/* Copyright (c) 2015 EPFL-BBP, All rights reserved.                             
                                                                                 
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THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR           
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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 
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*/      

/*                                                                               
 * @file synapses.hoc                                                          
 * @brief Template that store the synapses                                
 * @author Werner Van Geit @ BBP                                                 
 * @date 2015                                                                    
*/        

begintemplate synapses_b030a58a66                

public load_synapses, synapse_list, netcon_list, netstim_list, \
        pre_mtypes_bitmap, n_of_pre_mtypes, id_mtype_map, pre_mtypes, \
        active_pre_mtypes, update_synapses, pre_mtype_netconlists, \
        pre_mtype_freqs,  pre_mtype_netstimlists, reset_synapses, \
        weights, delays, synconf_list

objref synapse_list, netcon_list, netstim_list, pre_mtypes_bitmap, \
        id_mtype_map, pre_mtypes, active_pre_mtypes, pre_mtype_netconlists, \
        pre_cell_ids, pre_mtype_freqs, pre_mtype_netstimlists, rng_list, \
        pre_mtype_weightlists, pre_mtype_synapselists, \
        were_active_pre_mtypes, pre_mtypes_excinh, weights, delays, \
        synconf_list, stringtools, tmp_synapse, this

/** Constructor */                                                          
proc init() { local mtype_id localobj mtype_map_file, mtype_name_string
    strdef line, mtype_name

    // Number of m-types
    n_of_mtypes = 55

    synapse_list = new List()
    rng_list = new List()
    netcon_list = new List()
    netstim_list = new List()
    weights = new Vector()
    delays = new Vector()
    pre_mtype_netconlists = new List()
    pre_mtype_netstimlists = new List()
    pre_mtype_weightlists = new List()
    pre_mtype_synapselists = new List()
    pre_mtypes_bitmap = new Vector(n_of_mtypes, 0)
    pre_mtypes_excinh = new Vector(n_of_mtypes, 1)
    active_pre_mtypes = new Vector(n_of_mtypes, 1)
    were_active_pre_mtypes = new Vector(n_of_mtypes, 1)
    pre_mtype_freqs = new Vector(n_of_mtypes, 2.0)

    pre_mtypes = new Vector()
    pre_cell_ids = new Vector()

    for i=0, n_of_mtypes-1 {
        pre_mtype_netconlists.append(new List())
        pre_mtype_netstimlists.append(new List())
        pre_mtype_synapselists.append(new List())
        pre_mtype_weightlists.append(new Vector())
    }

    id_mtype_map = new List()

    // Read the mapping between m-type names and m-type IDs
    mtype_map_file = new File("synapses/mtype_map.tsv")
    mtype_map_file.ropen()
    for i=0, n_of_mtypes-1 {
        mtype_map_file.gets(line)
        sscanf(line, "%d\t%s", &mtype_id, mtype_name)
        mtype_name_string = new String(mtype_name)
        id_mtype_map.append(mtype_name_string)
    }
    mtype_map_file.close()

    stringtools = new StringFunctions()                                              
}

/** Update the active synapses in the GUI
                                                                                 
    Arguments:                                                                   
        synapse_plot: synapse plot object               
*/                                                          
proc update_synapses() { localobj pre_mtype_netcons, pre_mtype_netstims, \
                            pre_mtype_weights, pre_mtype_synapses, synapse_plot
   // synapse_plot = $o1

    for i=0, n_of_mtypes-1 {
        pre_mtype_netcons = pre_mtype_netconlists.o(i)
        pre_mtype_weights = pre_mtype_weightlists.o(i)
        pre_mtype_synapses = pre_mtype_synapselists.o(i)
		
		/*

        // Use different color for exc / inh synapses 
        if (pre_mtypes_excinh.x[i] == 0) {
            // Red, excitatory
            syn_color = 2
        } else {
            // Yellow, inhibitory
            syn_color = 5
        }
		
		*/

        // Loop over the synapses. 
        // enable and plot active synapses, 
        // disable and remove inactive ones
        // only do something if status of a synapse changed since last update
        for j=0, pre_mtype_netcons.count()-1 {
            if (active_pre_mtypes.x[i] == 1 && \
                    were_active_pre_mtypes.x[i] == 0) {
                // Set weight to correct value
                pre_mtype_netcons.o(j).weight = pre_mtype_weights.x[j]
                // Draw the synapses
            //    synapse_plot.point_mark(pre_mtype_synapses.o(j), \
            //                            syn_color, 4, 6)
            } else if (active_pre_mtypes.x[i] == 0 && \
                    were_active_pre_mtypes.x[i] == 1) {
                // Disable synapse by setting weight to 0
                pre_mtype_netcons.o(j).weight = 0.0
                // Remove synapse from plot
             //   synapse_plot.point_mark_remove(pre_mtype_synapses.o(j))
            }
        }

        // Update the list were_active_pre_mtypes, which is the list of 
        // mtypes that are active after this update
        if (active_pre_mtypes.x[i] == 1 && were_active_pre_mtypes.x[i] == 0) {
            were_active_pre_mtypes.x[i] = 1
        } else if (active_pre_mtypes.x[i] == 0 && \
                    were_active_pre_mtypes.x[i] == 1) {
            were_active_pre_mtypes.x[i] = 0
        }

        // Update the netstim frequencies
        pre_mtype_netstims = pre_mtype_netstimlists.o(i)
        for j=0, pre_mtype_netstims.count()-1 {
            pre_mtype_netstims.o(j).interval = 1000.0 / pre_mtype_freqs.x[i]
        }
    }

}

/** Disable all the synapses */
proc reset_synapses() {
    for i=0, n_of_mtypes-1 {
        were_active_pre_mtypes.x[i] = 0
    }
}



/** Load the synconf file
    Arguments:                                                                   
        nsynapses: number of synapses in cell
*/                                                          
proc load_synconf() { localobj synconf_file, synconf_gids
    strdef synconf_string
    nsynapses = $1

    synconf_list = new List()
    for isynapse=0, nsynapses-1 {
        synconf_list.append(new List())
    }

    synconf_file = new File("synapses/synconf.txt")                                                        
    {synconf_file.ropen()}
    
    synconf_gids = new Vector()                                                      
                                                                                                                                                                 
    while (synconf_file.gets(synconf_string) > 0) {                                  
        stringtools.left(synconf_string, stringtools.len(synconf_string)-1)          
        synconf_gids.scantil(synconf_file, -1e15)                                    
        for i=0, synconf_gids.size()-1 { 
            synconf_list.o(synconf_gids.x[i]).append(new String(synconf_string))
        }                                                 
    }                                                     
}

/** Load all the synapses
                                                                                 
    Arguments:                                                                   
        cell_ref: reference to the cell object               
*/                                                          


	    objref xx_p,yy_p,zz_p,length_p,range_p,xint_p,yint_p,zint_p,seg_cen,ref

proc load_synapses() { local isynapse, nsynapses, ncols, synapse_id, \
                            pre_cell_id, seg_x, synapse_type, \
                            dep, fac, use, tau_d, delay, weight, \
                            base_seed, gid, netstim_id \
                        localobj synapse_data, synapse_file, cell_ref, \
                            synapse, section, rng, netcon, netstim

    printf("Starting to add synapses\n")

    strdef sectionlist_name, synapse_type_name, synconf_string, head_string, \
        tail_string

    cell_ref = $o1
	
	rlx=$2
	rly=$3
	rlz=$4
	rlxang=$5
	
	ref=new Random(4)

    // Load the file that contains all the information about the synapses
    synapse_file = new File("synapses/synapses.tsv")
    {synapse_file.ropen()}

    // Data structure to store the data in synapses.tsv
    synapse_data = new Matrix()
    synapse_data.scanf(synapse_file)
    
    synapse_file.close()

    nsynapses = synapse_data.nrow
    ncols = synapse_data.ncol

    // There is only one cell in this simulation, so let's give it gid 1
    gid = 51423

    // Base seed for the rngs
    base_seed = 0

    // Load list of hoc commands that have to be execute on every synapse
    // to set certain parameters that are not specified in synapse.tsv
    load_synconf(nsynapses)

    for isynapse=0, nsynapses-1 {
        // Read the synapse parameters from the matrix
        synapse_id = synapse_data.x[isynapse][0]
        pre_cell_id = synapse_data.x[isynapse][1]
        pre_mtype = synapse_data.x[isynapse][2]
        sectionlist_id = synapse_data.x[isynapse][3]
        sectionlist_index = synapse_data.x[isynapse][4]
        seg_x = synapse_data.x[isynapse][5]
        synapse_type = synapse_data.x[isynapse][6]
        dep = synapse_data.x[isynapse][7] 
        fac = synapse_data.x[isynapse][8] 
        use = synapse_data.x[isynapse][9] 
        tau_d = synapse_data.x[isynapse][10] 
 //       delay = synapse_data.x[isynapse][11]
        delay = ref.uniform(0,2)  
//        weight = synapse_data.x[isynapse][12] 

        // Create sectionref to the section the synapse will be placed on
        if ( sectionlist_id == 0 ) {
            cell_ref.soma[sectionlist_index] section = new SectionRef()        
            sectionlist_name = "somatic"
			
		cell_ref.soma[sectionlist_index] nseg_p=nseg
		cell_ref.soma[sectionlist_index] nn = n3d()	
		
		xx_p = new Vector(nn) 
		yy_p = new Vector(nn)
		zz_p = new Vector(nn)
		length_p = new Vector(nn)
 		
		for ii = 0,nn-1 {					
		cell_ref.soma[sectionlist_index] xx_p.x[ii] = x3d(ii) 
		cell_ref.soma[sectionlist_index] yy_p.x[ii] = y3d(ii) 
		cell_ref.soma[sectionlist_index] zz_p.x[ii] = z3d(ii) 
		cell_ref.soma[sectionlist_index] length_p.x[ii] = arc3d(ii) 
		}

		
		length_p.div(length_p.x[nn-1])
        
		range_p = new Vector(nseg_p+2) 
		range_p.indgen(1/nseg_p) 
		range_p.sub(1/(2*nseg_p))
		range_p.x[0]=0
		range_p.x[nseg_p+1]=1

        
		xint_p = new Vector(nseg_p+2) 
		yint_p = new Vector(nseg_p+2)
		zint_p = new Vector(nseg_p+2)
		xint_p.interpolate(range_p, length_p, xx_p)
		yint_p.interpolate(range_p, length_p, yy_p)
		zint_p.interpolate(range_p, length_p, zz_p)									

		xint_p.remove(nseg_p+1)
		xint_p.remove(0)
		yint_p.remove(nseg_p+1)
		yint_p.remove(0)
		zint_p.remove(nseg_p+1)
		zint_p.remove(0)
		
		seg_cen=new Vector(nseg_p)
		
		for jj=1,nseg_p {
		      seg_cen.x[jj-1]=abs(((1/(nseg_p+1))*jj)-seg_x)
			  }
			  
		x_syn_real=xint_p.x[seg_cen.min_ind()]
		y_syn_real=yint_p.x[seg_cen.min_ind()]
		z_syn_real=zint_p.x[seg_cen.min_ind()]
		
			pnet_ad_x1=((rlx*cos(rlxang))-(rlz*sin(rlxang)))-(rlx)
            pnet_ad_y1=(rly)-(rly)
            pnet_ad_z1=(((rlx)*sin(rlxang))+((rlz)*cos(rlxang)))-(rlz)
			
			pxrot1=((rlx+x_syn_real)*cos(rlxang))-((rlz+z_syn_real)*sin(rlxang))
	        pyrot1=rly+y_syn_real
            pzrot1=((rlx+x_syn_real)*sin(rlxang))+((rlz+z_syn_real)*cos(rlxang))
	
	        px_fin=pxrot1-pnet_ad_x1
	        py_fin=pyrot1-pnet_ad_y1
	        pz_fin=pzrot1-pnet_ad_z1
		
		rdist1=sqrt(((px_fin-$6)^2)+((py_fin-$7)^2)+((pz_fin-$8)^2))
		rdist2=sqrt(((px_fin-$10)^2)+((py_fin-$11)^2)+((pz_fin-$12)^2))

		prob_act1=exp(-rdist1/$9)
		prob_act2=exp(-rdist2/$13)

		
		
		if ( ref.uniform(0,1) < prob_act1 || ref.uniform(0,1) < prob_act2 ) {
		        weight = synapse_data.x[isynapse][12] 
				} else {
		        weight = 0
				}
			
        } else if ( sectionlist_id == 1 ) {
            cell_ref.dend[sectionlist_index] section = new SectionRef()       
            sectionlist_name = "dend" 
			
		cell_ref.dend[sectionlist_index] nseg_p=nseg
		cell_ref.dend[sectionlist_index] nn = n3d()	
		
		xx_p = new Vector(nn) 
		yy_p = new Vector(nn)
		zz_p = new Vector(nn)
		length_p = new Vector(nn)
 		
		for ii = 0,nn-1 {					
		cell_ref.dend[sectionlist_index] xx_p.x[ii] = x3d(ii) 
		cell_ref.dend[sectionlist_index] yy_p.x[ii] = y3d(ii) 
		cell_ref.dend[sectionlist_index] zz_p.x[ii] = z3d(ii) 
		cell_ref.dend[sectionlist_index] length_p.x[ii] = arc3d(ii) 
		}

		
		length_p.div(length_p.x[nn-1])
        
		range_p = new Vector(nseg_p+2) 
		range_p.indgen(1/nseg_p) 
		range_p.sub(1/(2*nseg_p))
		range_p.x[0]=0
		range_p.x[nseg_p+1]=1

        
		xint_p = new Vector(nseg_p+2) 
		yint_p = new Vector(nseg_p+2)
		zint_p = new Vector(nseg_p+2)
		xint_p.interpolate(range_p, length_p, xx_p)
		yint_p.interpolate(range_p, length_p, yy_p)
		zint_p.interpolate(range_p, length_p, zz_p)									

		xint_p.remove(nseg_p+1)
		xint_p.remove(0)
		yint_p.remove(nseg_p+1)
		yint_p.remove(0)
		zint_p.remove(nseg_p+1)
		zint_p.remove(0)
		
		seg_cen=new Vector(nseg_p)
		
		for jj=1,nseg_p {
		      seg_cen.x[jj-1]=abs(((1/(nseg_p+1))*jj)-seg_x)
			  }
			  
        
	    x_syn_real=xint_p.x[seg_cen.min_ind()]
		y_syn_real=yint_p.x[seg_cen.min_ind()]
		z_syn_real=zint_p.x[seg_cen.min_ind()]
		
			pnet_ad_x1=((rlx*cos(rlxang))-(rlz*sin(rlxang)))-(rlx)
            pnet_ad_y1=(rly)-(rly)
            pnet_ad_z1=(((rlx)*sin(rlxang))+((rlz)*cos(rlxang)))-(rlz)
			
			pxrot1=((rlx+x_syn_real)*cos(rlxang))-((rlz+z_syn_real)*sin(rlxang))
	        pyrot1=rly+y_syn_real
            pzrot1=((rlx+x_syn_real)*sin(rlxang))+((rlz+z_syn_real)*cos(rlxang))
	
	        px_fin=pxrot1-pnet_ad_x1
	        py_fin=pyrot1-pnet_ad_y1
	        pz_fin=pzrot1-pnet_ad_z1
		
		rdist1=sqrt(((px_fin-$6)^2)+((py_fin-$7)^2)+((pz_fin-$8)^2))
		rdist2=sqrt(((px_fin-$10)^2)+((py_fin-$11)^2)+((pz_fin-$12)^2))

		prob_act1=exp(-rdist1/$9)
		prob_act2=exp(-rdist2/$13)

		
		
		if ( ref.uniform(0,1) < prob_act1 || ref.uniform(0,1) < prob_act2 ) {
		        weight = synapse_data.x[isynapse][12] 
				} else {
		        weight = 0
				}
			
        } else if ( sectionlist_id == 2 ) {
            cell_ref.apic[sectionlist_index] section = new SectionRef()        
            sectionlist_name = "apical"	
			
		cell_ref.apic[sectionlist_index] nseg_p=nseg
		cell_ref.apic[sectionlist_index] nn = n3d()	
		
		xx_p = new Vector(nn) 
		yy_p = new Vector(nn)
		zz_p = new Vector(nn)
		length_p = new Vector(nn)
 		
		for ii = 0,nn-1 {					
		cell_ref.apic[sectionlist_index] xx_p.x[ii] = x3d(ii) 
		cell_ref.apic[sectionlist_index] yy_p.x[ii] = y3d(ii) 
		cell_ref.apic[sectionlist_index] zz_p.x[ii] = z3d(ii) 
		cell_ref.apic[sectionlist_index] length_p.x[ii] = arc3d(ii) 
		}

		
		length_p.div(length_p.x[nn-1])
        
		range_p = new Vector(nseg_p+2) 
		range_p.indgen(1/nseg_p) 
		range_p.sub(1/(2*nseg_p))
		range_p.x[0]=0
		range_p.x[nseg_p+1]=1

        
		xint_p = new Vector(nseg_p+2) 
		yint_p = new Vector(nseg_p+2)
		zint_p = new Vector(nseg_p+2)
		xint_p.interpolate(range_p, length_p, xx_p)
		yint_p.interpolate(range_p, length_p, yy_p)
		zint_p.interpolate(range_p, length_p, zz_p)									

		xint_p.remove(nseg_p+1)
		xint_p.remove(0)
		yint_p.remove(nseg_p+1)
		yint_p.remove(0)
		zint_p.remove(nseg_p+1)
		zint_p.remove(0)
		
		seg_cen=new Vector(nseg_p)
		
		for jj=1,nseg_p {
		      seg_cen.x[jj-1]=abs(((1/(nseg_p+1))*jj)-seg_x)
			  }
			  
        
		x_syn_real=xint_p.x[seg_cen.min_ind()]
		y_syn_real=yint_p.x[seg_cen.min_ind()]
		z_syn_real=zint_p.x[seg_cen.min_ind()]
		
			pnet_ad_x1=((rlx*cos(rlxang))-(rlz*sin(rlxang)))-(rlx)
            pnet_ad_y1=(rly)-(rly)
            pnet_ad_z1=(((rlx)*sin(rlxang))+((rlz)*cos(rlxang)))-(rlz)
			
			pxrot1=((rlx+x_syn_real)*cos(rlxang))-((rlz+z_syn_real)*sin(rlxang))
	        pyrot1=rly+y_syn_real
            pzrot1=((rlx+x_syn_real)*sin(rlxang))+((rlz+z_syn_real)*cos(rlxang))
	
	        px_fin=pxrot1-pnet_ad_x1
	        py_fin=pyrot1-pnet_ad_y1
	        pz_fin=pzrot1-pnet_ad_z1
		
		rdist1=sqrt(((px_fin-$6)^2)+((py_fin-$7)^2)+((pz_fin-$8)^2))
		rdist2=sqrt(((px_fin-$10)^2)+((py_fin-$11)^2)+((pz_fin-$12)^2))

		prob_act1=exp(-rdist1/$9)
		prob_act2=exp(-rdist2/$13)

		
		
		if ( ref.uniform(0,1) < prob_act1 || ref.uniform(0,1) < prob_act2 ) {
		        weight = synapse_data.x[isynapse][12] 
				} else {
		        weight = 0    
				}
			
        } else if ( sectionlist_id == 3 ) {
            cell_ref.axon[sectionlist_index] section = new SectionRef()        
            sectionlist_name = "axonal" 
			
		cell_ref.axon[sectionlist_index] nseg_p=nseg
		cell_ref.axon[sectionlist_index] nn = n3d()	
		
		xx_p = new Vector(nn) 
		yy_p = new Vector(nn)
		zz_p = new Vector(nn)
		length_p = new Vector(nn)
 		
		for ii = 0,nn-1 {					
		cell_ref.axon[sectionlist_index] xx_p.x[ii] = x3d(ii) 
		cell_ref.axon[sectionlist_index] yy_p.x[ii] = y3d(ii) 
		cell_ref.axon[sectionlist_index] zz_p.x[ii] = z3d(ii) 
		cell_ref.axon[sectionlist_index] length_p.x[ii] = arc3d(ii) 
		}

		
		length_p.div(length_p.x[nn-1])
        
		range_p = new Vector(nseg_p+2) 
		range_p.indgen(1/nseg_p) 
		range_p.sub(1/(2*nseg_p))
		range_p.x[0]=0
		range_p.x[nseg_p+1]=1

        
		xint_p = new Vector(nseg_p+2) 
		yint_p = new Vector(nseg_p+2)
		zint_p = new Vector(nseg_p+2)
		xint_p.interpolate(range_p, length_p, xx_p)
		yint_p.interpolate(range_p, length_p, yy_p)
		zint_p.interpolate(range_p, length_p, zz_p)									

		xint_p.remove(nseg_p+1)
		xint_p.remove(0)
		yint_p.remove(nseg_p+1)
		yint_p.remove(0)
		zint_p.remove(nseg_p+1)
		zint_p.remove(0)
		
		seg_cen=new Vector(nseg_p)
		
		for jj=1,nseg_p {
		      seg_cen.x[jj-1]=abs(((1/(nseg_p+1))*jj)-seg_x)
			  }
			  
        
		x_syn_real=xint_p.x[seg_cen.min_ind()]
		y_syn_real=yint_p.x[seg_cen.min_ind()]
		z_syn_real=zint_p.x[seg_cen.min_ind()]
		
			pnet_ad_x1=((rlx*cos(rlxang))-(rlz*sin(rlxang)))-(rlx)
            pnet_ad_y1=(rly)-(rly)
            pnet_ad_z1=(((rlx)*sin(rlxang))+((rlz)*cos(rlxang)))-(rlz)
			
			pxrot1=((rlx+x_syn_real)*cos(rlxang))-((rlz+z_syn_real)*sin(rlxang))
	        pyrot1=rly+y_syn_real
            pzrot1=((rlx+x_syn_real)*sin(rlxang))+((rlz+z_syn_real)*cos(rlxang))
	
	        px_fin=pxrot1-pnet_ad_x1
	        py_fin=pyrot1-pnet_ad_y1
	        pz_fin=pzrot1-pnet_ad_z1
		
		rdist1=sqrt(((px_fin-$6)^2)+((py_fin-$7)^2)+((pz_fin-$8)^2))
		rdist2=sqrt(((px_fin-$10)^2)+((py_fin-$11)^2)+((pz_fin-$12)^2))

		prob_act1=exp(-rdist1/$9)
		prob_act2=exp(-rdist2/$13)

		
		
		if ( ref.uniform(0,1) < prob_act1 || ref.uniform(0,1) < prob_act2 ) {
		        weight = synapse_data.x[isynapse][12] 
				} else {
		        weight = 0
				}
			
			
        } else {                                                                
            printf("Sectionlist_id %d not support\n", sectionlist_id)           
            exit(1)                                                             
        }

        // If synapse_type < 100 the synapse is inhibitory, otherwise 
        // excitatory
        if ( synapse_type < 100 ) {
            synapse_type_name = "inhibitory"
            section.sec synapse = new ProbGABAAB_EMS(seg_x)
            synapse.tau_d_GABAA  = tau_d
            rng = new Random()                                                      
            rng.MCellRan4( isynapse*100000+100, gid+250+base_seed )                
            rng.lognormal(0.2, 0.1)                                                 
            synapse.tau_r_GABAA = rng.repick()
            pre_mtypes_excinh.x[pre_mtype] = 1                      
        } else {
            synapse_type_name = "excitatory"
            section.sec synapse = new ProbAMPANMDA_EMS(seg_x)
            synapse.tau_d_AMPA = tau_d
            pre_mtypes_excinh.x[pre_mtype] = 0                      
        }

        synapse.Use = abs( use )                                                  
        synapse.Dep = abs( dep )                                                  
        synapse.Fac = abs( fac )   

        // Execute all the extra synaptic configuration lines from synconf.txt
        tmp_synapse = synapse
        for isynconf=0,synconf_list.o(isynapse).count()-1 {
            synconf_string = synconf_list.o(isynapse).o(isynconf).s
            // Replacing all occurrences of %s with the temporary synapse name
            while( stringtools.substr( synconf_string, "%s" ) != -1 ) {
                stringtools.head(synconf_string, "%s", head_string)
                stringtools.tail(synconf_string, "%s", synconf_string)
                sprint(synconf_string, "%s%s%s", head_string, "tmp_synapse", synconf_string)
            }
            // Add {} around the string
            sprint(synconf_string, "{%s}", synconf_string)
            // Execute the statement 
            execute1(synconf_string, this)
        }

        // Create the random number generator for the synapse
        rng = new Random()                                                          
        rng.MCellRan4( isynapse*100000+100, gid+250+base_seed )                    
        rng.uniform(0,1)                                                            
        synapse.setRNG( rng )                                                       
        rng_list.append(rng)        
                                             
        synapse_list.append(synapse)

        // Check if there is already a netstim (spike generator) 
        // for the presynaptic cell
        // If it exists, use that one
        // Otherwise create a new one
        if (pre_cell_ids.contains(pre_cell_id)) {
            netstim_id = pre_cell_ids.indwhere("==", pre_cell_id)
            netstim = netstim_list.o(netstim_id)
        } else {
            section.sec netstim = new NetStim(seg_x)
            netstim.start = 201
            netstim.interval = 500
            netstim.number = 1e20
            netstim.noise = 0
            netstim_list.append(netstim)
            pre_mtype_netstimlists.o(pre_mtype).append(netstim)
            pre_cell_ids.append(pre_cell_id)
        }

        // Create a connection between the netstim and the synapse
        netcon = new NetCon(netstim, synapse)
        netcon.delay = delay
        netcon.weight = 0.0 
        netcon_list.append(netcon)  
        
        // Save all the objects we made to make them persistent        
        pre_mtype_netconlists.o(pre_mtype).append(netcon) 
        pre_mtype_weightlists.o(pre_mtype).append(weight) 
        weights.append(weight) 
        delays.append(delay) 
        pre_mtype_synapselists.o(pre_mtype).append(synapse) 
        pre_mtypes_bitmap.x[pre_mtype] = 1
        
		
		
        printf("Added %s synapse %d originating from cell %d of m-type %s on %s section %d(%f) and dep %f\n", \
           synapse_type_name, synapse_id, pre_cell_id, id_mtype_map.o(pre_mtype).s, sectionlist_name, \
            sectionlist_index, seg_x, dep)
 
    }    

    // Make a list of all the m-types presynaptic to this cell, used by the GUI
    for i=0, n_of_mtypes-1 {
        if (pre_mtypes_bitmap.x[i] == 1) {
            pre_mtypes.append(i)
        }
    } 

}

endtemplate synapses_b030a58a66