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]
Citations  Citation Browser
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_Cl_HCO3_1GDP_Var-Cl-VDpas__Fig2g
cldif_CA3_NKCC1_HCO3.mod *
gabaA_Cl_HCO3.mod *
VDpas.mod *
vecevent.mod *
Cell1_Cl_HCO3_VDPas.hoc
GDP_Cl_HCO3_All_short.ses *
Single_GDP_101-GABA_018-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_101-GABA_044-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_101-GABA_0-PHCO3_VDpas_10-Cl.hoc
Single_GDP_101-GABA_0-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_101-GABA_0-PHCO3_VDpas_Div_Cl.hoc
Single_GDP_151-GABA_018-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_151-GABA_044-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_151-GABA_0-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_201-GABA_018-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_201-GABA_044-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_201-GABA_0-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_201-GABA_0-PHCO3_VDpas_Div_Cl.hoc
Single_GDP_251-GABA_018-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_251-GABA_044-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_251-GABA_0-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_301-GABA_018-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_301-GABA_044-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_301-GABA_0-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_301-GABA_0-PHCO3_VDpas_Div_Cl.hoc
Single_GDP_351-GABA_018-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_351-GABA_044-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_351-GABA_0-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_401-GABA_018-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_401-GABA_044-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_401-GABA_0-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_451-GABA_018-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_451-GABA_044-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_451-GABA_0-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_501-GABA_018-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_501-GABA_044-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_501-GABA_0-PHCO3_VDpas_10-Cl_GUI.hoc
Single_GDP_GABA_HCO3_VDpas_Div_pGABA_Div_Cl.hoc *
start_Single_GDP_101-GABA_0-PHCO3_VDpas_Div_Cl.hoc
start_Single_GDP_201-GABA_0-PHCO3_VDpas_Div_Cl.hoc
start_Single_GDP_301-GABA_0-PHCO3_VDpas_Div_Cl.hoc
start_Single_GDP_GABA_VDpas_Div_pGABA_Div_Cl.hoc
                            
//--------------------------------------------------------------------
// Simulation of  a single GDP
//---------------------------------------------------------------------

// ------------Definition of Parameters -------------------------------
// --------------------------------------------------------------------


// Determining Cl- Properties --------------------------------------
Cl_Steps = 11  // Number of Different [Cl-]i  
Min_Cl = 10
Max_Cl = 50

    // Write Vektor with Cl- Values
    objref Cl_List
    Cl_List = new Vector(Cl_Steps)
    for i=0, Cl_Steps-1 {
       Cl_List.x[i] = Min_Cl + i*((Max_Cl-Min_Cl)/(Cl_Steps-1))
    }



// Determination Parameters GABA ------------------------------------
G_GABA = 0.000789   // synaptic weight according to miniature events
DECAY_GABA = 37
P_GABA = 0
ngabasyn = 101 //Number of GABA-Synapses // calculated was 101
gninputs = 1 // number of inputs per synapse


// Definition of various runtime parameters --------------------------

lenghtoutputvec = 6000  // Number of Lines for output (< 32000 for Excel-Figures)

ndend=56  // Number of dendrites

seed = 8  // seed for random function
   
tstop = 1500   // Duration
v_init = -60   // Initial voltage
dt = 0.01      // Step Interval in ms



// ------------Procedures and Functions -------------------------------
// --------------------------------------------------------------------

// Function MakeShort ---------------------------------------//
// Inputs: $1 Objref to Inputvector                          //
//         $2 Objref to Outoutvector                         //
//         lenoutvec  desired lendth of Outputvector         //
//                                                           //
// Reduce Inputvec to Outputvev by averaging n elements      //
// n (reducing factor) = floor(Inputvec.size() / lenoutvec)  //
// ----------------------------------------------------------//

obfunc MakeShort() {local i, n

  n = int($o1.size()/$3)
  $o2.resize($3)
  for i=0, $3-1 {
    $o2.x[i] = $o1.mean(i*n, (i+1)*n-1)
  }
  return $o2
}   //  End of function



// ---------Definition of objects -------------------------------------
// --------------------------------------------------------------------


// Objects for GABA Synapses ---------------------------------------------------------
objref GABA_SYN_LOCATION
GABA_SYN_LOCATION = new Random(seed)                // Position of GABA synapse as random Object

objref gabasyn[ngabasyn]                            // Definition of synapse objects

// random function for localization of synapses
objref rand_loc

// 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]

// Define vectors to link modelled parameter output ---------------------------------
objref timevec, voltvec, clivec[ndend], hco3ivec[ndend]                   // vectors linked to parameter-pointers
objref clivec_aver, hco3ivec_aver                                         // vectors for Averagees over all Dendrites
objref shorttimevec, shortvoltvec, shortclivec, shorthco3ivec, spacevec   // shorter Vectros for output

// Matrix for output 0 = time, 1 = Voltage, 2 = average Cli , 3 to 3+ndend, Cli n Dend[i]
objref Outmatrix

// Define Name of Output-File
strdef OutFileName

// Define Output File
objref OutFile


// Generate vectors and matrices -------------------------------------
voltvec = new Vector()
timevec = new Vector()
clivec_aver = new Vector()
hco3ivec_aver = new Vector()
shortvoltvec = new Vector()
shorttimevec = new Vector()
shortclivec = new Vector()
shorthco3ivec = new Vector()
spacevec = new Vector()
for i=0, ndend-1 {
  clivec[i] = new Vector()
  hco3ivec[i] = new Vector()
}
Outmatrix = new Matrix()



// Start of Input generation -------------------------------------------

// initiation of Random functions
rand_loc = new Random(seed)
rand_gaba_t = new Random(seed)
rand_gaba_g = new Random(seed)

//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

// 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)
}

  // Distribute GABA synapses -----------------------------------------------------------

    for k=0, ngabasyn-1 {
      pos = rand_loc.uniform(0,ndend-1)
      pos2 = GABA_SYN_LOCATION.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
        }
      }


//-- Simulation starts here -----------------------------------------------------------------
//-------------------------------------------------------------------------------------------

 
  // Loop Variation of Cl- --------------------------------------------------

  Cl_Step = 0

  while (Cl_Step < Cl_Steps){

    // 1. define Cl- concentration ----------------------------------------------
  
    forsec all {
      cli0_cldif_CA3_NKCC1_HCO3 = Cl_List.x[Cl_Step]
      cli_Start_cldif_CA3_NKCC1_HCO3 = Cl_List.x[Cl_Step]
      cli_cldif_CA3_NKCC1_HCO3 = Cl_List.x[Cl_Step]
    }
    printf("Sequence %g of %g; [Cl-]i = %g, dt = %g, tstop = %g, ", (Cl_Step+1), (Cl_Steps), Cl_List.x[Cl_Step], dt, tstop)

    // 2. 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()
    }


  // 3. 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


  // 4. 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

 
  // 5. Link Objects to Output-Vectors -----------------------------------
        timevec.record(&t)   // Time vector
        voltvec.record(&v(.5)) // Volt vector in soma
        for i=0, ndend-1 {
            clivec[i].record(&dend_0[i].cli(0.5))
            hco3ivec[i].record(&dend_0[i].hco3i(0.5))
        }

  // 6. Run Simulation --------------------------------------------------------

     run()
 
  // 8. Put Data in Output Vector ------------------------------------------------------  
   
 
     MakeShort(timevec, shorttimevec, lenghtoutputvec)
     Outmatrix.resize(shorttimevec.size()+1, Cl_Steps*4+1)
     Outmatrix.setcol(0, shorttimevec)
        
     spacevec.resize(shorttimevec.size()+1) //spavevec caries information about the properties during a loop)
     spacevec.fill(0) 
     spacevec.x[0] = Cl_List.x[Cl_Step]
     spacevec.x[1] = P_GABA
     spacevec.x[2] = 777
     Outmatrix.setcol(Cl_Step*4+1, spacevec)

     MakeShort(voltvec, shortvoltvec, lenghtoutputvec)
     Outmatrix.setcol(Cl_Step*4+2, shortvoltvec)

    // Calculate average [Cli] in dendrites -------
     clivec_aver.mul(0) // empty vector
     hco3ivec_aver.mul(0)
     for i=0, ndend-1 {
        clivec_aver.resize(clivec[i].size())
        clivec_aver.add(clivec[i])
        hco3ivec_aver.resize(hco3ivec[i].size())
        hco3ivec_aver.add(hco3ivec[i])
     } 
     clivec_aver.div(ndend)
     printf(", max [Cl-]i %g, min [Cl-]i %g \n", clivec_aver.max, clivec_aver.min)
     hco3ivec_aver.div(ndend)
     MakeShort(clivec_aver, shortclivec, lenghtoutputvec)
     MakeShort(hco3ivec_aver, shorthco3ivec, lenghtoutputvec)
     Outmatrix.setcol(Cl_Step*4+3, shortclivec)
     Outmatrix.setcol(Cl_Step*4+4, shorthco3ivec)

     // Goto next Cl- Concentration
     Cl_Step+=1
  }  
  // End inner loop ---------------------------------



  // Save the Data --------------------------------------------------------------------
   OutFile = new File()
   sprint(OutFileName, "Result_VDpas_pGABA-0_nGABA-101_Var_Cl_Short.asc")
   OutFile.wopen(OutFileName) 
   Outmatrix.fprint(OutFile, "\t%g")     
   OutFile.close