(s): Gaba;
Simulation Environment: NEURON;
Model Concept(s): Synaptic Plasticity;
Implementer(s):
Search NeuronDB for information about:  Hippocampus CA3 pyramidal GLU cell; GabaA; Gaba;
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LombardiEtAl2019
Real_Cell_Dynamic_Cl_HCO3_1GDP
cldif_CA3_NKCC1_HCO3.mod *
gabaA_Cl_HCO3.mod *
VDpas.mod *
vecevent.mod *
Cell1_Cl_HCO3_Pas.hoc
Cell1_Cl_HCO3_VDPas.hoc
GDP_Cl_HCO3_All_short.ses *
single_GDP_AMPA_GABA_HCO3_Frontiers.hoc
single_GDP_GABA_HCO3_ForTest.hoc
start_single_GDP_HCO3_Frontiers.hoc
                            
//--------------------------------------------------------------------
// Simulation of  a single GDP
//------------------------------------------------------------------
seed_GABA = 22

// ------------------------------------- Parameters -------------------------------------

// Number of dendrites
ndend=56

// Determination Parameters GABA 
G_GABA = 0.000789   // synaptic weight according to miniature events 
DECAY_GABA = 37
P_GABA = 0.18
ngabasyn = 534 //Number of GABA-Synapses // calculated was 101
gninputs = 1 // number of inputs per synapse


Resting_Chloride = 10


// definition of objects


// Definition of synapse objects
objref gabasyn[ngabasyn]

// random function for localization of synapses in a dendrite
objref rand_gaba_loc 

// random function for localization of synapses in which dendrite
objref rand_gaba_dend

// random function for synapses parameters
objref rand_gaba_t, rand_gaba_g 

// definition of Vectors for Gaba-Stimulation (t_vec = timestamps t_vecr = sorted timestamps, g_vec = rel conductance)
objref gabastim[ngabasyn], gaba_t_vec[ngabasyn], gaba_t_vecr[ngabasyn], synpulsegaba[ngabasyn], gaba_g_vec[ngabasyn]



// Initialize Random Functions -----------
rand_gaba_loc = new Random(seed_GABA)
rand_gaba_dend = new Random(seed_GABA)
rand_gaba_t = new Random(seed_GABA)
rand_gaba_g = new Random(seed_GABA)

//Define properties of random Function ---------
rand_gaba_t.normal(600, 9000)
rand_gaba_g.normal(1, 0.28) //rel variance of GABA according to results


// start of Input generation -------------------------------------------


// generate Vectors --- (gniputs, aninputs defines number of inputs per synapse) ------
for i = 0, ngabasyn-1 {
gaba_t_vec[i] = new Vector(gninputs)
gaba_t_vecr[i] = new Vector(gninputs)
gaba_g_vec[i] = new Vector(gninputs)
}


// start of main loop -----------------------------------------------------------


// 1. define Cl- concentration ----------------------------------------------
      forsec all {
      cli0_cldif_CA3_NKCC1_HCO3 = Resting_Chloride
      cli_Start_cldif_CA3_NKCC1_HCO3 = Resting_Chloride
      cli_cldif_CA3_NKCC1_HCO3 = Resting_Chloride
      }

// 2a. distribute GABA synapses -----------------------------------------------------------
    for k=0, ngabasyn-1 {
      pos = rand_gaba_loc.uniform(0,ndend-1)
    pos2 = rand_gaba_dend.uniform (0.0001, 0.999)
    dend_0[pos]{
      gabasyn[k] = new gaba(pos2)
      gabasyn[k].tau1 = 0.1
      gabasyn[k].tau2 = DECAY_GABA
      gabasyn[k].P = P_GABA
    }
  }                                              


// 3a. Generate timestamps/conductances for GABA synapses --------------------------------------
  for f=0, ngabasyn-1 {
    for i=0, gninputs-1 {
        t = rand_gaba_t.repick()
        g = rand_gaba_g.repick()
        gaba_t_vec[f].x[i]=t
        gaba_g_vec[f].x[i]=g * G_GABA
      }
    }

  for f=0, ngabasyn-1 {                          
    gaba_t_vecr[f] = gaba_t_vec[f].sort()
    }                                               

                            

// 4. generate Vecstim-vectrors from the sorted timestamp-vectors -------------------------------
    for i=0, ngabasyn-1 {
      gabastim[i] = new VecStim()
      gabastim[i].play(gaba_t_vecr[i])
   }                                                 // GABA stimulator


// 5. Play the Vecstim objects to the synapses ---------------------------------------------
    for i=0, ngabasyn-1 {
      synpulsegaba[i] = new NetCon(gabastim[i], gabasyn[i], 0, 0, G_GABA)
 }                                                  // GABA NetCon




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