A single column thalamocortical network model (Traub et al 2005)

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Accession:45539
To better understand population phenomena in thalamocortical neuronal ensembles, we have constructed a preliminary network model with 3,560 multicompartment neurons (containing soma, branching dendrites, and a portion of axon). Types of neurons included superficial pyramids (with regular spiking [RS] and fast rhythmic bursting [FRB] firing behaviors); RS spiny stellates; fast spiking (FS) interneurons, with basket-type and axoaxonic types of connectivity, and located in superficial and deep cortical layers; low threshold spiking (LTS) interneurons, that contacted principal cell dendrites; deep pyramids, that could have RS or intrinsic bursting (IB) firing behaviors, and endowed either with non-tufted apical dendrites or with long tufted apical dendrites; thalamocortical relay (TCR) cells; and nucleus reticularis (nRT) cells. To the extent possible, both electrophysiology and synaptic connectivity were based on published data, although many arbitrary choices were necessary.
References:
1 . Traub RD, Contreras D, Cunningham MO, Murray H, LeBeau FE, Roopun A, Bibbig A, Wilent WB, Higley MJ, Whittington MA (2005) Single-column thalamocortical network model exhibiting gamma oscillations, sleep spindles, and epileptogenic bursts. J Neurophysiol 93:2194-232 [PubMed]
2 . Traub RD, Contreras D, Whittington MA (2005) Combined experimental/simulation studies of cellular and network mechanisms of epileptogenesis in vitro and in vivo. J Clin Neurophysiol 22:330-42 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex; Thalamus;
Cell Type(s): Thalamus geniculate nucleus/lateral principal GLU cell; Thalamus reticular nucleus GABA cell; Neocortex U1 L6 pyramidal corticalthalamic GLU cell; Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex fast spiking (FS) interneuron; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron;
Channel(s): I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I h; I K,Ca; I Calcium; I A, slow;
Gap Junctions: Gap junctions;
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; FORTRAN;
Model Concept(s): Activity Patterns; Bursting; Temporal Pattern Generation; Oscillations; Simplified Models; Epilepsy; Sleep; Spindles;
Implementer(s): Traub, Roger D [rtraub at us.ibm.com];
Search NeuronDB for information about:  Thalamus geniculate nucleus/lateral principal GLU cell; Thalamus reticular nucleus GABA cell; Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex U1 L6 pyramidal corticalthalamic GLU cell; GabaA; AMPA; NMDA; I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I h; I K,Ca; I Calcium; I A, slow;
Files displayed below are from the implementation
/
nrntraub
cells
deepaxax_template.hoc *
deepbask_template.hoc *
deepLTS_template.hoc *
nontuftRS_template.hoc *
nRT_template.hoc *
spinstell_template.hoc *
supaxax_template.hoc *
supbask_template.hoc *
supLTS_template.hoc *
suppyrFRB_template.hoc *
suppyrRS_template.hoc *
TCR_template.hoc *
tuftIB_template.hoc *
tuftRS_template.hoc *
                            
 // Trying to open ../diagnostic/tstop.dat
 // new end time timtot =   150.
 // Trying to open ../diagnostic/dt_F.dat
 // new dt =  0.002
  // i, neigh(i, j=1,10
 //       1   69    2    3    4    5    6    7    8    9   38
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 //      38    1   39   10   11   12   13    0    0    0    0
 //      39   38   40    0    0    0    0    0    0    0    0
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 //      43   41   45   46   47   48    0    0    0    0    0
 //      44   42   49   50   51   52    0    0    0    0    0
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 //      69    1   70    0    0    0    0    0    0    0    0
 //      70   69   71   73    0    0    0    0    0    0    0
 //      71   70   72   73    0    0    0    0    0    0    0
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 //      73   70   71   74    0    0    0    0    0    0    0
 //      74   73    0    0    0    0    0    0    0    0    0
 /* suppyrFRB/suppyrFRB_template.hoc
 automatically written from f2nrn/neuron_code_writer.f
 via subroutines that were inserted into the fortran
 code e.g., suppyrFRB/integrate_suppyrFRB.hoc
 
 The template's form was derived by
 Tom Morse and Michael Hines
 from a template, pyr3_template created
 by Roger Traub and Maciej Lazarewicz when they ported
 
         Traub RD, Buhl EH, Gloveli T, Whittington MA.
 Fast Rhythmic Bursting Can Be Induced in Layer 2/3
 Cortical Neurons by Enhancing Persistent Na(+)
 Conductance or by Blocking BK Channels.J Neurophysiol.
 2003 Feb;89(2):909-21.
 
 to NEURON
 
 */
 
 begintemplate suppyrFRB
	public type
 
 // parts of the template were lifted from a default
 // cell writing from Network Builder NetGUI[0]
 
         public is_art
         public init, topol, basic_shape, subsets
         public geom, biophys 
         public synlist, x, y, z, position
         public connect2target
         public set_netcon_src_comp
         // the above function added to set neton
         // compartment source in the presyn cell
 
         public comp, level, Soma, Dendrites
         public Soma_Dendrites, Axon, all
         public presyn_comp, top_level
         // it is the responsibility of the calling
         // program to set the above presynaptic
         // compartment number
 
         external traub_connect
         objref this
  create  comp[ 74+1]
         objref level[ 12+1], Soma, Dendrites
         objref Soma_Dendrites, Axon
         objref synlist
func type() {return 1 }

         proc init() {
           doubler = 1
  comp[0] delete_section() // clean up for fortran code
            traub_connect( 74+1)
 
            titlePrint()
 
            presyn_comp = 72
            // in Traub model;changed by calling prog.
            objref Soma, Axon, Dendrites, Soma_Dendrites
            objref level
 
            topol()
            shape()
 
            geom()        // the geometry and
            subsets()        // subsets and
            biophys()  // active currents
            synlist = new List() // list of synapses
 // NetGUI[0] stores synapses in the cell object, in 
 // Traub model it is easier to store them outside
            set_doubler() // to double or not
            if (doubler) {double_dend_cond()}
                          /* for taking
  spine membrane area correction into account (the 
  method used doubles max cond's when spines present)
 */
             more_adjustments()
         }
         proc double_dend_cond() {
         /* this function gets replaced later with 
 another one if double_dend_cond() is tacked on. */
         }
 
         proc titlePrint() {
 
 /*              print "
                 print "-----"
                 print "
             print "suppyrFRB Neuron Model based on "
             print "Traub RD et al (2005, 2003)"
                 print "
                 print "-----"
 Remove title printing with this comment for now.  
 Printing otherwise repeats (for each cell)
 -too voluminous for a network creation */
         }
 
         proc set_doubler() {doubler=1}
         // this function gets replaced with one that
         // sets doubler to 0 when there are no spines
         // in the cell (for no spines the additional
         // hoc code is written from integrate_cell.f
         // where cell is nRT, TCR.  Woops I just
         // found that deepaxax, deepbask, deepLTS,
         // supaxax, supbask, supLTS all use the script
         // cell/run_fortran.sh to replace the =1's with
         // =0's.  I will change the fortran code to
         // make it all run_fortran.sh replacements or
         // not for uniformity.
         proc topol() {
 // create comp[ 75] // note one greater than numcomp due to fortran indicies
  // last argument, parent location for connection
  // is overwritten to 1 for parents with connected children 
  // in below traub_connect proc calls
 traub_connect(this,  1,  69,   0.19978229, 0)
 traub_connect(this,  1,  2,   0.012551651, 1)
 traub_connect(this,  1,  3,   0.012551651, 1)
 traub_connect(this,  1,  4,   0.012551651, 1)
 traub_connect(this,  1,  5,   0.012551651, 1)
 traub_connect(this,  1,  6,   0.012551651, 1)
 traub_connect(this,  1,  7,   0.012551651, 1)
 traub_connect(this,  1,  8,   0.012551651, 1)
 traub_connect(this,  1,  9,   0.012551651, 1)
 traub_connect(this,  1,  38,   0.748136781, 1)
 traub_connect(this,  2,  14,   0.00628318,  1.)
 traub_connect(this,  3,  15,   0.00628318,  1.)
 traub_connect(this,  4,  16,   0.00628318,  1.)
 traub_connect(this,  5,  17,   0.00628318,  1.)
 traub_connect(this,  6,  18,   0.00628318,  1.)
 traub_connect(this,  7,  19,   0.00628318,  1.)
 traub_connect(this,  8,  20,   0.00628318,  1.)
 traub_connect(this,  9,  21,   0.00628318,  1.)
 traub_connect(this,  10,  38,   0.0123730314,  1.)
 traub_connect(this,  10,  22,   0.00628318,  1.)
 traub_connect(this,  11,  38,   0.0123730314,  1.)
 traub_connect(this,  11,  23,   0.00628318,  1.)
 traub_connect(this,  12,  38,   0.0123730314,  1.)
 traub_connect(this,  12,  24,   0.00628318,  1.)
 traub_connect(this,  13,  38,   0.0123730314,  1.)
 traub_connect(this,  13,  25,   0.00628318,  1.)
 traub_connect(this,  14,  26,   0.00628318,  1.)
 traub_connect(this,  15,  27,   0.00628318,  1.)
 traub_connect(this,  16,  28,   0.00628318,  1.)
 traub_connect(this,  17,  29,   0.00628318,  1.)
 traub_connect(this,  18,  30,   0.00628318,  1.)
 traub_connect(this,  19,  31,   0.00628318,  1.)
 traub_connect(this,  20,  32,   0.00628318,  1.)
 traub_connect(this,  21,  33,   0.00628318,  1.)
 traub_connect(this,  22,  34,   0.00628318,  1.)
 traub_connect(this,  23,  35,   0.00628318,  1.)
 traub_connect(this,  24,  36,   0.00628318,  1.)
 traub_connect(this,  25,  37,   0.00628318,  1.)
 traub_connect(this,  38,  39,   0.359911659,  1.)
 traub_connect(this,  39,  40,   0.287532183,  1.)
 traub_connect(this,  40,  41,   0.144583738, 1)
 traub_connect(this,  40,  42,   0.144583738, 1)
 traub_connect(this,  41,  42,   0.10053088, 1)
 traub_connect(this,  41,  43,   0.10053088,  1.)
 traub_connect(this,  42,  44,   0.10053088,  1.)
 traub_connect(this,  43,  45,   0.0277326566, 1)
 traub_connect(this,  43,  46,   0.0277326566, 1)
 traub_connect(this,  43,  47,   0.0277326566, 1)
 traub_connect(this,  43,  48,   0.0277326566, 1)
 traub_connect(this,  44,  49,   0.0277326566, 1)
 traub_connect(this,  44,  50,   0.0277326566, 1)
 traub_connect(this,  44,  51,   0.0277326566, 1)
 traub_connect(this,  44,  52,   0.0277326566, 1)
 traub_connect(this,  45,  53,   0.0160849408,  1.)
 traub_connect(this,  45,  46,   0.0160849408, 1)
 traub_connect(this,  45,  47,   0.0160849408, 1)
 traub_connect(this,  45,  48,   0.0160849408, 1)
 traub_connect(this,  46,  54,   0.0160849408,  1.)
 traub_connect(this,  46,  47,   0.0160849408, 1)
 traub_connect(this,  46,  48,   0.0160849408, 1)
 traub_connect(this,  47,  55,   0.0160849408,  1.)
 traub_connect(this,  47,  48,   0.0160849408, 1)
 traub_connect(this,  48,  56,   0.0160849408,  1.)
 traub_connect(this,  49,  57,   0.0160849408,  1.)
 traub_connect(this,  49,  50,   0.0160849408, 1)
 traub_connect(this,  49,  51,   0.0160849408, 1)
 traub_connect(this,  49,  52,   0.0160849408, 1)
 traub_connect(this,  50,  58,   0.0160849408,  1.)
 traub_connect(this,  50,  51,   0.0160849408, 1)
 traub_connect(this,  50,  52,   0.0160849408, 1)
 traub_connect(this,  51,  59,   0.0160849408,  1.)
 traub_connect(this,  51,  52,   0.0160849408, 1)
 traub_connect(this,  52,  60,   0.0160849408,  1.)
 traub_connect(this,  53,  61,   0.0160849408,  1.)
 traub_connect(this,  54,  62,   0.0160849408,  1.)
 traub_connect(this,  55,  63,   0.0160849408,  1.)
 traub_connect(this,  56,  64,   0.0160849408,  1.)
 traub_connect(this,  57,  65,   0.0160849408,  1.)
 traub_connect(this,  58,  66,   0.0160849408,  1.)
 traub_connect(this,  59,  67,   0.0160849408,  1.)
 traub_connect(this,  60,  68,   0.0160849408,  1.)
 traub_connect(this,  69,  70,   0.0472757183,  1.)
 traub_connect(this,  70,  71,   0.0208024203, 1)
 traub_connect(this,  70,  73,   0.0208024203, 1)
 traub_connect(this,  71,  72,   0.01570795,  1.)
 traub_connect(this,  71,  73,   0.01570795, 1)
 traub_connect(this,  73,  74,   0.01570795,  1.)
 access comp[1] // handy statement if want to start gui's from nrnmainmenu
 }
         proc geom() {
 // the "traub level" subsets are created and defined below
 top_level =  12
 objref level[top_level+1]
 for i=0,top_level { level[i] = new SectionList() }
  
 comp[ 1] { level[ 1].append() L=  15. diam = 2*  8. }
 comp[ 2] { level[ 2].append() L=  50. diam = 2*  0.5 }
 comp[ 3] { level[ 2].append() L=  50. diam = 2*  0.5 }
 comp[ 4] { level[ 2].append() L=  50. diam = 2*  0.5 }
 comp[ 5] { level[ 2].append() L=  50. diam = 2*  0.5 }
 comp[ 6] { level[ 2].append() L=  50. diam = 2*  0.5 }
 comp[ 7] { level[ 2].append() L=  50. diam = 2*  0.5 }
 comp[ 8] { level[ 2].append() L=  50. diam = 2*  0.5 }
 comp[ 9] { level[ 2].append() L=  50. diam = 2*  0.5 }
 comp[ 10] { level[ 2].append() L=  50. diam = 2*  0.5 }
 comp[ 11] { level[ 2].append() L=  50. diam = 2*  0.5 }
 comp[ 12] { level[ 2].append() L=  50. diam = 2*  0.5 }
 comp[ 13] { level[ 2].append() L=  50. diam = 2*  0.5 }
 comp[ 14] { level[ 3].append() L=  50. diam = 2*  0.5 }
 comp[ 15] { level[ 3].append() L=  50. diam = 2*  0.5 }
 comp[ 16] { level[ 3].append() L=  50. diam = 2*  0.5 }
 comp[ 17] { level[ 3].append() L=  50. diam = 2*  0.5 }
 comp[ 18] { level[ 3].append() L=  50. diam = 2*  0.5 }
 comp[ 19] { level[ 3].append() L=  50. diam = 2*  0.5 }
 comp[ 20] { level[ 3].append() L=  50. diam = 2*  0.5 }
 comp[ 21] { level[ 3].append() L=  50. diam = 2*  0.5 }
 comp[ 22] { level[ 3].append() L=  50. diam = 2*  0.5 }
 comp[ 23] { level[ 3].append() L=  50. diam = 2*  0.5 }
 comp[ 24] { level[ 3].append() L=  50. diam = 2*  0.5 }
 comp[ 25] { level[ 3].append() L=  50. diam = 2*  0.5 }
 comp[ 26] { level[ 4].append() L=  50. diam = 2*  0.5 }
 comp[ 27] { level[ 4].append() L=  50. diam = 2*  0.5 }
 comp[ 28] { level[ 4].append() L=  50. diam = 2*  0.5 }
 comp[ 29] { level[ 4].append() L=  50. diam = 2*  0.5 }
 comp[ 30] { level[ 4].append() L=  50. diam = 2*  0.5 }
 comp[ 31] { level[ 4].append() L=  50. diam = 2*  0.5 }
 comp[ 32] { level[ 4].append() L=  50. diam = 2*  0.5 }
 comp[ 33] { level[ 4].append() L=  50. diam = 2*  0.5 }
 comp[ 34] { level[ 4].append() L=  50. diam = 2*  0.5 }
 comp[ 35] { level[ 4].append() L=  50. diam = 2*  0.5 }
 comp[ 36] { level[ 4].append() L=  50. diam = 2*  0.5 }
 comp[ 37] { level[ 4].append() L=  50. diam = 2*  0.5 }
 comp[ 38] { level[ 5].append() L=  50. diam = 2*  4. }
 comp[ 39] { level[ 6].append() L=  50. diam = 2*  3.6 }
 comp[ 40] { level[ 7].append() L=  50. diam = 2*  3.2 }
 comp[ 41] { level[ 8].append() L=  50. diam = 2*  2. }
 comp[ 42] { level[ 8].append() L=  50. diam = 2*  2. }
 comp[ 43] { level[ 9].append() L=  50. diam = 2*  2. }
 comp[ 44] { level[ 9].append() L=  50. diam = 2*  2. }
 comp[ 45] { level[ 10].append() L=  50. diam = 2*  0.8 }
 comp[ 46] { level[ 10].append() L=  50. diam = 2*  0.8 }
 comp[ 47] { level[ 10].append() L=  50. diam = 2*  0.8 }
 comp[ 48] { level[ 10].append() L=  50. diam = 2*  0.8 }
 comp[ 49] { level[ 10].append() L=  50. diam = 2*  0.8 }
 comp[ 50] { level[ 10].append() L=  50. diam = 2*  0.8 }
 comp[ 51] { level[ 10].append() L=  50. diam = 2*  0.8 }
 comp[ 52] { level[ 10].append() L=  50. diam = 2*  0.8 }
 comp[ 53] { level[ 11].append() L=  50. diam = 2*  0.8 }
 comp[ 54] { level[ 11].append() L=  50. diam = 2*  0.8 }
 comp[ 55] { level[ 11].append() L=  50. diam = 2*  0.8 }
 comp[ 56] { level[ 11].append() L=  50. diam = 2*  0.8 }
 comp[ 57] { level[ 11].append() L=  50. diam = 2*  0.8 }
 comp[ 58] { level[ 11].append() L=  50. diam = 2*  0.8 }
 comp[ 59] { level[ 11].append() L=  50. diam = 2*  0.8 }
 comp[ 60] { level[ 11].append() L=  50. diam = 2*  0.8 }
 comp[ 61] { level[ 12].append() L=  50. diam = 2*  0.8 }
 comp[ 62] { level[ 12].append() L=  50. diam = 2*  0.8 }
 comp[ 63] { level[ 12].append() L=  50. diam = 2*  0.8 }
 comp[ 64] { level[ 12].append() L=  50. diam = 2*  0.8 }
 comp[ 65] { level[ 12].append() L=  50. diam = 2*  0.8 }
 comp[ 66] { level[ 12].append() L=  50. diam = 2*  0.8 }
 comp[ 67] { level[ 12].append() L=  50. diam = 2*  0.8 }
 comp[ 68] { level[ 12].append() L=  50. diam = 2*  0.8 }
 comp[ 69] { level[ 0].append() L=  25. diam = 2*  0.9 }
 comp[ 70] { level[ 0].append() L=  50. diam = 2*  0.7 }
 comp[ 71] { level[ 0].append() L=  50. diam = 2*  0.5 }
 comp[ 72] { level[ 0].append() L=  50. diam = 2*  0.5 }
 comp[ 73] { level[ 0].append() L=  50. diam = 2*  0.5 }
 comp[ 74] { level[ 0].append() L=  50. diam = 2*  0.5 }
 } 
 // Here are some commonly used subsets of sections
         objref all
         proc subsets() { local i
           objref Soma, Dendrites, Soma_Dendrites, Axon
           objref all
           Soma = new SectionList()
           Dendrites = new SectionList()
           Soma_Dendrites = new SectionList()
           Axon = new SectionList()
           for i=1,top_level {
             forsec level[i] { // recall level 0 is axon, 1 is soma, higher are dends
               Soma_Dendrites.append()
                 if (i>1) {Dendrites.append()}
             }
           }
           forsec level[1] {
             Soma.append()
           }
           forsec level[0] { Axon.append() }
           all = new SectionList()
           for i=1, 74 comp[i] all.append()
          }
 
        proc shape() {
 
 /*      This section could contain statements like
 {pt3dclear() pt3dadd(-1,-1,0,1) pt3dadd(-1,-2,0,1)}
 These visual settings do not effect the electrical
 and chemical systems of equations.              */
 }
         proc biophys() {
 // 
 //       insert the mechanisms and assign max conductances
 // 
 forsec all { insert pas }   // g_pas has two values; soma-dend,axon
 forsec level[ 0] {
       insert naf
       gbar_naf =   0.4
       insert kdr
       gbar_kdr =   0.4
       insert ka
       gbar_ka =   0.002
       insert k2
       gbar_k2 =   0.0001
 }
 forsec level[ 1] {
       insert naf
       gbar_naf =   0.15
       insert nap
       gbar_nap =   0.0006
       insert kdr
       gbar_kdr =   0.1
       insert kc
       gbar_kc =   0.0075
       insert ka
       gbar_ka =   0.03
       insert km
       gbar_km =   0.00375
       insert k2
       gbar_k2 =   0.0001
       insert kahp
       gbar_kahp =   0.0001
       insert cal
       gbar_cal =   0.001
       insert cat
       gbar_cat =   0.0001
       insert ar
       gbar_ar =   0.00025
       insert cad
       // *** ca diffusion: beta=1/tau
       beta_cad  =   0.01
       // cafor(I) (FORTRAN) converted to phi (NEURON)
       phi_cad =   26000.
 }
 forsec level[ 2] {
       insert naf
       gbar_naf =   0.075
       insert nap
       gbar_nap =   0.0003
       insert kdr
       gbar_kdr =   0.075
       insert kc
       gbar_kc =   0.0075
       insert ka
       gbar_ka =   0.002
       insert km
       gbar_km =   0.00375
       insert k2
       gbar_k2 =   0.0001
       insert kahp
       gbar_kahp =   0.0001
       insert cal
       gbar_cal =   0.001
       insert cat
       gbar_cat =   0.0001
       insert ar
       gbar_ar =   0.00025
       insert cad
       // *** ca diffusion: beta=1/tau
       beta_cad  =   0.05
       // cafor(I) (FORTRAN) converted to phi (NEURON)
       phi_cad =   52000.
 }
 forsec level[ 3] {
       insert naf
       gbar_naf =   0.01
       insert nap
       gbar_nap =   4.E-05
       insert kdr
       gbar_kdr =   0.005
       insert kc
       gbar_kc =   0.0075
       insert ka
       gbar_ka =   0.002
       insert km
       gbar_km =   0.00375
       insert k2
       gbar_k2 =   0.0001
       insert kahp
       gbar_kahp =   0.0001
       insert cal
       gbar_cal =   0.001
       insert cat
       gbar_cat =   0.0001
       insert ar
       gbar_ar =   0.00025
       insert cad
       // *** ca diffusion: beta=1/tau
       beta_cad  =   0.05
       // cafor(I) (FORTRAN) converted to phi (NEURON)
       phi_cad =   52000.
 }
 forsec level[ 4] {
       insert naf
       gbar_naf =   0.01
       insert nap
       gbar_nap =   4.E-05
       insert kdr
       gbar_kdr =   0.005
       insert kc
       gbar_kc =   0.0075
       insert ka
       gbar_ka =   0.002
       insert km
       gbar_km =   0.00375
       insert k2
       gbar_k2 =   0.0001
       insert kahp
       gbar_kahp =   0.0001
       insert cal
       gbar_cal =   0.001
       insert cat
       gbar_cat =   0.0001
       insert ar
       gbar_ar =   0.00025
       insert cad
       // *** ca diffusion: beta=1/tau
       beta_cad  =   0.05
       // cafor(I) (FORTRAN) converted to phi (NEURON)
       phi_cad =   52000.
 }
 forsec level[ 5] {
       insert naf
       gbar_naf =   0.1
       insert nap
       gbar_nap =   0.0004
       insert kdr
       gbar_kdr =   0.1
       insert kc
       gbar_kc =   0.0075
       insert ka
       gbar_ka =   0.03
       insert km
       gbar_km =   0.00375
       insert k2
       gbar_k2 =   0.0001
       insert kahp
       gbar_kahp =   0.0001
       insert cal
       gbar_cal =   0.001
       insert cat
       gbar_cat =   0.0001
       insert ar
       gbar_ar =   0.00025
       insert cad
       // *** ca diffusion: beta=1/tau
       beta_cad  =   0.05
       // cafor(I) (FORTRAN) converted to phi (NEURON)
       phi_cad =   52000.
 }
 forsec level[ 6] {
       insert naf
       gbar_naf =   0.075
       insert nap
       gbar_nap =   0.0003
       insert kdr
       gbar_kdr =   0.075
       insert kc
       gbar_kc =   0.0075
       insert ka
       gbar_ka =   0.002
       insert km
       gbar_km =   0.00375
       insert k2
       gbar_k2 =   0.0001
       insert kahp
       gbar_kahp =   0.0001
       insert cal
       gbar_cal =   0.001
       insert cat
       gbar_cat =   0.0001
       insert ar
       gbar_ar =   0.00025
       insert cad
       // *** ca diffusion: beta=1/tau
       beta_cad  =   0.05
       // cafor(I) (FORTRAN) converted to phi (NEURON)
       phi_cad =   52000.
 }
 forsec level[ 7] {
       insert naf
       gbar_naf =   0.01
       insert nap
       gbar_nap =   4.E-05
       insert kdr
       gbar_kdr =   0.005
       insert kc
       gbar_kc =   0.0075
       insert ka
       gbar_ka =   0.002
       insert km
       gbar_km =   0.00375
       insert k2
       gbar_k2 =   0.0001
       insert kahp
       gbar_kahp =   0.0001
       insert cal
       gbar_cal =   0.001
       insert cat
       gbar_cat =   0.0001
       insert ar
       gbar_ar =   0.00025
       insert cad
       // *** ca diffusion: beta=1/tau
       beta_cad  =   0.05
       // cafor(I) (FORTRAN) converted to phi (NEURON)
       phi_cad =   52000.
 }
 forsec level[ 8] {
       insert naf
       gbar_naf =   0.01
       insert nap
       gbar_nap =   4.E-05
       insert kdr
       gbar_kdr =   0.005
       insert kc
       gbar_kc =   0.0075
       insert ka
       gbar_ka =   0.002
       insert km
       gbar_km =   0.00375
       insert k2
       gbar_k2 =   0.0001
       insert kahp
       gbar_kahp =   0.0001
       insert cal
       gbar_cal =   0.001
       insert cat
       gbar_cat =   0.0001
       insert ar
       gbar_ar =   0.00025
       insert cad
       // *** ca diffusion: beta=1/tau
       beta_cad  =   0.05
       // cafor(I) (FORTRAN) converted to phi (NEURON)
       phi_cad =   52000.
 }
 forsec level[ 9] {
       insert naf
       gbar_naf =   0.01
       insert nap
       gbar_nap =   4.E-05
       insert kdr
       gbar_kdr =   0.005
       insert kc
       gbar_kc =   0.0075
       insert ka
       gbar_ka =   0.002
       insert km
       gbar_km =   0.00375
       insert k2
       gbar_k2 =   0.0001
       insert kahp
       gbar_kahp =   0.0001
       insert cal
       gbar_cal =   0.001
       insert cat
       gbar_cat =   0.0001
       insert ar
       gbar_ar =   0.00025
       insert cad
       // *** ca diffusion: beta=1/tau
       beta_cad  =   0.05
       // cafor(I) (FORTRAN) converted to phi (NEURON)
       phi_cad =   52000.
 }
 forsec level[ 10] {
       insert naf
       gbar_naf =   0.01
       insert nap
       gbar_nap =   4.E-05
       insert kdr
       gbar_kdr =   0.005
       insert kc
       gbar_kc =   0.0075
       insert ka
       gbar_ka =   0.002
       insert km
       gbar_km =   0.00375
       insert k2
       gbar_k2 =   0.0001
       insert kahp
       gbar_kahp =   0.0001
       insert cal
       gbar_cal =   0.001
       insert cat
       gbar_cat =   0.0001
       insert ar
       gbar_ar =   0.00025
       insert cad
       // *** ca diffusion: beta=1/tau
       beta_cad  =   0.05
       // cafor(I) (FORTRAN) converted to phi (NEURON)
       phi_cad =   52000.
 }
 forsec level[ 11] {
       insert naf
       gbar_naf =   0.01
       insert nap
       gbar_nap =   4.E-05
       insert kdr
       gbar_kdr =   0.005
       insert kc
       gbar_kc =   0.0075
       insert ka
       gbar_ka =   0.002
       insert km
       gbar_km =   0.00375
       insert k2
       gbar_k2 =   0.0001
       insert kahp
       gbar_kahp =   0.0001
       insert cal
       gbar_cal =   0.001
       insert cat
       gbar_cat =   0.0001
       insert ar
       gbar_ar =   0.00025
       insert cad
       // *** ca diffusion: beta=1/tau
       beta_cad  =   0.05
       // cafor(I) (FORTRAN) converted to phi (NEURON)
       phi_cad =   52000.
 }
 forsec level[ 12] {
       insert naf
       gbar_naf =   0.01
       insert nap
       gbar_nap =   4.E-05
       insert kdr
       gbar_kdr =   0.005
       insert kc
       gbar_kc =   0.0075
       insert ka
       gbar_ka =   0.002
       insert km
       gbar_km =   0.00375
       insert k2
       gbar_k2 =   0.0001
       insert kahp
       gbar_kahp =   0.0001
       insert cal
       gbar_cal =   0.001
       insert cat
       gbar_cat =   0.0001
       insert ar
       gbar_ar =   0.00025
       insert cad
       // *** ca diffusion: beta=1/tau
       beta_cad  =   0.05
       // cafor(I) (FORTRAN) converted to phi (NEURON)
       phi_cad =   52000.
 }
 forsec all {
    cm =   0.9  // assign global specific capac.
 }
 // 
 //  passive membrane resistance (leak) and axial resistance
 // 
 forsec Soma_Dendrites {
    g_pas =   2.E-05
    Ra =   250.
 }
 forsec Axon {
    g_pas =   0.001
    Ra =   100.
 }
 ceiling_cad = 1e6 //  nearly unlimited Ca concentration
 // print "made it to end of initialization from SCORTMAJ_FRB()"
 }  // end of biophys
 
 // Compartment Area: Dendritic.spines double area of
 // dend. membrane, which in Traubs method is equivalent to
 // only multiplying all dend. max conductances by two
 // (the area is doubled but the volume is const.)
 proc double_dend_cond() {
   spine_area_multiplier = 2
   forsec Dendrites {
        if (ismembrane("nap")) { gbar_nap *= spine_area_multiplier }
        if (ismembrane("napf")) { gbar_napf *= spine_area_multiplier }
        if (ismembrane("napf_tcr")) { gbar_napf_tcr *= spine_area_multiplier }
        if (ismembrane("naf")) { gbar_naf *= spine_area_multiplier }
        if (ismembrane("naf_tcr")) { gbar_naf_tcr *= spine_area_multiplier }
        if (ismembrane("naf2")) { gbar_naf2 *= spine_area_multiplier }
        if (ismembrane("kc")) { gbar_kc *= spine_area_multiplier }
        if (ismembrane("kc_fast")) { gbar_kc_fast *= spine_area_multiplier }

        if (ismembrane("kahp")) { gbar_kahp *= spine_area_multiplier }
        if (ismembrane("km")) { gbar_km *= spine_area_multiplier }
        if (ismembrane("kdr")) { gbar_kdr *= spine_area_multiplier }
        if (ismembrane("kdr_fs")) { gbar_kdr_fs *= spine_area_multiplier }
        if (ismembrane("ka")) { gbar_ka *= spine_area_multiplier }
        if (ismembrane("ka_ib")) { gbar_ka_ib *= spine_area_multiplier }
        if (ismembrane("k2")) { gbar_k2 *= spine_area_multiplier }
        if (ismembrane("cal")) { gbar_cal *= spine_area_multiplier }
        if (ismembrane("cat")) { gbar_cat *= spine_area_multiplier }
        if (ismembrane("cat_a")) { gbar_cat_a *= spine_area_multiplier }
        if (ismembrane("ar")) { gbar_ar *= spine_area_multiplier }
        if (ismembrane("pas")) { g_pas *= spine_area_multiplier }
        cm = cm * spine_area_multiplier
   }
 }
 // double_dend_cond()  // run for cells w/ spines
 
 // The below is after doubling of dendritic area to
 // take into account the effect of spines
 // These areas were used in the FORTRAN code to 
 // compute the conductances from specific conductances.
 //  I AREA(I) (compartments and their areas)
 //  1   753.9816
 //  2   314.159
 //  3   314.159
 //  4   314.159
 //  5   314.159
 //  6   314.159
 //  7   314.159
 //  8   314.159
 //  9   314.159
 //  10   314.159
 //  11   314.159
 //  12   314.159
 //  13   314.159
 //  14   314.159
 //  15   314.159
 //  16   314.159
 //  17   314.159
 //  18   314.159
 //  19   314.159
 //  20   314.159
 //  21   314.159
 //  22   314.159
 //  23   314.159
 //  24   314.159
 //  25   314.159
 //  26   314.159
 //  27   314.159
 //  28   314.159
 //  29   314.159
 //  30   314.159
 //  31   314.159
 //  32   314.159
 //  33   314.159
 //  34   314.159
 //  35   314.159
 //  36   314.159
 //  37   314.159
 //  38   2513.272
 //  39   2261.9448
 //  40   2010.6176
 //  41   1256.636
 //  42   1256.636
 //  43   1256.636
 //  44   1256.636
 //  45   502.6544
 //  46   502.6544
 //  47   502.6544
 //  48   502.6544
 //  49   502.6544
 //  50   502.6544
 //  51   502.6544
 //  52   502.6544
 //  53   502.6544
 //  54   502.6544
 //  55   502.6544
 //  56   502.6544
 //  57   502.6544
 //  58   502.6544
 //  59   502.6544
 //  60   502.6544
 //  61   502.6544
 //  62   502.6544
 //  63   502.6544
 //  64   502.6544
 //  65   502.6544
 //  66   502.6544
 //  67   502.6544
 //  68   502.6544
 //  69   141.37155
 //  70   219.9113
 //  71   157.0795
 //  72   157.0795
 //  73   157.0795
 //  74   157.0795
        proc position() { local i
 // comp switched to comp[1] since 0 deleted
         comp[1] for i = 0, n3d()-1 {
     pt3dchange(i, $1-x+x3d(i), \
      $2-y+y3d(i), $3-z+z3d(i),diam3d(i))
        }
         x=$1 y=$2 z=$3
        }
         proc connect2target() { 
  // $o1 targ point process, $o2 returned NetCon
           comp[presyn_comp] $o2 = new NetCon(&v(1),$o1)
	$o2.threshold = 0
         }
         objref syn_
         proc synapses() {
         // place for each compartment that has input
         // statements like 
 //comp[3] syn_=new AlphaSynKinT(1) synlist.append(syn_)
 //comp[4] syn_=new NMDA(1) synlist.append(syn_)
         }
 
 // is not an artificial cell:
      func is_art() { return 0 }
 
 
 
         proc more_adjustments() {
 forsec all {
    // global reversal potentials
    ek =  -95.
    e_pas =  -70.
    ena =   50.
    vca =   125.
    forsec all if (ismembrane("ar")) erev_ar =  -35.
    e_gaba_a =  -81.
 }
 forsec all if (ismembrane("naf")) {fastNa_shift_naf=-3.5} // extended initializations from integrate_suppyrFRB()
 forsec all { if (ismembrane("km")) {gbar_km = 2*gbar_km}}
 forsec Soma_Dendrites {
   gbar_naf = 1.25*gbar_naf
   gbar_kdr = 1.25*gbar_kdr
 }
 forsec all if (ismembrane("nap")) { gbar_nap *=   0.8 }
 forsec all if (ismembrane("kc")) { gbar_kc *=   0.6 }

 forsec all if (ismembrane("km")) { gbar_km *=   1. }
 }
  endtemplate suppyrFRB

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