//--------------------------------------------------------------------
// 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))
}
// Determining GABA receptor Conductance ---------------------------
tauGABA_Steps = 3 // 3 different decay constants
objref tauGABA_List
tauGABA_List = new Vector(tauGABA_Steps)
// manually put desired tau_Values in List
tauGABA_List.x[0] = 3.7 // synaptic weight according to miniature events
tauGABA_List.x[1] = 37
tauGABA_List.x[2] = 370
// Determination Parameters GABA ------------------------------------
G_GABA = 0.000789
P_GABA = 0
ngabasyn = 302
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 = 0.001 // 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].P = P_GABA
}
}
//-- Simulation starts here -----------------------------------------------------------------
//-------------------------------------------------------------------------------------------
//-- Outer Loop Variation of P_GABA -------------------
tauGABA_Step = 0
while (tauGABA_Step < tauGABA_Steps){
// Set Decay Time of all GABA synapses -----------------------------------------------------------
tau_GABA = tauGABA_List.x[tauGABA_Step]
for k=0, ngabasyn-1 {
gabasyn[k].tau2 = tau_GABA
}
// Inner 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, tau = %g, ", (tauGABA_Step*Cl_Steps+Cl_Step+1), (Cl_Steps*tauGABA_Steps), Cl_List.x[Cl_Step], tauGABA_List.x[tauGABA_Step])
// 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_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, tauGABA_Steps*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] = tau_GABA
spacevec.x[2] = 777
Outmatrix.setcol(tauGABA_Step*Cl_Steps*4+Cl_Step*4+1, spacevec)
MakeShort(voltvec, shortvoltvec, lenghtoutputvec)
Outmatrix.setcol(tauGABA_Step*Cl_Steps*4+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(tauGABA_Step*Cl_Steps*4+Cl_Step*4+3, shortclivec)
Outmatrix.setcol(tauGABA_Step*Cl_Steps*4+Cl_Step*4+4, shorthco3ivec)
// Goto next Cl- Concentration
Cl_Step+=1
}
// End inner loop ---------------------------------
// Goto next P_GABA
tauGABA_Step+=1
} // End of outer loop
// Save the Data --------------------------------------------------------------------
OutFile = new File()
sprint(OutFileName, "Result_Cell_GDP_nGABA-302_VDpas_Div-tauGABA_Div-Cl.asc")
OutFile.wopen(OutFileName)
Outmatrix.fprint(OutFile, "\t%g")
OutFile.close
