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
 /* supbask/supbask_template.hoc
 automatically written from f2nrn/neuron_code_writer.f
 via subroutines that were inserted into the fortran
 code e.g., supbask/integrate_supbask.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 supbask
	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[ 59+1]
         objref level[ 9+1], Soma, Dendrites
         objref Soma_Dendrites, Axon
         objref synlist
func type() {return   2 }

         proc init() {
           doubler = 1
  comp[0] delete_section() // clean up for fortran code
            traub_connect( 59+1)
 
            titlePrint()
 
            presyn_comp = 59
            // 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 "supbask 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=0}
         // 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[ 60] // 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,  54,   0.0611490233, 0)
 traub_connect(this,  1,  2,   0.0873746044, 1)
 traub_connect(this,  1,  15,   0.0873746044, 1)
 traub_connect(this,  1,  28,   0.0873746044, 1)
 traub_connect(this,  1,  41,   0.0873746044, 1)
 traub_connect(this,  2,  3,   0.0250126876, 1)
 traub_connect(this,  2,  4,   0.0250126876, 1)
 traub_connect(this,  3,  4,   0.0174532778, 1)
 traub_connect(this,  3,  5,   0.00988321907, 1)
 traub_connect(this,  3,  6,   0.00988321907, 1)
 traub_connect(this,  4,  7,   0.0174532778,  1.)
 traub_connect(this,  5,  6,   0.00689334805, 1)
 traub_connect(this,  5,  8,   0.00689334805,  1.)
 traub_connect(this,  6,  9,   0.00689334805,  1.)
 traub_connect(this,  7,  10,   0.0174532778,  1.)
 traub_connect(this,  8,  11,   0.00689334805,  1.)
 traub_connect(this,  11,  12,   0.00689334805,  1.)
 traub_connect(this,  12,  13,   0.00689334805,  1.)
 traub_connect(this,  13,  14,   0.00689334805,  1.)
 traub_connect(this,  15,  16,   0.0250126876, 1)
 traub_connect(this,  15,  17,   0.0250126876, 1)
 traub_connect(this,  16,  17,   0.0174532778, 1)
 traub_connect(this,  16,  18,   0.00988321907, 1)
 traub_connect(this,  16,  19,   0.00988321907, 1)
 traub_connect(this,  17,  20,   0.0174532778,  1.)
 traub_connect(this,  18,  19,   0.00689334805, 1)
 traub_connect(this,  18,  21,   0.00689334805,  1.)
 traub_connect(this,  19,  22,   0.00689334805,  1.)
 traub_connect(this,  20,  23,   0.0174532778,  1.)
 traub_connect(this,  21,  24,   0.00689334805,  1.)
 traub_connect(this,  24,  25,   0.00689334805,  1.)
 traub_connect(this,  25,  26,   0.00689334805,  1.)
 traub_connect(this,  26,  27,   0.00689334805,  1.)
 traub_connect(this,  28,  29,   0.0250126876, 1)
 traub_connect(this,  28,  30,   0.0250126876, 1)
 traub_connect(this,  29,  30,   0.0174532778, 1)
 traub_connect(this,  29,  31,   0.00988321907, 1)
 traub_connect(this,  29,  32,   0.00988321907, 1)
 traub_connect(this,  30,  33,   0.0174532778,  1.)
 traub_connect(this,  31,  32,   0.00689334805, 1)
 traub_connect(this,  31,  34,   0.00689334805,  1.)
 traub_connect(this,  32,  35,   0.00689334805,  1.)
 traub_connect(this,  33,  36,   0.0174532778,  1.)
 traub_connect(this,  34,  37,   0.00689334805,  1.)
 traub_connect(this,  37,  38,   0.00689334805,  1.)
 traub_connect(this,  38,  39,   0.00689334805,  1.)
 traub_connect(this,  39,  40,   0.00689334805,  1.)
 traub_connect(this,  41,  42,   0.0250126876, 1)
 traub_connect(this,  41,  43,   0.0250126876, 1)
 traub_connect(this,  42,  43,   0.0174532778, 1)
 traub_connect(this,  42,  44,   0.00988321907, 1)
 traub_connect(this,  42,  45,   0.00988321907, 1)
 traub_connect(this,  43,  46,   0.0174532778,  1.)
 traub_connect(this,  44,  45,   0.00689334805, 1)
 traub_connect(this,  44,  47,   0.00689334805,  1.)
 traub_connect(this,  45,  48,   0.00689334805,  1.)
 traub_connect(this,  46,  49,   0.0174532778,  1.)
 traub_connect(this,  47,  50,   0.00689334805,  1.)
 traub_connect(this,  50,  51,   0.00689334805,  1.)
 traub_connect(this,  51,  52,   0.00689334805,  1.)
 traub_connect(this,  52,  53,   0.00689334805,  1.)
 traub_connect(this,  54,  55,   0.026078893,  1.)
 traub_connect(this,  55,  56,   0.0185405311, 1)
 traub_connect(this,  55,  58,   0.0185405311, 1)
 traub_connect(this,  56,  57,   0.01570795,  1.)
 traub_connect(this,  56,  58,   0.01570795, 1)
 traub_connect(this,  58,  59,   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 =  9
 objref level[top_level+1]
 for i=0,top_level { level[i] = new SectionList() }
  
 comp[ 1] { level[ 1].append() L=  20. diam = 2*  7.5 }
 comp[ 2] { level[ 2].append() L=  40. diam = 2*  1.06 }
 comp[ 3] { level[ 3].append() L=  40. diam = 2*  0.666666667 }
 comp[ 4] { level[ 3].append() L=  40. diam = 2*  0.666666667 }
 comp[ 5] { level[ 4].append() L=  40. diam = 2*  0.418972332 }
 comp[ 6] { level[ 4].append() L=  40. diam = 2*  0.418972332 }
 comp[ 7] { level[ 4].append() L=  40. diam = 2*  0.666666667 }
 comp[ 8] { level[ 5].append() L=  40. diam = 2*  0.418972332 }
 comp[ 9] { level[ 5].append() L=  40. diam = 2*  0.418972332 }
 comp[ 10] { level[ 5].append() L=  40. diam = 2*  0.666666667 }
 comp[ 11] { level[ 6].append() L=  40. diam = 2*  0.418972332 }
 comp[ 12] { level[ 7].append() L=  40. diam = 2*  0.418972332 }
 comp[ 13] { level[ 8].append() L=  40. diam = 2*  0.418972332 }
 comp[ 14] { level[ 9].append() L=  40. diam = 2*  0.418972332 }
 comp[ 15] { level[ 2].append() L=  40. diam = 2*  1.06 }
 comp[ 16] { level[ 3].append() L=  40. diam = 2*  0.666666667 }
 comp[ 17] { level[ 3].append() L=  40. diam = 2*  0.666666667 }
 comp[ 18] { level[ 4].append() L=  40. diam = 2*  0.418972332 }
 comp[ 19] { level[ 4].append() L=  40. diam = 2*  0.418972332 }
 comp[ 20] { level[ 4].append() L=  40. diam = 2*  0.666666667 }
 comp[ 21] { level[ 5].append() L=  40. diam = 2*  0.418972332 }
 comp[ 22] { level[ 5].append() L=  40. diam = 2*  0.418972332 }
 comp[ 23] { level[ 5].append() L=  40. diam = 2*  0.666666667 }
 comp[ 24] { level[ 6].append() L=  40. diam = 2*  0.418972332 }
 comp[ 25] { level[ 7].append() L=  40. diam = 2*  0.418972332 }
 comp[ 26] { level[ 8].append() L=  40. diam = 2*  0.418972332 }
 comp[ 27] { level[ 9].append() L=  40. diam = 2*  0.418972332 }
 comp[ 28] { level[ 2].append() L=  40. diam = 2*  1.06 }
 comp[ 29] { level[ 3].append() L=  40. diam = 2*  0.666666667 }
 comp[ 30] { level[ 3].append() L=  40. diam = 2*  0.666666667 }
 comp[ 31] { level[ 4].append() L=  40. diam = 2*  0.418972332 }
 comp[ 32] { level[ 4].append() L=  40. diam = 2*  0.418972332 }
 comp[ 33] { level[ 4].append() L=  40. diam = 2*  0.666666667 }
 comp[ 34] { level[ 5].append() L=  40. diam = 2*  0.418972332 }
 comp[ 35] { level[ 5].append() L=  40. diam = 2*  0.418972332 }
 comp[ 36] { level[ 5].append() L=  40. diam = 2*  0.666666667 }
 comp[ 37] { level[ 6].append() L=  40. diam = 2*  0.418972332 }
 comp[ 38] { level[ 7].append() L=  40. diam = 2*  0.418972332 }
 comp[ 39] { level[ 8].append() L=  40. diam = 2*  0.418972332 }
 comp[ 40] { level[ 9].append() L=  40. diam = 2*  0.418972332 }
 comp[ 41] { level[ 2].append() L=  40. diam = 2*  1.06 }
 comp[ 42] { level[ 3].append() L=  40. diam = 2*  0.666666667 }
 comp[ 43] { level[ 3].append() L=  40. diam = 2*  0.666666667 }
 comp[ 44] { level[ 4].append() L=  40. diam = 2*  0.418972332 }
 comp[ 45] { level[ 4].append() L=  40. diam = 2*  0.418972332 }
 comp[ 46] { level[ 4].append() L=  40. diam = 2*  0.666666667 }
 comp[ 47] { level[ 5].append() L=  40. diam = 2*  0.418972332 }
 comp[ 48] { level[ 5].append() L=  40. diam = 2*  0.418972332 }
 comp[ 49] { level[ 5].append() L=  40. diam = 2*  0.666666667 }
 comp[ 50] { level[ 6].append() L=  40. diam = 2*  0.418972332 }
 comp[ 51] { level[ 7].append() L=  40. diam = 2*  0.418972332 }
 comp[ 52] { level[ 8].append() L=  40. diam = 2*  0.418972332 }
 comp[ 53] { level[ 9].append() L=  40. diam = 2*  0.418972332 }
 comp[ 54] { level[ 0].append() L=  50. diam = 2*  0.7 }
 comp[ 55] { level[ 0].append() L=  50. diam = 2*  0.6 }
 comp[ 56] { level[ 0].append() L=  50. diam = 2*  0.5 }
 comp[ 57] { level[ 0].append() L=  50. diam = 2*  0.5 }
 comp[ 58] { level[ 0].append() L=  50. diam = 2*  0.5 }
 comp[ 59] { 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, 59 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 naf2
       gbar_naf2 =   0.4
       insert kdr_fs
       gbar_kdr_fs =   0.4
       insert ka
       gbar_ka =   0.001
       insert k2
       gbar_k2 =   0.0005
 }
 forsec level[ 1] {
       insert naf2
       gbar_naf2 =   0.06
       insert nap
       gbar_nap =   0.0006
       insert kdr_fs
       gbar_kdr_fs =   0.1
       insert kc_fast
       gbar_kc_fast =   0.025
       insert ka
       gbar_ka =   0.001
       insert km
       gbar_km =   0.0005
       insert k2
       gbar_k2 =   0.0005
       insert kahp_slower
       gbar_kahp_slower =   0.0001
       insert cal
       gbar_cal =   0.0001
       insert cat
       gbar_cat =   5.E-05
       insert ar
       gbar_ar =   2.5E-05
       insert cad
       // *** ca diffusion: beta=1/tau
       beta_cad  =   0.02
       // cafor(I) (FORTRAN) converted to phi (NEURON)
       phi_cad =   260000.
 }
 forsec level[ 2] {
       insert naf2
       gbar_naf2 =   0.06
       insert nap
       gbar_nap =   0.0006
       insert kdr_fs
       gbar_kdr_fs =   0.1
       insert kc_fast
       gbar_kc_fast =   0.025
       insert ka
       gbar_ka =   0.001
       insert km
       gbar_km =   0.0005
       insert k2
       gbar_k2 =   0.0005
       insert kahp_slower
       gbar_kahp_slower =   0.0001
       insert cal
       gbar_cal =   0.0001
       insert cat
       gbar_cat =   5.E-05
       insert ar
       gbar_ar =   2.5E-05
       insert cad
       // *** ca diffusion: beta=1/tau
       beta_cad  =   0.05
       // cafor(I) (FORTRAN) converted to phi (NEURON)
       phi_cad =   520000.
 }
 forsec level[ 3] {
       insert naf2
       gbar_naf2 =   0.06
       insert nap
       gbar_nap =   0.0006
       insert kdr_fs
       gbar_kdr_fs =   0.1
       insert kc_fast
       gbar_kc_fast =   0.025
       insert ka
       gbar_ka =   0.001
       insert km
       gbar_km =   0.0005
       insert k2
       gbar_k2 =   0.0005
       insert kahp_slower
       gbar_kahp_slower =   0.0001
       insert cal
       gbar_cal =   0.0001
       insert cat
       gbar_cat =   5.E-05
       insert ar
       gbar_ar =   2.5E-05
       insert cad
       // *** ca diffusion: beta=1/tau
       beta_cad  =   0.05
       // cafor(I) (FORTRAN) converted to phi (NEURON)
       phi_cad =   520000.
 }
 forsec level[ 4] {
       insert naf2
       gbar_naf2 =   0.01
       insert nap
       gbar_nap =   0.0001
       insert kdr_fs
       gbar_kdr_fs =   0.01
       insert kc_fast
       gbar_kc_fast =   0.025
       insert ka
       gbar_ka =   0.001
       insert km
       gbar_km =   0.0005
       insert k2
       gbar_k2 =   0.0005
       insert kahp_slower
       gbar_kahp_slower =   0.0001
       insert cal
       gbar_cal =   0.0002
       insert cat
       gbar_cat =   0.002
       insert ar
       gbar_ar =   2.5E-05
       insert cad
       // *** ca diffusion: beta=1/tau
       beta_cad  =   0.05
       // cafor(I) (FORTRAN) converted to phi (NEURON)
       phi_cad =   520000.
 }
 forsec level[ 5] {
       insert naf2
       gbar_naf2 =   0.01
       insert nap
       gbar_nap =   0.0001
       insert kdr_fs
       gbar_kdr_fs =   0.01
       insert kc_fast
       gbar_kc_fast =   0.025
       insert ka
       gbar_ka =   0.001
       insert km
       gbar_km =   0.0005
       insert k2
       gbar_k2 =   0.0005
       insert kahp_slower
       gbar_kahp_slower =   0.0001
       insert cal
       gbar_cal =   0.0002
       insert cat
       gbar_cat =   0.002
       insert ar
       gbar_ar =   2.5E-05
       insert cad
       // *** ca diffusion: beta=1/tau
       beta_cad  =   0.05
       // cafor(I) (FORTRAN) converted to phi (NEURON)
       phi_cad =   520000.
 }
 forsec level[ 6] {
       insert naf2
       gbar_naf2 =   0.01
       insert nap
       gbar_nap =   0.0001
       insert kdr_fs
       gbar_kdr_fs =   0.01
       insert kc_fast
       gbar_kc_fast =   0.025
       insert ka
       gbar_ka =   0.001
       insert km
       gbar_km =   0.0005
       insert k2
       gbar_k2 =   0.0005
       insert kahp_slower
       gbar_kahp_slower =   0.0001
       insert cal
       gbar_cal =   0.0002
       insert cat
       gbar_cat =   0.002
       insert ar
       gbar_ar =   2.5E-05
       insert cad
       // *** ca diffusion: beta=1/tau
       beta_cad  =   0.05
       // cafor(I) (FORTRAN) converted to phi (NEURON)
       phi_cad =   520000.
 }
 forsec level[ 7] {
       insert naf2
       gbar_naf2 =   0.01
       insert nap
       gbar_nap =   0.0001
       insert kdr_fs
       gbar_kdr_fs =   0.01
       insert kc_fast
       gbar_kc_fast =   0.025
       insert ka
       gbar_ka =   0.001
       insert km
       gbar_km =   0.0005
       insert k2
       gbar_k2 =   0.0005
       insert kahp_slower
       gbar_kahp_slower =   0.0001
       insert cal
       gbar_cal =   0.0002
       insert cat
       gbar_cat =   0.002
       insert ar
       gbar_ar =   2.5E-05
       insert cad
       // *** ca diffusion: beta=1/tau
       beta_cad  =   0.05
       // cafor(I) (FORTRAN) converted to phi (NEURON)
       phi_cad =   520000.
 }
 forsec level[ 8] {
       insert naf2
       gbar_naf2 =   0.01
       insert nap
       gbar_nap =   0.0001
       insert kdr_fs
       gbar_kdr_fs =   0.01
       insert kc_fast
       gbar_kc_fast =   0.025
       insert ka
       gbar_ka =   0.001
       insert km
       gbar_km =   0.0005
       insert k2
       gbar_k2 =   0.0005
       insert kahp_slower
       gbar_kahp_slower =   0.0001
       insert cal
       gbar_cal =   0.0002
       insert cat
       gbar_cat =   0.002
       insert ar
       gbar_ar =   2.5E-05
       insert cad
       // *** ca diffusion: beta=1/tau
       beta_cad  =   0.05
       // cafor(I) (FORTRAN) converted to phi (NEURON)
       phi_cad =   520000.
 }
 forsec level[ 9] {
       insert naf2
       gbar_naf2 =   0.01
       insert nap
       gbar_nap =   0.0001
       insert kdr_fs
       gbar_kdr_fs =   0.01
       insert kc_fast
       gbar_kc_fast =   0.025
       insert ka
       gbar_ka =   0.001
       insert km
       gbar_km =   0.0005
       insert k2
       gbar_k2 =   0.0005
       insert kahp_slower
       gbar_kahp_slower =   0.0001
       insert cal
       gbar_cal =   0.0002
       insert cat
       gbar_cat =   0.002
       insert ar
       gbar_ar =   2.5E-05
       insert cad
       // *** ca diffusion: beta=1/tau
       beta_cad  =   0.05
       // cafor(I) (FORTRAN) converted to phi (NEURON)
       phi_cad =   520000.
 }
 forsec all {
    cm =   1.  // assign global specific capac.
 }
 // 
 //  passive membrane resistance (leak) and axial resistance
 // 
 forsec Soma_Dendrites {
    g_pas =   4.E-05
    Ra =   200.
 }
 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("naf2")) { gbar_naf2 *= spine_area_multiplier }
        if (ismembrane("naf2_tcr")) { gbar_naf2_tcr *= spine_area_multiplier }
        if (ismembrane("naf22")) { gbar_naf22 *= spine_area_multiplier }
        if (ismembrane("kc_fast")) { gbar_kc_fast *= spine_area_multiplier }
        if (ismembrane("kc_fast_fast")) { gbar_kc_fast_fast *= spine_area_multiplier }
        if (ismembrane("kahp")) { gbar_kahp *= spine_area_multiplier }
        if (ismembrane("kahp_slower")) { gbar_kahp_slower *= spine_area_multiplier }
        if (ismembrane("km")) { gbar_km *= spine_area_multiplier }
        if (ismembrane("kdr_fs")) { gbar_kdr_fs *= spine_area_multiplier }
        if (ismembrane("kdr_fs_fs")) { gbar_kdr_fs_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   942.477
 //  2   266.406832
 //  3   167.551467
 //  4   167.551467
 //  5   105.299143
 //  6   105.299143
 //  7   167.551467
 //  8   105.299143
 //  9   105.299143
 //  10   167.551467
 //  11   105.299143
 //  12   105.299143
 //  13   105.299143
 //  14   105.299143
 //  15   266.406832
 //  16   167.551467
 //  17   167.551467
 //  18   105.299143
 //  19   105.299143
 //  20   167.551467
 //  21   105.299143
 //  22   105.299143
 //  23   167.551467
 //  24   105.299143
 //  25   105.299143
 //  26   105.299143
 //  27   105.299143
 //  28   266.406832
 //  29   167.551467
 //  30   167.551467
 //  31   105.299143
 //  32   105.299143
 //  33   167.551467
 //  34   105.299143
 //  35   105.299143
 //  36   167.551467
 //  37   105.299143
 //  38   105.299143
 //  39   105.299143
 //  40   105.299143
 //  41   266.406832
 //  42   167.551467
 //  43   167.551467
 //  44   105.299143
 //  45   105.299143
 //  46   167.551467
 //  47   105.299143
 //  48   105.299143
 //  49   167.551467
 //  50   105.299143
 //  51   105.299143
 //  52   105.299143
 //  53   105.299143
 //  54   219.9113
 //  55   188.4954
 //  56   157.0795
 //  57   157.0795
 //  58   157.0795
 //  59   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 {
        if (ismembrane("naf2")) {fastNa_shift_naf2=-2.5}
    // global reversal potentials
    ek =  -100.
    e_pas =  -65.
    ena =   50.
    vca =   125.
    forsec all if (ismembrane("ar")) erev_ar =  -40.
    e_gaba_a =  -75.
 }
     // more extended initializations
     // Note: the following currents are not
     // present in fast spiking and LTS interneurons
 // Would be slightly more efficient to not include them
     forsec all {
         if (ismembrane("nap")) {gbar_nap = 0.0}
         if (ismembrane("k2")) {gbar_k2 = 0.0}
         if (ismembrane("km")) {gbar_km = 0.0}
         if (ismembrane("kahp")) {gbar_kahp = 0.0}
         if (ismembrane("kahp_slower")) {gbar_kahp_slower = 0.0}
         if (ismembrane("cat")) {gbar_cat = 0.0}
         if (ismembrane("ar")) {gbar_ar = 0.0}
    }
 }
  endtemplate supbask

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