//--------------------------------------------------------------------
// Simulation of a single GDP
// Simulation of AMPA and GABA events with defined correlation
//---------------------------------------------------------------------
// ------------Definition of Parameters -------------------------------
// --------------------------------------------------------------------
// Define Seed Values
seed_AMPA = 8
seed_GABA = seed_AMPA +2
// Define Cl--Concentration
Cl_Steps = 6 // Number of Different [Cl-]i
objref Cl_List
Cl_List = new Vector(Cl_Steps)
Cl_List.x[0] = 5 //mM
Cl_List.x[1] = 10
Cl_List.x[2] = 15
Cl_List.x[3] = 25
Cl_List.x[4] = 35
Cl_List.x[5] = 50
// Determination Parameters AMPA ------------------------------------
G_AMPA = 0.000305 * 1
DECAY_AMPA = 11
nampasyn = 100 //Number of AMPA-Synapses // calculated was 107
aninputs = 1 // number of inputs per synapse
// Determining Parameters GABA allone ---------------------------
G_GABA = 0.000789 // synaptic weight according to miniature events
DECAY_GABA = 37
P_GABA = 0.18
ngabasyn = 100 // from experiments = 534
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
tstop = 1500 // Duration
v_init = -60 // Initial voltage
dt = 0.01 // Step Interval in ms
// definition of boolean variables
TRUE = 1
FALSE = 0
AmpaCtrl_Steps = 2 // 0 = without AMPA, 1 = With AMPA
// ------------Procedures and Functions -------------------------------
// --------------------------------------------------------------------
// Function MakeShort ---------------------------------------//
// Inputs: $1 Objref to Inputvector //
// $2 Objref to Outoutvector //
// $3 lenoutvec desired lendth of Outputvector //
// //
// Reduce Inputvec to Outputvev by averaging n elements //
// n (reducing factor) = floor(Inputvec.size() / lenoutvec) //
// ----------------------------------------------------------//
obfunc MakeShort() {local i, n
n = int($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 Synapses ---------------------------------------------------------
objref gabasyn[ngabasyn] // Definition of synapse objects
objref ampasyn[nampasyn]
// random function for localization of synapses
objref rand_ampa_loc, rand_gaba_loc
// random function for localization of synapses in which dendrite
objref rand_ampa_dend, rand_gaba_dend
// random function for synapses parameters
objref rand_gaba_t, rand_gaba_g
objref rand_ampa_t, rand_ampa_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]
// definition of Vectors for AMPA-Stimulation
objref ampastim[nampasyn], ampa_t_vec[nampasyn], ampa_t_vecr[nampasyn], synpulseampa[nampasyn], ampa_g_vec[nampasyn]
// Define vectors to link modelled parameter output ---------------------------------
objref timevec, voltvec, clivec[ndend] // vectors linked to parameter-pointers
objref clivec_aver // vectors for Averagees over all Dendrites
objref shorttimevec, shortvoltvec, shortclivec, spacevec // shorter Vectors for output
// Matrix for output 0 = time, 1 = Voltage, 2 = average Cli , 3 to 3+ndend, Cli n Dend[i]
objref Outmatrix, TimeStampMatrix
objref ampa_timestamp, gaba_timestamp
// Define Name of Output-File
strdef OutFileName, OutTimestampFileName
// Define Output File
objref OutFile, OutTimestampFile
// Generate vectors and matrices -------------------------------------
voltvec = new Vector()
timevec = new Vector()
clivec_aver = new Vector()
shortvoltvec = new Vector()
shorttimevec = new Vector()
shortclivec = new Vector()
spacevec = new Vector()
for i=0, ndend-1 {
clivec[i] = new Vector()
}
Outmatrix = new Matrix(lenghtoutputvec+1, Cl_Steps*AmpaCtrl_Steps*3+1)
TimeStampMatrix = new Matrix(ngabasyn+1, Cl_Steps*3+1)
// Start of Input generation -------------------------------------------
// Initialize Random Functions -----------
rand_ampa_loc = new Random(seed_AMPA)
rand_gaba_loc = new Random(seed_GABA)
rand_ampa_dend = new Random(seed_AMPA)
rand_gaba_dend = new Random(seed_GABA)
rand_ampa_t = new Random(seed_AMPA)
rand_ampa_g = new Random(seed_AMPA)
rand_gaba_t = new Random(seed_GABA)
rand_gaba_g = new Random(seed_GABA)
//Define properties of random Function
rand_gaba_t.normal(600, 20000) //9000 was original from experimental results
rand_gaba_g.normal(1, 0.28) //rel variance of GABA according to experimental results
rand_ampa_t.normal(600, 20000) //8500 was original from experimental results
rand_ampa_g.normal(1, 0.26) //rel variance of AMPA according to experimental 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)
}
gaba_timestamp = new Vector(ngabasyn)
for i = 0, nampasyn-1 {
ampa_t_vec[i] = new Vector(aninputs)
ampa_t_vecr[i] = new Vector(aninputs)
ampa_g_vec[i] = new Vector(aninputs)
}
ampa_timestamp = new Vector(nampasyn)
// Distribute GABA synapses -----------------------------------------------------------
for k=0, ngabasyn - 1 {
pos = rand_gaba_loc.uniform(0,ndend-1)
pos2 = rand_gaba_dend.uniform (0.001, 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
}
}
// distribute AMPA synapses ------------------------------------------------------------------------
for k=0, nampasyn-1 {
pos = rand_ampa_loc.uniform(0,ndend-1)
pos3 = rand_ampa_dend.uniform (0.001, 0.999)
dend_0[pos]{
ampasyn[k] = new Exp2Syn(pos3)
ampasyn[k].tau1 = 0.1
ampasyn[k].tau2 = DECAY_AMPA
}
}
//-- Simulation starts here -----------------------------------------------------------------
//-------------------------------------------------------------------------------------------
//-- Outer Loop Variation of Cl- --------------------------------------------------
Cl_Step = 0
while (Cl_Step < Cl_Steps){
// 1. define Cl- concentration in all elements of the cell ------------------------------------
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]
}
// 2a. Generate timestamps/conductances for AMPA and GABA synapses (at same timepoint) --------------------------------------
for f=0, nampasyn-1 {
for i=0, aninputs-1 {
t = abs(rand_ampa_t.repick())
ampa_timestamp.x[f] = t
gaba_timestamp.x[f] = t
ampa_t_vec[f].x[i]= t
gaba_t_vec[f].x[i]=t
}
}
ampa_timestamp.sort()
for f=0, nampasyn-1 {
ampa_t_vecr[f] = ampa_t_vec[f].sort()
}
gaba_timestamp.sort()
for f=0, ngabasyn - 1 {
gaba_t_vecr[f] = gaba_t_vec[f].sort()
}
// 2d. Generate conductances for GABA synapses --------------------------------------
for f=0, ngabasyn-1 {
for i=0, gninputs-1 {
g = rand_gaba_g.repick()
gaba_t_vec[f].x[i]= gaba_timestamp.x[f]
gaba_g_vec[f].x[i]= abs(G_GABA*g)
}
}
// 2d. Generate conductances for AMPA synapses --------------------------------------
for f=0, nampasyn-1 {
for i=0, aninputs-1 {
g = rand_ampa_g.repick()
ampa_g_vec[f].x[i]=abs(G_AMPA*g)
}
}
// Save the vectors to analyze the temporal correlation
ActCol = (Cl_Step)*3
TimeStampMatrix.x[0][ActCol] = Cl_List.x[Cl_Step]
TimeStampMatrix.x[1][ActCol] = seed_AMPA
TimeStampMatrix.setcol(ActCol+1, gaba_timestamp)
TimeStampMatrix.setcol(ActCol+2, ampa_timestamp)
// 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
}
for i=0, nampasyn-1{
ampastim[i] = new VecStim()
ampastim[i].play(ampa_t_vecr[i]) // AMPA stimulator
}
// Run the simulation for each stimulation pattern once with and once without AMPA co-stimulation
for AmpaCtrl_Step = 0, AmpaCtrl_Steps-1 {
// 4. Play the Vecstim objects to the synapses ---------------------------------------------
for i=0, ngabasyn-1 {
synpulsegaba[i] = new NetCon(gabastim[i], gabasyn[i], 0, 0, gaba_g_vec[i].x[0])
} // GABA NetCon
for i=0, nampasyn-1 {
synpulseampa[i] = new NetCon(ampastim[i], ampasyn[i], 0, 0, ampa_g_vec[i].x[0]*AmpaCtrl_Step)
}
// Ampa NetCon
printf("%g of %g;",(Cl_Step*AmpaCtrl_Steps+AmpaCtrl_Step+1), (Cl_Steps*AmpaCtrl_Steps))
printf("seed- %g;[Cl-]i=%gmM;AMPA=%g:",seed_AMPA, Cl_List.x[Cl_Step], AmpaCtrl_Step)
// 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))
}
// 6. Run Simulation --------------------------------------------------------
run()
// 7. Put Data in Output Vector ------------------------------------------------------
MakeShort(timevec, shorttimevec, lenghtoutputvec)
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] = seed_AMPA
spacevec.x[2] = G_AMPA*1000*AmpaCtrl_Step
spacevec.x[3] = 777
ActCol = (Cl_Step*AmpaCtrl_Steps+AmpaCtrl_Step)*3
Outmatrix.setcol(ActCol+1, spacevec)
MakeShort(voltvec, shortvoltvec, lenghtoutputvec)
Outmatrix.setcol(ActCol+2, shortvoltvec)
// Calculate average [Cli] in dendrites -------
clivec_aver.mul(0) // empty vector
for i=0, ndend-1 {
clivec_aver.resize(clivec[i].size())
clivec_aver.add(clivec[i])
}
clivec_aver.div(ndend)
// - end of averaging
MakeShort(clivec_aver, shortclivec, lenghtoutputvec)
Outmatrix.setcol(ActCol+3, shortclivec)
printf(", max [Cl-]i %g, min [Cl-]i %g \n", clivec_aver.max, clivec_aver.min)
} // End of forr loop with/without AMPA
Cl_Step+=1 // Goto next Cl- Concentration
} // End of outer loop ---------------------------------
// Save the Data --------------------------------------------------------------------
OutTimestampFile = new File()
sprint(OutTimestampFileName, "TimeStamps_gGABA-0.789_nGABA-100_gAMPA-0.305_Div-Cl_100percCorrel_seed-%g.asc",seed_AMPA)
OutTimestampFile.wopen(OutTimestampFileName)
TimeStampMatrix.fprint(OutTimestampFile, "\t%.12g")
OutTimestampFile.close
OutFile = new File()
sprint(OutFileName, "Result_Single_GDP_gGABA-0.789_nGABA-100_gAMPA-0.305_Div-Cl_100percCorrel_seed-%g.asc",seed_AMPA)
OutFile.wopen(OutFileName)
Outmatrix.fprint(OutFile, "\t%.12g")
OutFile.close
// - end of save data ---------------------------------------------------------------