Local variable time step method (Lytton, Hines 2005)

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Accession:33975
The local variable time-step method utilizes separate variable step integrators for individual neurons in the network. It is most suitable for medium size networks in which average synaptic input intervals to a single cell are much greater than a fixed step dt.
Reference:
1 . Lytton WW, Hines ML (2005) Independent variable time-step integration of individual neurons for network simulations. Neural Comput 17:903-21 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Methods;
Implementer(s): Hines, Michael [Michael.Hines at Yale.edu];
// NetGUI default section. Artificial cells, if any, are located here.
  create acell_home_
  access acell_home_

//Network cell templates
//   C_Cell


begintemplate C_Cell
public is_art
public init, topol, basic_shape, subsets, geom, memb
public synlist, x, y, z, position, connect2target

public soma
public all

objref synlist

proc init() {
  topol()
  subsets()
  geom()
  biophys()
  geom_nseg()
  synlist = new List()
  synapses()
  x = y = z = 0 // only change via position
}

create soma

proc topol() { local i
  basic_shape()
}
proc basic_shape() {
  soma {pt3dclear() pt3dadd(0, 0, 0, 1) pt3dadd(15, 0, 0, 1)}
}

objref all
proc subsets() { local i
  objref all
  all = new SectionList()
    soma all.append()

}
proc geom() {
  soma {  /*area = 100 */ L = diam = 5.6419  }
}
external lambda_f
proc geom_nseg() {
  soma area(.5) // make sure diam reflects 3d points
   soma { nseg = 1  }
}
proc biophys() {
  soma {
    cm = 1
    insert pas
      g_pas = 0.0001
      e_pas = -65
    insert kdr
      gmax_kdr = 0.009
    insert naf
      gmax_naf = 0.035
    insert cur
      amp_cur = 0.001
  }
}
proc position() { local i
  soma 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 target point process, $o2 returned NetCon
  soma $o2 = new NetCon(&v(.5), $o1)
}
objref syn_
proc synapses() {
  /* G0 */   soma syn_ = new GABAA(0.5)  synlist.append(syn_)
}
func is_art() { return 0 }

endtemplate C_Cell

//Network specification interface

objref cells, nclist, netcon, nil
{cells = new List()  nclist = new List()}

func cell_append() {cells.append($o1)  $o1.position($2,$3,$4)
	return cells.count - 1
}

func nc_append() {//srcindex, tarcelindex, synindex
  if ($3 >= 0) {
    cells.object($1).connect2target(cells.object($2).synlist.object($3),netcon)
    netcon.weight = $4   netcon.delay = $5
  }else{
    cells.object($1).connect2target(cells.object($2).pp,netcon)
    netcon.weight = $4   netcon.delay = $5
  }
  nclist.append(netcon)
  netcon = nil
  return nclist.count - 1
}

//Network instantiation

ncell = 100
variable_domain(&ncell, 2, 1e6)

proc makenet() {
	nclist.remove_all
	cells.remove_all
	for i=0, ncell-1 { cell_append(new C_Cell(),	i,	 0, 0) }

    if (1) {
	for i=0, ncell-1 for j=0, ncell-1 if (i != j) {
		nc_append(i, j, 0, 1e-4/(ncell - 1), 0)
	}
    }else{
	for i=0, ncell-1 for j=0, ncell-1 {
		nc_append(i, j, 0, 1e-4/ncell, 0)
	}
    }
	for i=0, nclist.count-1 {
		nclist.object(i).threshold = -5.67 // fitted to wang 1996
	}
	Cdur_GABAA = .4145 // fitted to wang 1996
}
makenet()

Lytton WW, Hines ML (2005) Independent variable time-step integration of individual neurons for network simulations. Neural Comput 17:903-21[PubMed]

References and models cited by this paper

References and models that cite this paper

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