//turn off rectification of GABAergic inputs
for ii=1,totVgatAt  {
    synGABArect[ii-1].slope_factor=0.3
    synGABArect[ii-1].V50=-150
}
//reduce synaptic weight to weight at -70 mV
GABAweight1=0.25*4*GABAweight_total/5 
reset_InhSyn()  
ActSyn_inh = set_InhSyn_syn_fixed(curInh_SLM, randShift_inh/4, nInhAct_SLM, shapeInh) 
print "In SLM, the number of activated inhibitory synapses at each pulse are: ",ActSyn_inh.x[0], ", " , ActSyn_inh.x[1], ", " , ActSyn_inh.x[2], ", " , ActSyn_inh.x[3], ", " , ActSyn_inh.x[4]

// Create graphs for visualisation.
objref voltNoRect_d1,voltNoRect_d2

voltNoRect_d1 = new Graph()
voltNoRect_d1.addvar("soma.sec.v(0.5)",2,1)
voltNoRect_d1.addvar("dend7Ref.sec.v(0.5)",3,1) 
voltNoRect_d1.addvar("dend2Ref.sec.v(0.5)",4,1) 
voltNoRect_d1.label("Linear")
voltNoRect_d1.exec_menu("Keep Lines")
voltNoRect_d1.size(stimStart-20,tstop,-75,-5)

voltNoRect_d2 = new Graph()
voltNoRect_d2.addvar("soma.sec.v(0.5)",2,1)
voltNoRect_d2.addvar("dend7Ref.sec.v(0.5)",3,1) 
voltNoRect_d2.addvar("dend1Ref.sec.v(0.5)",4,1) 
voltNoRect_d2.label("Linear")
voltNoRect_d2.exec_menu("Keep Lines")
voltNoRect_d2.size(stimStart-20,tstop,-75,-5)

curGr = graphList[0].append(voltNoRect_d1)
curGr = graphList[0].append(voltNoRect_d2)

for(jj=1; jj<simul_iter+1; jj=jj+1){
    print jj
	// Clear excitation and then turn on select synapses.
	activateExcitation(cellList,-1,1) // clear excitation
	curExc_SLM = activateExcitation(tuftList,jj*nExcAct_SLM,randShift_exc) // activate excitatory synapses
	
    shape_no=(jj/2)-1
    if (jj%2==1){shape_no=(jj-1)/2}
	
	ActSyn = set_gluSyn_fixed_N(curExc_SLM, randShift_exc/2, 0.1, norm_Pr_exc, shapeExc[shape_no])
	print "In SLM, the number of activated synapses at each pulse are: ",ActSyn.x[0], ", " , ActSyn.x[1], ", " , ActSyn.x[2], ", " , ActSyn.x[3], ", " , ActSyn.x[4]

    init() 
    run()
    storeM_noRect.setcol(jj-1,recv_soma)
    storeM_noRect.setcol(jj-1+simul_iter,recv_tuft1) 
    storeM_noRect.setcol(jj-1+2*simul_iter,recv_tuft2) 
    storeM_noRect.setcol(jj-1+3*simul_iter,recv_tuft3)
    storeM_noRect.setcol(jj-1+4*simul_iter,recv_tuft4) 
    storeM_noRect.setcol(jj-1+5*simul_iter,recv_tuft5) 
    storeM_noRect.setcol(jj-1+6*simul_iter,recv_trunk) 
    storeM_noRect.setcol(jj-1+7*simul_iter,recv_trunk2)
        
    reset_gluSyn()
}


graphList[0].remove_all()

// SAVE OUTPUT
//print2file(storeM_noRect,file_name2,ColLabel)

//turn on rectification of GABAergic inputs
for ii=1,totVgatAt  {
    synGABArect[ii-1].slope_factor=3
    synGABArect[ii-1].V50=-52
}
//reset synaptic weight 
GABAweight1=4*GABAweight_total/5 
reset_InhSyn()  
ActSyn_inh = set_InhSyn_syn_fixed(curInh_SLM, randShift_inh/4, nInhAct_SLM, shapeInh) 
print "In SLM, the number of activated inhibitory synapses at each pulse are: ",ActSyn_inh.x[0], ", " , ActSyn_inh.x[1], ", " , ActSyn_inh.x[2], ", " , ActSyn_inh.x[3], ", " , ActSyn_inh.x[4]