Mirror Neuron (Antunes et al 2017)

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Accession:229276
Modeling Mirror Neurons Through Spike-Timing Dependent Plasticity. This script reproduces Figure 3B.
Reference:
1 . Antunes G, Faria da Silva SF, Simoes de Souza FM (2018) Mirror Neurons Modeled Through Spike-Timing-Dependent Plasticity are Affected by Channelopathies Associated with Autism Spectrum Disorder. Int J Neural Syst 28:1750058 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Neocortex;
Cell Type(s):
Channel(s): I Calcium; I M; I h; I Potassium; I Sodium;
Gap Junctions:
Receptor(s): AMPA; NMDA;
Gene(s): Cav1.2 CACNA1C; Cav1.3 CACNA1D;
Transmitter(s): Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Detailed Neuronal Models; STDP;
Implementer(s): Simoes-de-Souza, Fabio [fabio.souza at ufabc.edu.br];
Search NeuronDB for information about:  AMPA; NMDA; I M; I h; I Sodium; I Calcium; I Potassium; Glutamate;
/
Final
hoc_recordings
mechanisms
morphology
python_recordings
synapses
README
.provenance.json
biophysics.hoc
cellinfo.json
CHANGELOG
constants.hoc *
creategui.hoc
createsimulation.hoc
current_amps.dat
init_super.hoc
LICENSE *
morphology.hoc
mosinit.hoc
ringplot.hoc *
run.py
run_hoc.sh
run_RmpRiTau.py
template.hoc
VERSION
                            
/*                                                                               
Copyright (c) 2015 EPFL-BBP, All rights reserved.                                
                                                                                 
THIS SOFTWARE IS PROVIDED BY THE BLUE BRAIN PROJECT ``AS IS''                    
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,            
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SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR                  
BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,            
WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE             
OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN           
IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.                                    
                                                                                 
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/legalcode or send a letter to Creative Commons, 171
Second Street, Suite 300, San Francisco, California, 94105, USA.                 
*/ 

/*                                                                               
 * @file createsimulation.hoc                                                           
 * @brief Set up the simulation                                
 * @author Werner Van Geit @ BBP                                                 
 * @date 2015                                                                    
*/        

//modified by Fabio Simoes de Souza @ UFABC, 2016

load_file("nrngui.hoc")
load_file("import3d.hoc")

load_file("morphology.hoc")
load_file("biophysics.hoc")
load_file("template.hoc")

//======================== settings ===================================

//PARAMETERS 

NUMBER_NEURONS=1  //number neurons per layer

tmax=25000 //simulation time

//Synaptic Stimulation Sequencies
//sensory, motor, or re-afference sequencies
//cond="1" //basal only -control-
//cond="2a" //sensory only 1 pulse
//cond="3a" //motor only 1 pulse
//cond="4a" //motor with sensory re-afference 1 pulse
//cond="2b" //sensory only 4 pulses
//cond="3b" //motor only 4 pulses
//cond="4b" //motor with sensory re-afference 4 pulses

strdef condi //string
condi="4b"

//set poisson random seeds for presynaptic spikes
if (strcmp(condi,"1") == 0) {
RANDOM_SEED=1
print RANDOM_SEED
} else if (strcmp(condi,"2a") == 0) {
RANDOM_SEED=2
print RANDOM_SEED
} else if (strcmp(condi,"3a") == 0) {
RANDOM_SEED=3
print RANDOM_SEED
} else if (strcmp(condi,"4a") == 0) {
RANDOM_SEED=4
print RANDOM_SEED
} else if (strcmp(condi,"2b") == 0) {
RANDOM_SEED=5
print RANDOM_SEED
} else if (strcmp(condi,"3b") == 0) {
RANDOM_SEED=6
print RANDOM_SEED
} else if (strcmp(condi,"4b") == 0) {
RANDOM_SEED=7
print RANDOM_SEED
}

//=================== creating cell object ===========================
objref semente

objref cell0[NUMBER_NEURONS]  //pre synaptic cells
objref cell1[NUMBER_NEURONS]  //post synaptic cells


proc create_cell() { local synapses_enabled

synapses_enabled = $1
semente = new Random($2)
semente.uniform(0,1000000000)

NUMBER_NEURONS = $3


for i=0, NUMBER_NEURONS-1 {
//print i
semente_pick=int(semente.repick())
    cell0[i] = new cADpyr232_L5_TTPC1_b6836fcf6f(synapses_enabled,semente_pick)

semente_pick=int(semente.repick())
    cell1[i] = new cADpyr232_L5_TTPC1_b6836fcf6f(synapses_enabled,semente_pick)
}
}

////////////////////////////////////////
//Setting up for Saving Spiking Events//
///////////////////////////////////////

//==================== recording settings ==========================

objref Cell0MatrixS, Cell1MatrixS
objref Cell0MatrixV, Cell1MatrixV

objref Cell1Matrixgsyn0, Cell1Matrixgsyn1
objref Cell1Matrixpsyn0, Cell1Matrixpsyn1

objref sav_Cell0MatrixS,sav_Cell1MatrixS
objref sav_Cell0MatrixV,sav_Cell1MatrixV

objref sav_Cell1Matrixgsyn0,sav_Cell1Matrixgsyn1
objref sav_Cell1Matrixpsyn0,sav_Cell1Matrixpsyn1

objref time
objref nc0, nil0, nc1, nil1

proc create_recording() {

NUMBER_NEURONS=$1

//generate matrix and send the data to them

Cell0MatrixS=new Matrix() 
Cell1MatrixS=new Matrix() 
Cell0MatrixV=new Matrix() 
Cell1MatrixV=new Matrix() 

Cell1Matrixgsyn0=new Matrix() 
Cell1Matrixgsyn1=new Matrix() 
Cell1Matrixpsyn0=new Matrix() 
Cell1Matrixpsyn1=new Matrix() 

sav_Cell0MatrixS=new File()
sav_Cell1MatrixS=new File()
sav_Cell0MatrixV=new File()
sav_Cell1MatrixV=new File()

sav_Cell1Matrixgsyn0=new File()
sav_Cell1Matrixgsyn1=new File()
sav_Cell1Matrixpsyn0=new File()
sav_Cell1Matrixpsyn1=new File()

//Open recording Files
sav_Cell0MatrixS.wopen("hoc_recordings/Cell0MatrixS.dat")
sav_Cell1MatrixS.wopen("hoc_recordings/Cell1MatrixS.dat")
sav_Cell0MatrixV.wopen("hoc_recordings/Cell0MatrixV.dat")
sav_Cell1MatrixV.wopen("hoc_recordings/Cell1MatrixV.dat")

sav_Cell1Matrixgsyn0.wopen("hoc_recordings/Cell1Matrixgsyn0.dat")
sav_Cell1Matrixgsyn1.wopen("hoc_recordings/Cell1Matrixgsyn1.dat")
sav_Cell1Matrixpsyn0.wopen("hoc_recordings/Cell1Matrixpsyn0.dat")
sav_Cell1Matrixpsyn1.wopen("hoc_recordings/Cell1Matrixpsyn1.dat")

time = new Vector()
time.record(&t, dt)

for i=0, NUMBER_NEURONS-1 {
//set voltage recording
cell0[i].voltagem.record(&cell0[i].soma.v(0.5),dt)
cell1[i].voltagem.record(&cell1[i].soma.v(0.5),dt)

//set gsyn recording
cell1[i].gsyn0.record(&cell1[i].sinapse0[0].g,dt)
cell1[i].gsyn1.record(&cell1[i].sinapse1[0].g,dt)

//set psyn recording
cell1[i].psyn0.record(&cell1[i].sinapse0[0].peso,dt)
cell1[i].psyn1.record(&cell1[i].sinapse1[0].peso,dt)

//Fabio´s stuff
//saving spiketimes
cell0[i].soma cell0[i].nc = new NetCon(&cell0[i].soma.v(0.5),nil0)
cell1[i].soma cell1[i].nc = new NetCon(&cell1[i].soma.v(0.5),nil1)

cell0[i].nc.record(cell0[i].spiketimes)
cell1[i].nc.record(cell1[i].spiketimes)
}

}

//==================== stimulus settings ===========================

//current stimulus
objref step_stimulus[NUMBER_NEURONS]

proc create_current_stimulus() {
NUMBER_NEURONS = $1
for i=0, NUMBER_NEURONS-1 {
access cell0[i].soma
step_stimulus[i] = new IClamp(0.5)
step_stimulus[i].dur = 1000
step_stimulus[i].del = 11000+1e4 
step_stimulus[i].amp = 0.5 //nA 	
}
}

//synaptic stimulus

proc create_synaptic_stimulus() {

//sensory, motor, or re-afference sequencies
//cond="1" //basal only -control-
//cond="2a" //sensory only 1 pulse
//cond="3a" //motor only 1 pulse
//cond="4a" //motor with sensory re-afference 1 pulse
//cond="2b" //sensory only 4 pulses
//cond="3b" //motor only 4 pulses
//cond="4b" //motor with sensory re-afference 4 pulses

strdef cond  //string
cond = $s1

tmax=$2

//synapses index
id1=19 //L5_TTP1
id2=20 //L5_TTPC2
id3=3 //L23_PC

freq=2 //basal pre-synaptic firing freq

tstop=1 //1 ms
run() 

print tstop

if (strcmp(cond,"1") == 0) {
print RANDOM_SEED

cell0_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // basal sensory stimulus on 
cell1_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // basal motor stimulus on 
tstop=10000
print tstop
continuerun(tstop)//t=10000

cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // test stimulus on
tstop=11000 
continuerun(tstop) //t=11000

cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // test stimulus off
tstop=tmax //run till tmax

continuerun(tstop)

} else if (strcmp(cond,"2a") == 0) {
print RANDOM_SEED

cell0_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // basal sensory stimulus on 
cell1_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // basal motor stimulus on 
tstop=1000 
continuerun(tstop) //t=1000

cell0_synapse_on_off(id1,50,1,NUMBER_NEURONS) // sensory stimulus on 
tstop=2000 //run till tstop ms
continuerun(tstop) //t=2000

cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // sensory stimulus off 
tstop=10000 
continuerun(tstop) //t=10000 ms

cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // test stimulus on 
tstop=11000 
continuerun(tstop)//t=11000

cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // test stimulus off
tstop=tmax //run till tmax
continuerun(tstop)

} else if (strcmp(cond,"3a") == 0) {
print RANDOM_SEED

cell0_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // basal sensory stimulus on 
cell1_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // basal motor stimulus on 
tstop=1000 
continuerun(tstop) //t=1000

cell1_synapse_on_off(id2,50,1,NUMBER_NEURONS) // motor stimulus on 
tstop=2000 //run till tstop ms
continuerun(tstop) //t=2000

cell1_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // motor stimulus off
tstop=10000 
continuerun(tstop) //t=10000 ms

cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // test stimulus on 
tstop=11000 
continuerun(tstop)//t=11000

cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // test stimulus off
tstop=tmax //run till tmax
continuerun(tstop)

} else if (strcmp(cond,"4a") == 0) {
print RANDOM_SEED

cell0_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // basal sensory stimulus on 
cell1_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // basal motor stimulus on 
tstop=1000
continuerun(tstop) //t=1000

cell1_synapse_on_off(id2,50,1,NUMBER_NEURONS) // motor stimulus on 
tstop=1200 
continuerun(tstop) //t=1200

cell0_synapse_on_off(id1,50,1,NUMBER_NEURONS) // re-affer sensory stimulus on
tstop=2000
continuerun(tstop) //t=2000

cell1_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // motor stimulus off 
tstop=2200
continuerun(tstop) //t=2200

cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // re-affer sensory stimulus off
tstop=10000 //pause between stimulus
continuerun(tstop)

cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // test stimulus on 
tstop=11000
continuerun(tstop) //t=11000

cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // test stimulus off
tstop=tmax //run till tmax
continuerun(tstop)

} else if (strcmp(cond,"2b") == 0) {
print RANDOM_SEED

cell0_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // basal sensory stimulus on 
cell1_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // basal motor stimulus on 
tstop=1000 
continuerun(tstop) //t=1000

cell0_synapse_on_off(id1,50,1,NUMBER_NEURONS) // sensory stimulus on 1
tstop=2000 //run till tstop ms
continuerun(tstop) //t=2000

cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // sensory stimulus off 
tstop=3000 
continuerun(tstop) //t=10000 ms

cell0_synapse_on_off(id1,50,1,NUMBER_NEURONS) // sensory stimulus on 2
tstop=4000 //run till tstop ms
continuerun(tstop) //t=2000

cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // sensory stimulus off 
tstop=5000 
continuerun(tstop) //t=10000 ms

cell0_synapse_on_off(id1,50,1,NUMBER_NEURONS) // sensory stimulus on 3
tstop=6000 //run till tstop ms
continuerun(tstop) //t=2000

cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // sensory stimulus off 
tstop=7000 
continuerun(tstop) //t=10000 ms

cell0_synapse_on_off(id1,50,1,NUMBER_NEURONS) // sensory stimulus on 4
tstop=8000 //run till tstop ms
continuerun(tstop) //t=2000

cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // sensory stimulus off 
tstop=9000 
continuerun(tstop) //t=10000 ms


cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // sensory stimulus off 
tstop=10000 
continuerun(tstop) //t=10000 ms

cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // test stimulus on 
tstop=11000 
continuerun(tstop)//t=11000

cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // test stimulus off
tstop=tmax //run till tmax
continuerun(tstop)

} else if (strcmp(cond,"3b") == 0) {

print RANDOM_SEED

cell0_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // basal sensory stimulus on 
cell1_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // basal motor stimulus on 
tstop=1000 
continuerun(tstop) //t=1000

cell1_synapse_on_off(id2,50,1,NUMBER_NEURONS) // motor stimulus on 1
tstop=2000 //run till tstop ms
continuerun(tstop) //t=2000

cell1_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // motor stimulus off
tstop=3000 
continuerun(tstop) //t=10000 ms

cell1_synapse_on_off(id2,50,1,NUMBER_NEURONS) // motor stimulus on 2
tstop=4000 //run till tstop ms
continuerun(tstop) //t=2000

cell1_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // motor stimulus off
tstop=5000 
continuerun(tstop) //t=10000 ms

cell1_synapse_on_off(id2,50,1,NUMBER_NEURONS) // motor stimulus on 3
tstop=6000 //run till tstop ms
continuerun(tstop) //t=2000

cell1_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // motor stimulus off
tstop=7000 
continuerun(tstop) //t=10000 ms

cell1_synapse_on_off(id2,50,1,NUMBER_NEURONS) // motor stimulus on 4
tstop=8000 //run till tstop ms
continuerun(tstop) //t=2000

cell1_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // motor stimulus off
tstop=9000 
continuerun(tstop) //t=10000 ms

cell1_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // motor stimulus off
tstop=10000 
continuerun(tstop) //t=10000 ms


cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // test stimulus on 
tstop=11000 
continuerun(tstop)//t=11000

cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // test stimulus off
tstop=tmax //run till tmax
continuerun(tstop)

} else (strcmp(cond,"4b") == 0) {

print RANDOM_SEED

cell0_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // basal sensory stimulus on 
cell1_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // basal motor stimulus on 
tstop=1000+1e4
continuerun(tstop) //t=1000

cell1_synapse_on_off(id2,50,1,NUMBER_NEURONS) // motor stimulus on 1
tstop=1200+1e4 
continuerun(tstop) //t=1200

cell0_synapse_on_off(id1,50,1,NUMBER_NEURONS) // re-affer sensory stimulus on
tstop=2000+1e4
continuerun(tstop) //t=2000

cell1_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // motor stimulus off 
tstop=2200+1e4
continuerun(tstop) //t=2200

cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // re-affer sensory stimulus off
tstop=3000+1e4 //pause between stimulus
continuerun(tstop)


cell1_synapse_on_off(id2,50,1,NUMBER_NEURONS) // motor stimulus on 2
tstop=3200+1e4 
continuerun(tstop) //t=1200

cell0_synapse_on_off(id1,50,1,NUMBER_NEURONS) // re-affer sensory stimulus on
tstop=4000+1e4
continuerun(tstop) //t=2000

cell1_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // motor stimulus off 
tstop=4200+1e4
continuerun(tstop) //t=2200

cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // re-affer sensory stimulus off
tstop=5000+1e4 //pause between stimulus
continuerun(tstop)


cell1_synapse_on_off(id2,50,1,NUMBER_NEURONS) // motor stimulus on 3
tstop=5200+1e4 
continuerun(tstop) //t=1200

cell0_synapse_on_off(id1,50,1,NUMBER_NEURONS) // re-affer sensory stimulus on
tstop=6000+1e4
continuerun(tstop) //t=2000

cell1_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // motor stimulus off 
tstop=6200+1e4
continuerun(tstop) //t=2200

cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // re-affer sensory stimulus off
tstop=7000+1e4 //pause between stimulus
continuerun(tstop)


cell1_synapse_on_off(id2,50,1,NUMBER_NEURONS) // motor stimulus on 4
tstop=7200+1e4 
continuerun(tstop) //t=1200

cell0_synapse_on_off(id1,50,1,NUMBER_NEURONS) // re-affer sensory stimulus on
tstop=8000+1e4
continuerun(tstop) //t=2000

cell1_synapse_on_off(id2,freq,1,NUMBER_NEURONS) // motor stimulus off 
tstop=8200+1e4
continuerun(tstop) //t=2200

cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // re-affer sensory stimulus off
tstop=9000+1e4 //pause between stimulus
continuerun(tstop)

cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // re-affer sensory stimulus off
tstop=10000+1e4 //pause between stimulus
continuerun(tstop)


cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // test stimulus on 
tstop=11000+1e4
continuerun(tstop) //t=11000

cell0_synapse_on_off(id1,50,0,NUMBER_NEURONS) // test stimulus off
tstop=tmax //run till tmax
continuerun(tstop)

}

}

//turn synapses on-off
//id, freq, 0 (on) or 1(off)
proc cell0_synapse_on_off() {local id
NUMBER_NEURONS=$4
for i=0, NUMBER_NEURONS-1 {
id=cell0[i].synapses.pre_mtypes.x[$1]
cell0[i].synapses.active_pre_mtypes.x[id]=$3
cell0[i].synapses.pre_mtype_freqs.x[id]=$2
cell0[i].synapses.update_synapses(synapse_plot)
}
}

//id, freq, 0 (on) or 1(off)
proc cell1_synapse_on_off() {local id
NUMBER_NEURONS=$4
for i=0, NUMBER_NEURONS-1 {
id=cell1[i].synapses.pre_mtypes.x[$1]
cell1[i].synapses.active_pre_mtypes.x[id]=$3
cell1[i].synapses.pre_mtype_freqs.x[id]=$2
cell1[i].synapses.update_synapses(synapse_plot)
}
}


//============================= simulation ================================

proc simulate() {
    cvode.active(0)

    run()
}

//============================= saving results ============================

objref tempo
objref savtime

proc save_recording() {

///////////////////////
//Save Data to Files//
//////////////////////

//save time
savtime=new File()
savtime.wopen("hoc_recordings/time.dat")

time.printf(savtime)
savtime.close()

//save spikes and voltage
Cell0MatrixS.resize(time.size(),NUMBER_NEURONS) 
Cell1MatrixS.resize(time.size(),NUMBER_NEURONS) 
Cell0MatrixV.resize(time.size(),NUMBER_NEURONS) 
Cell1MatrixV.resize(time.size(),NUMBER_NEURONS) 

for i = 0, NUMBER_NEURONS-1 {
Cell0MatrixS.setcol(i,cell0[i].spiketimes)  
Cell1MatrixS.setcol(i,cell1[i].spiketimes)  
Cell0MatrixV.setcol(i,cell0[i].voltagem)  
Cell1MatrixV.setcol(i,cell1[i].voltagem)  
}

//record Vectors to File	
Cell0MatrixS.fprint(sav_Cell0MatrixS," %g")
Cell1MatrixS.fprint(sav_Cell1MatrixS," %g")
Cell0MatrixV.fprint(sav_Cell0MatrixV," %g")
Cell1MatrixV.fprint(sav_Cell1MatrixV," %g")

//Close Files	
sav_Cell0MatrixS.close()
sav_Cell1MatrixS.close()
sav_Cell0MatrixV.close()
sav_Cell1MatrixV.close()


//save synaptic conductances and weights
Cell1Matrixgsyn0.resize(time.size(),NUMBER_NEURONS) 
Cell1Matrixgsyn1.resize(time.size(),NUMBER_NEURONS) 
Cell1Matrixpsyn0.resize(time.size(),NUMBER_NEURONS) 
Cell1Matrixpsyn1.resize(time.size(),NUMBER_NEURONS) 

for i = 0, NUMBER_NEURONS-1 {
Cell1Matrixgsyn0.setcol(i,cell1[i].gsyn0)  
Cell1Matrixgsyn1.setcol(i,cell1[i].gsyn1)  
Cell1Matrixpsyn0.setcol(i,cell1[i].psyn0)  
Cell1Matrixpsyn1.setcol(i,cell1[i].psyn1)  
}

//record Vectors to File	
Cell1Matrixgsyn0.fprint(sav_Cell1Matrixgsyn0," %g")
Cell1Matrixgsyn1.fprint(sav_Cell1Matrixgsyn1," %g")
Cell1Matrixpsyn0.fprint(sav_Cell1Matrixpsyn0," %g")
Cell1Matrixpsyn1.fprint(sav_Cell1Matrixpsyn1," %g")

//Close Files	
sav_Cell1Matrixgsyn0.close()
sav_Cell1Matrixgsyn1.close()
sav_Cell1Matrixpsyn0.close()
sav_Cell1Matrixpsyn1.close()
}


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