// Trying to open ../diagnostic/tstop.dat
// new end time timtot = 150.
// Trying to open ../diagnostic/dt_F.dat
// new dt = 0.002
/* nRT/nRT_template.hoc
automatically written from f2nrn/neuron_code_writer.f
via subroutines that were inserted into the fortran
code e.g., nRT/integrate_nRT.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 nRT
public type
public name
strdef name
// 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 presynapftic
// compartment number
external traub_connect
objref this, all
create comp[ 59+1]
objref level[ 9+1], Soma, Dendrites
objref Soma_Dendrites, Axon
objref synlist
func type() {return 13 }
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
forsec all if (ismembrane("ar")) { m0_ar = 0 }
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()
name = "nRT"
}
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 "nRT 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[ 60] // note one greater than numcomp due to fortran indicies
// last argument, parent locat_aion for connection
// is overwritten to 1 for parents with connected children
// in below traub_connect proc calls
traub_connect(this, 1, 54, 0.0795492894, 0)
traub_connect(this, 1, 2, 0.037459175, 1)
traub_connect(this, 1, 15, 0.037459175, 1)
traub_connect(this, 1, 28, 0.037459175, 1)
traub_connect(this, 1, 41, 0.037459175, 1)
traub_connect(this, 2, 3, 0.01067208, 1)
traub_connect(this, 2, 4, 0.01067208, 1)
traub_connect(this, 3, 4, 0.00744673185, 1)
traub_connect(this, 3, 5, 0.00421684014, 1)
traub_connect(this, 3, 6, 0.00421684014, 1)
traub_connect(this, 4, 7, 0.00744673185, 1.)
traub_connect(this, 5, 6, 0.00294116184, 1)
traub_connect(this, 5, 8, 0.00294116184, 1.)
traub_connect(this, 6, 9, 0.00294116184, 1.)
traub_connect(this, 7, 10, 0.00744673185, 1.)
traub_connect(this, 8, 11, 0.00294116184, 1.)
traub_connect(this, 11, 12, 0.00294116184, 1.)
traub_connect(this, 12, 13, 0.00294116184, 1.)
traub_connect(this, 13, 14, 0.00294116184, 1.)
traub_connect(this, 15, 16, 0.01067208, 1)
traub_connect(this, 15, 17, 0.01067208, 1)
traub_connect(this, 16, 17, 0.00744673185, 1)
traub_connect(this, 16, 18, 0.00421684014, 1)
traub_connect(this, 16, 19, 0.00421684014, 1)
traub_connect(this, 17, 20, 0.00744673185, 1.)
traub_connect(this, 18, 19, 0.00294116184, 1)
traub_connect(this, 18, 21, 0.00294116184, 1.)
traub_connect(this, 19, 22, 0.00294116184, 1.)
traub_connect(this, 20, 23, 0.00744673185, 1.)
traub_connect(this, 21, 24, 0.00294116184, 1.)
traub_connect(this, 24, 25, 0.00294116184, 1.)
traub_connect(this, 25, 26, 0.00294116184, 1.)
traub_connect(this, 26, 27, 0.00294116184, 1.)
traub_connect(this, 28, 29, 0.01067208, 1)
traub_connect(this, 28, 30, 0.01067208, 1)
traub_connect(this, 29, 30, 0.00744673185, 1)
traub_connect(this, 29, 31, 0.00421684014, 1)
traub_connect(this, 29, 32, 0.00421684014, 1)
traub_connect(this, 30, 33, 0.00744673185, 1.)
traub_connect(this, 31, 32, 0.00294116184, 1)
traub_connect(this, 31, 34, 0.00294116184, 1.)
traub_connect(this, 32, 35, 0.00294116184, 1.)
traub_connect(this, 33, 36, 0.00744673185, 1.)
traub_connect(this, 34, 37, 0.00294116184, 1.)
traub_connect(this, 37, 38, 0.00294116184, 1.)
traub_connect(this, 38, 39, 0.00294116184, 1.)
traub_connect(this, 39, 40, 0.00294116184, 1.)
traub_connect(this, 41, 42, 0.01067208, 1)
traub_connect(this, 41, 43, 0.01067208, 1)
traub_connect(this, 42, 43, 0.00744673185, 1)
traub_connect(this, 42, 44, 0.00421684014, 1)
traub_connect(this, 42, 45, 0.00421684014, 1)
traub_connect(this, 43, 46, 0.00744673185, 1.)
traub_connect(this, 44, 45, 0.00294116184, 1)
traub_connect(this, 44, 47, 0.00294116184, 1.)
traub_connect(this, 45, 48, 0.00294116184, 1.)
traub_connect(this, 46, 49, 0.00744673185, 1.)
traub_connect(this, 47, 50, 0.00294116184, 1.)
traub_connect(this, 50, 51, 0.00294116184, 1.)
traub_connect(this, 51, 52, 0.00294116184, 1.)
traub_connect(this, 52, 53, 0.00294116184, 1.)
traub_connect(this, 54, 55, 0.0348744292, 1.)
traub_connect(this, 55, 56, 0.0208024203, 1)
traub_connect(this, 55, 58, 0.0208024203, 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= 30. diam = 2* 9.34 }
comp[ 2] { level[ 2].append() L= 75. diam = 2* 1.06 }
comp[ 3] { level[ 3].append() L= 75. diam = 2* 0.666666667 }
comp[ 4] { level[ 3].append() L= 75. diam = 2* 0.666666667 }
comp[ 5] { level[ 4].append() L= 75. diam = 2* 0.418972332 }
comp[ 6] { level[ 4].append() L= 75. diam = 2* 0.418972332 }
comp[ 7] { level[ 4].append() L= 75. diam = 2* 0.666666667 }
comp[ 8] { level[ 5].append() L= 75. diam = 2* 0.418972332 }
comp[ 9] { level[ 5].append() L= 75. diam = 2* 0.418972332 }
comp[ 10] { level[ 5].append() L= 75. diam = 2* 0.666666667 }
comp[ 11] { level[ 6].append() L= 75. diam = 2* 0.418972332 }
comp[ 12] { level[ 7].append() L= 75. diam = 2* 0.418972332 }
comp[ 13] { level[ 8].append() L= 75. diam = 2* 0.418972332 }
comp[ 14] { level[ 9].append() L= 75. diam = 2* 0.418972332 }
comp[ 15] { level[ 2].append() L= 75. diam = 2* 1.06 }
comp[ 16] { level[ 3].append() L= 75. diam = 2* 0.666666667 }
comp[ 17] { level[ 3].append() L= 75. diam = 2* 0.666666667 }
comp[ 18] { level[ 4].append() L= 75. diam = 2* 0.418972332 }
comp[ 19] { level[ 4].append() L= 75. diam = 2* 0.418972332 }
comp[ 20] { level[ 4].append() L= 75. diam = 2* 0.666666667 }
comp[ 21] { level[ 5].append() L= 75. diam = 2* 0.418972332 }
comp[ 22] { level[ 5].append() L= 75. diam = 2* 0.418972332 }
comp[ 23] { level[ 5].append() L= 75. diam = 2* 0.666666667 }
comp[ 24] { level[ 6].append() L= 75. diam = 2* 0.418972332 }
comp[ 25] { level[ 7].append() L= 75. diam = 2* 0.418972332 }
comp[ 26] { level[ 8].append() L= 75. diam = 2* 0.418972332 }
comp[ 27] { level[ 9].append() L= 75. diam = 2* 0.418972332 }
comp[ 28] { level[ 2].append() L= 75. diam = 2* 1.06 }
comp[ 29] { level[ 3].append() L= 75. diam = 2* 0.666666667 }
comp[ 30] { level[ 3].append() L= 75. diam = 2* 0.666666667 }
comp[ 31] { level[ 4].append() L= 75. diam = 2* 0.418972332 }
comp[ 32] { level[ 4].append() L= 75. diam = 2* 0.418972332 }
comp[ 33] { level[ 4].append() L= 75. diam = 2* 0.666666667 }
comp[ 34] { level[ 5].append() L= 75. diam = 2* 0.418972332 }
comp[ 35] { level[ 5].append() L= 75. diam = 2* 0.418972332 }
comp[ 36] { level[ 5].append() L= 75. diam = 2* 0.666666667 }
comp[ 37] { level[ 6].append() L= 75. diam = 2* 0.418972332 }
comp[ 38] { level[ 7].append() L= 75. diam = 2* 0.418972332 }
comp[ 39] { level[ 8].append() L= 75. diam = 2* 0.418972332 }
comp[ 40] { level[ 9].append() L= 75. diam = 2* 0.418972332 }
comp[ 41] { level[ 2].append() L= 75. diam = 2* 1.06 }
comp[ 42] { level[ 3].append() L= 75. diam = 2* 0.666666667 }
comp[ 43] { level[ 3].append() L= 75. diam = 2* 0.666666667 }
comp[ 44] { level[ 4].append() L= 75. diam = 2* 0.418972332 }
comp[ 45] { level[ 4].append() L= 75. diam = 2* 0.418972332 }
comp[ 46] { level[ 4].append() L= 75. diam = 2* 0.666666667 }
comp[ 47] { level[ 5].append() L= 75. diam = 2* 0.418972332 }
comp[ 48] { level[ 5].append() L= 75. diam = 2* 0.418972332 }
comp[ 49] { level[ 5].append() L= 75. diam = 2* 0.666666667 }
comp[ 50] { level[ 6].append() L= 75. diam = 2* 0.418972332 }
comp[ 51] { level[ 7].append() L= 75. diam = 2* 0.418972332 }
comp[ 52] { level[ 8].append() L= 75. diam = 2* 0.418972332 }
comp[ 53] { level[ 9].append() L= 75. diam = 2* 0.418972332 }
comp[ 54] { level[ 0].append() L= 50. diam = 2* 0.8 }
comp[ 55] { level[ 0].append() L= 50. diam = 2* 0.7 }
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 napf
gbar_napf = 0.004
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 napf
gbar_napf = 0.0006
insert kdr_fs
gbar_kdr_fs = 0.06
insert kc
gbar_kc = 0.01
insert ka
gbar_ka = 0.005
insert km
gbar_km = 0.0005
insert k2
gbar_k2 = 0.0005
insert kahp_slower
gbar_kahp_slower = 0.0001
insert cal
gbar_cal = 0.0005
insert cat_a
gbar_cat_a = 0.00005
insert ar
gbar_ar = 0.000025
insert cad
// *** ca diffusion: beta=1/tau
beta_cad = 0.02
// cafor(I) (FORTRAN) converted to phi (NEURON)
phi_cad = 10400.
}
forsec level[ 2] {
insert naf2
gbar_naf2 = 0.06
insert napf
gbar_napf = 0.0006
insert kdr_fs
gbar_kdr_fs = 0.06
insert kc
gbar_kc = 0.01
insert ka
gbar_ka = 0.005
insert km
gbar_km = 0.0005
insert k2
gbar_k2 = 0.0005
insert kahp_slower
gbar_kahp_slower = 0.0001
insert cal
gbar_cal = 0.0005
insert cat_a
gbar_cat_a = 0.00005
insert ar
gbar_ar = 0.000025
insert cad
// *** ca diffusion: beta=1/tau
beta_cad = 0.05
// cafor(I) (FORTRAN) converted to phi (NEURON)
phi_cad = 260000.
}
forsec level[ 3] {
insert naf2
gbar_naf2 = 0.06
insert napf
gbar_napf = 0.0006
insert kdr_fs
gbar_kdr_fs = 0.06
insert kc
gbar_kc = 0.01
insert ka
gbar_ka = 0.005
insert km
gbar_km = 0.0005
insert k2
gbar_k2 = 0.0005
insert kahp_slower
gbar_kahp_slower = 0.0001
insert cal
gbar_cal = 0.0005
insert cat_a
gbar_cat_a = 0.00005
insert ar
gbar_ar = 0.000025
insert cad
// *** ca diffusion: beta=1/tau
beta_cad = 0.05
// cafor(I) (FORTRAN) converted to phi (NEURON)
phi_cad = 260000.
}
forsec level[ 4] {
insert naf2
gbar_naf2 = 0.01
insert napf
gbar_napf = 0.0001
insert kdr_fs
gbar_kdr_fs = 0.01
insert kc
gbar_kc = 0.01
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 = 0.0005
insert cat_a
gbar_cat_a = 0.002
insert ar
gbar_ar = 0.000025
insert cad
// *** ca diffusion: beta=1/tau
beta_cad = 0.05
// cafor(I) (FORTRAN) converted to phi (NEURON)
phi_cad = 260000.
}
forsec level[ 5] {
insert naf2
gbar_naf2 = 0.01
insert napf
gbar_napf = 0.0001
insert kdr_fs
gbar_kdr_fs = 0.01
insert kc
gbar_kc = 0.01
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 = 0.0005
insert cat_a
gbar_cat_a = 0.002
insert ar
gbar_ar = 0.000025
insert cad
// *** ca diffusion: beta=1/tau
beta_cad = 0.05
// cafor(I) (FORTRAN) converted to phi (NEURON)
phi_cad = 260000.
}
forsec level[ 6] {
insert naf2
gbar_naf2 = 0.01
insert napf
gbar_napf = 0.0001
insert kdr_fs
gbar_kdr_fs = 0.01
insert kc
gbar_kc = 0.01
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 = 0.0005
insert cat_a
gbar_cat_a = 0.002
insert ar
gbar_ar = 0.000025
insert cad
// *** ca diffusion: beta=1/tau
beta_cad = 0.05
// cafor(I) (FORTRAN) converted to phi (NEURON)
phi_cad = 260000.
}
forsec level[ 7] {
insert naf2
gbar_naf2 = 0.01
insert napf
gbar_napf = 0.0001
insert kdr_fs
gbar_kdr_fs = 0.01
insert kc
gbar_kc = 0.01
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 = 0.0005
insert cat_a
gbar_cat_a = 0.002
insert ar
gbar_ar = 0.000025
insert cad
// *** ca diffusion: beta=1/tau
beta_cad = 0.05
// cafor(I) (FORTRAN) converted to phi (NEURON)
phi_cad = 260000.
}
forsec level[ 8] {
insert naf2
gbar_naf2 = 0.01
insert napf
gbar_napf = 0.0001
insert kdr_fs
gbar_kdr_fs = 0.01
insert kc
gbar_kc = 0.01
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 = 0.0005
insert cat_a
gbar_cat_a = 0.002
insert ar
gbar_ar = 0.000025
insert cad
// *** ca diffusion: beta=1/tau
beta_cad = 0.05
// cafor(I) (FORTRAN) converted to phi (NEURON)
phi_cad = 260000.
}
forsec level[ 9] {
insert naf2
gbar_naf2 = 0.01
insert napf
gbar_napf = 0.0001
insert kdr_fs
gbar_kdr_fs = 0.01
insert kc
gbar_kc = 0.01
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 = 0.0005
insert cat_a
gbar_cat_a = 0.002
insert ar
gbar_ar = 0.000025
insert cad
// *** ca diffusion: beta=1/tau
beta_cad = 0.05
// cafor(I) (FORTRAN) converted to phi (NEURON)
phi_cad = 260000.
}
forsec all {
cm = 1. // assign global specific capac.
}
//
// passive membrane resistance (leak) and axial resistance
//
forsec Soma_Dendrites {
g_pas = 5.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("napf")) { gbar_napf *= spine_area_multiplier }
if (ismembrane("napff")) { gbar_napff *= spine_area_multiplier }
if (ismembrane("napff_tcr")) { gbar_napff_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")) { 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("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_a")) { gbar_cat_a *= spine_area_multiplier }
if (ismembrane("cat_a_a")) { gbar_cat_a_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
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 = -100.
e_pas = -75.
ena = 50.
vca = 125.
forsec all if (ismembrane("ar")) erev_ar = -40.
e_gaba_a = -75.
}
}
proc set_doubler() {doubler=0}
// this function sets doubler to 0
// because there are no spines
// in the cell, replacing earlier fnc.
endtemplate nRT