//--------------------------------------------------------------------
// Simulation of Phasic Activity
// Synapses Distributet in soma, dasal and perisomatc dendrites
// synaptic activity starts after 100s
//---------------------------------------------------------------------
// ------------Definition of Parameters -------------------------------
// --------------------------------------------------------------------
// Model specific parameters
duration = 101000 // of the recording in ms
synact_start = 1000
synact_dur = 100000
ndend=107 // Number of dendrites
// Determining Parameters GABA ---------------------------
G_GABA = 0.000169 // synaptic weight according to miniature events
DECAY_GABA = 37
P_GABA = 0.18
ngabasyn = 107
gninputs = 2
// Definition of various runtime parameters --------------------------
lenghtoutputvec = 25000 // Number of Lines for output (< 32000 for Excel-Figures)
tstop = duration // Duration
v_init = -70 // Initial voltage
dt = 0.05 // 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
// ---------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 -----------------------------------------------------------------
//-------------------------------------------------------------------------------------------
Frequency = (gninputs*ngabasyn)/(synact_dur/1000)
printf("Simlation only Soma g_GABA = %g - F(PSC) = %.4g \n", 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 --------------------------------------------------------
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-forChargeTransfer-GABA=%g-F=%.3g.asc",G_GABA*1000, Frequency)
OutFile.wopen(OutFileName)
Outmatrix.fprint(OutFile, "\t%g")
OutFile.close
printf("-finished \n")
printf("Simulation complete \n")