Cl- homeostasis in immature hippocampal CA3 neurons (Kolbaev et al 2020)

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Accession:266811
Model used for the revision of the manuscript. Insertion of a passive Cl- flux and an active Cl-accumulation. Parameters adapted to match the properties of [Cl-]i determined in immature rat CA3 neurons in-vitro.
Reference:
1 . Kolbaev SN, Mohapatra N, Chen R, Lombardi A, Staiger JF, Luhmann HJ, Jedlicka P, Kilb W (2020) NKCC-1 mediated Cl- uptake in immature CA3 pyramidal neurons is sufficient to compensate phasic GABAergic inputs. Sci Rep 10:18399 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Synapse;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s):
Channel(s): NKCC1;
Gap Junctions:
Receptor(s): GabaA;
Gene(s):
Transmitter(s): Gaba;
Simulation Environment: NEURON;
Model Concept(s): Synaptic Plasticity; Homeostasis;
Implementer(s): Jedlicka, Peter [jedlicka at em.uni-frankfurt.de]; Kilb, Werner [wkilb at uni-mainz.de];
Search NeuronDB for information about:  GabaA; NKCC1; Gaba;
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Models_Kolbaev et al Scientific Reports Revision
Readme.txt
asin.mod *
cldif_CA3_NKCC1_HCO3.mod *
gabaA_Cl_HCO3.mod *
tonic.mod *
vecevent.mod *
Add_tonic_Cl-current.hoc
anyl.m
Block-Tonic-Cl-current.ses
Cell_1_SciRep_ShrinkCorr.hoc
Determine_cl-Flux_w-o_NKCC1_rig.ses
Determine_R_input_rig.ses
Determine_tau_NKCC1_rig.ses
Display_Phasic-Cl-current.ses
Display_Phasic-Cl-current_for_Charge_Transfer.ses
Display_Phasic-Cl-current_forFreq.ses
Phasic_GABA_activity_Div_Freq.hoc
Phasic_GABA_activity_Div_gGABA.hoc
Phasic_GABA_activity_only_soma_Backregul.hoc
Phasic_GABA_activity_only_soma_Div_Freq.hoc
Phasic_GABA_activity_only_soma_Div_gGABA.hoc
Phasic_GABA_activity_only_soma_for_Charge_Transfer.hoc
Phasic_GABA_activity_only_soma_PlaceSynapsesForFigure.hoc
start_Add_Tonic_Cl-currents.hoc
start_Block_Tonic_Cl-currents.hoc
start_Phasic_Cl-currents.hoc
start_Phasic_Cl-currents_for _Change-transfer.hoc
start_Phasic_GABA_activity_Div_Freq.hoc
start_Phasic_GABA_activity_only_soma_Backregul.hoc
start_Phasic_GABA_activity_only_soma_Div_Freq.hoc
start_Phasic_GABA_activity_only_soma_Div_gGABA.hoc
Switch_off_tonic_Cl-current.hoc
                            
//--------------------------------------------------------------------
// Simulation of Phasic Activity
// Synapses Distributet in soma, dasal and perisomatc dendrites
// synaptic activity starts after 100s
//---------------------------------------------------------------------

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

// Model specific parameters
duration = 1700000   // of the recording in ms
synact_start = 1400000
synact_dur = 100000
ndend=107         // Number of dendrites
Freq_Steps = 1
Start_Freq = 2

// Determining Parameters GABA ---------------------------
G_GABA = 0.000169   // synaptic weight according to miniature events 
DECAY_GABA = 37
P_GABA = 0.18
ngabasyn = 107
gninputs = Start_Freq


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

   lenghtoutputvec = 12000 // Number of Lines for output (< 32000 for Excel-Figures)



tstop = duration   // Duration
v_init = -70   // Initial voltage
dt = 0.025       // Step Interval in ms

// seed Values for random generator
seed_GABA = 1  // seed for random function


// ------------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


// Functions init_changedt and set_changedt -----------------//
//                                                           //
// Event Handler that decreases dt at a given timepoint      //
// required for fast equilibratin at low temp resolution     //
// but a high resolution for phaes with synaptic events      //
// ----------------------------------------------------------//

objref fih
fih = new FInitializeHandler("init_changedt()")

proc init_changedt() {
  printf("\n Init_changedt-ok, Start = %g s \n",synact_start/1000)
  dt = 5  //initial dt = 2 ms  - used for non synaptic input intervals
  cvode.event(synact_start, "set_changedt()")
  cvode.event(synact_start+synact_dur+10*DECAY_GABA, "set_changedtback()")
}

proc set_changedt() {
  printf("Call executed - changed dt \n")
  dt = 0.05
  if (cvode.active()) {
    cvode.re_init()
  } else {
    fcurrent()
  }
}

proc set_changedtback() {
  printf("Call executed - changed dt \n")
  dt = 5
  if (cvode.active()) {
    cvode.re_init()
  } else {
    fcurrent()
  }
}

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


// Objects for Synapses ---------------------------------------------------------
objref gabasyn[ngabasyn]                            // Definition of synapse objects

// random function for localization of synapses
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

// 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]


// Define vectors to link modelled parameter output ---------------------------------
objref timevec, curvec, clivec                                           // vectors linked to parameter-pointers
objref shorttimevec, shortcurvec, shortclivec                            // shorter Vectors for output

// Matrix for output 0 = time, 1 = Voltage, 2 = Cli
objref Outmatrix

// Define Name of Output-File
strdef OutFileName

// Define Output File
objref OutFile


// Generate vectors and matrices -------------------------------------
curvec = new Vector()
timevec = new Vector()
clivec = new Vector()
shortcurvec = new Vector()
shorttimevec = new Vector()
shortclivec = new Vector()

Outmatrix = new Matrix()



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

// Initialize Random Functions -----------
rand_gaba_loc = new Random(seed_GABA+2)
rand_gaba_dend = new Random(seed_GABA+4)
rand_gaba_t = new Random(seed_GABA+6)

//Define properties of random Function
rand_gaba_t.uniform(0, synact_dur)

// 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)
}

printf("\n Pos: ")
// Distribute GABA synapses -----------------------------------------------------------
// Distribute in soma and perisomatic dendrites
for k=0, 8 {
    pos = rand_gaba_dend.uniform (0.0001, 0.999)
    printf("%g = %g,",k, pos)
    dend[0]{
      gabasyn[k] = new gaba(0.2)
      gabasyn[k].tau1 = 0.1
      gabasyn[k].tau2 = DECAY_GABA
      gabasyn[k].P = P_GABA
      }
    }
for k=9, 17 {
    pos = rand_gaba_dend.uniform (0.0001, 0.999)
    printf("%g = %g,",k, pos)
    dend[3]{
      gabasyn[k] = new gaba(pos)
      gabasyn[k].tau1 = 0.1
      gabasyn[k].tau2 = DECAY_GABA
      gabasyn[k].P = P_GABA
      }
    }
for k=18, 26 {
    pos = rand_gaba_dend.uniform (0.0001, 0.999)
    printf("%g = %g,",k, pos)
    dend[8]{
      gabasyn[k] = new gaba(pos)
      gabasyn[k].tau1 = 0.1
      gabasyn[k].tau2 = DECAY_GABA
      gabasyn[k].P = P_GABA
      }
    }
for k=27, 35 {
    pos = rand_gaba_dend.uniform (0.0001, 0.999)
    printf("%g = %g,",k, pos)
    dend[8]{
      gabasyn[k] = new gaba(pos)
      gabasyn[k].tau1 = 0.1
      gabasyn[k].tau2 = DECAY_GABA
      gabasyn[k].P = P_GABA
      }
    }                                         
for k=36, 44 {
    pos = rand_gaba_dend.uniform (0.0001, 0.999)
    printf("%g = %g,",k, pos)
    dend[10]{
      gabasyn[k] = new gaba(pos)
      gabasyn[k].tau1 = 0.1
      gabasyn[k].tau2 = DECAY_GABA
      gabasyn[k].P = P_GABA
      }
    }
for k=45, 53 {
    pos = rand_gaba_dend.uniform (0.0001, 0.999)
    printf("%g = %g,",k, pos)
    apic[10]{
      gabasyn[k] = new gaba(pos)
      gabasyn[k].tau1 = 0.1
      gabasyn[k].tau2 = DECAY_GABA
      gabasyn[k].P = P_GABA
      }
    }
for k=54, 62 {
    pos = rand_gaba_dend.uniform (0.0001, 0.999)
    printf("%g = %g,",k, pos)
    apic[11]{
      gabasyn[k] = new gaba(pos)
      gabasyn[k].tau1 = 0.1
      gabasyn[k].tau2 = DECAY_GABA
      gabasyn[k].P = P_GABA
      }
    }
for k=63, 71 {
    pos = rand_gaba_dend.uniform (0.0001, 0.999)
    printf("%g = %g,",k, pos)
    apic[12]{
      gabasyn[k] = new gaba(pos)
      gabasyn[k].tau1 = 0.1
      gabasyn[k].tau2 = DECAY_GABA
      gabasyn[k].P = P_GABA
      }
    }
for k=72, 80 {
    pos = rand_gaba_dend.uniform (0.0001, 0.999)
    printf("%g = %g,",k, pos)
    apic[13]{
      gabasyn[k] = new gaba(pos)
      gabasyn[k].tau1 = 0.1
      gabasyn[k].tau2 = DECAY_GABA
      gabasyn[k].P = P_GABA
      }
    }
for k=81, 89 {
    pos = rand_gaba_dend.uniform (0.0001, 0.999)
    printf("%g = %g,",k, pos)
    apic[14]{
      gabasyn[k] = new gaba(pos)
      gabasyn[k].tau1 = 0.1
      gabasyn[k].tau2 = DECAY_GABA
      gabasyn[k].P = P_GABA
      }
    }
for k=90, ndend-1 {
    pos = rand_gaba_dend.uniform (0.0001, 0.999)
    printf("%g = %g,",k, pos)
    soma{
      gabasyn[k] = new gaba(pos)
      gabasyn[k].tau1 = 0.1
      gabasyn[k].tau2 = DECAY_GABA
      gabasyn[k].P = P_GABA
      }
    }

printf("\n \n")

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

//-- Outer Loop Variation of gGABA -------------------

Freq_Step = Start_Freq

while (Freq_Step < (Start_Freq + Freq_Steps)){
    gninputs = Freq_Step
    Frequency = (gninputs*ngabasyn)/(synact_dur/1000)
    printf("Simlation only Soma Backregulation - g_GABA = %g  - F(PSC) = %.4g \n", Freq_Step, Freq_Steps, G_GABA, Frequency)

    // 2a. Generate timestamps/conductances for GABA synapses --------------------------------------
    for f=0, ngabasyn-1 {
      for i=0, gninputs-1 {
          t = rand_gaba_t.repick()
          gaba_t_vec[f].x[i]=t + synact_start
      }
    }

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


  // 3. generate Vecstim-vectors 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
        curvec.record(&SEClamp[0].i) // Injection current
        clivec.record(&soma.cli(0.5))
 

  // 6. Run Simulation --------------------------------------------------------
     init_changedt()   //Initializes time dependent switch in dt 
     run()


  // 7. Put Data in Output Vector ------------------------------------------------------  
   
 
     MakeShort(timevec, shorttimevec, lenghtoutputvec)
     Outmatrix.resize(shorttimevec.size()+1, 3)
     Outmatrix.setcol(0, shorttimevec)
        
     MakeShort(curvec, shortcurvec, lenghtoutputvec)
     Outmatrix.setcol(1 , shortcurvec)


     MakeShort(clivec, shortclivec, lenghtoutputvec)
     Outmatrix.setcol(2, shortclivec)

     // Save the Data --------------------------------------------------------------------
     OutFile = new File()
     sprint(OutFileName, "Result_Phasic-GABA-OnlySoma-Backregulation-GABA=%g-F=%.3g.asc",G_GABA*1000, Frequency)
     OutFile.wopen(OutFileName) 
     Outmatrix.fprint(OutFile, "\t%g")     
     OutFile.close

     printf("-finished \n")

  // Goto next Frequency
     Freq_Step+=1
} // End of outer loop
printf("Simulation complete \n")


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