Networks of spiking neurons: a review of tools and strategies (Brette et al. 2007)

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This package provides a series of codes that simulate networks of spiking neurons (excitatory and inhibitory, integrate-and-fire or Hodgkin-Huxley type, current-based or conductance-based synapses; some of them are event-based). The same networks are implemented in different simulators (NEURON, GENESIS, NEST, NCS, CSIM, XPP, SPLIT, MVAspike; there is also a couple of implementations in SciLab and C++). The codes included in this package are benchmark simulations; see the associated review paper (Brette et al. 2007). The main goal is to provide a series of benchmark simulations of networks of spiking neurons, and demonstrate how these are implemented in the different simulators overviewed in the paper. See also details in the enclosed file Appendix2.pdf, which describes these different benchmarks. Some of these benchmarks were based on the Vogels-Abbott model (Vogels TP and Abbott LF 2005).
1 . Vogels TP, Abbott LF (2005) Signal propagation and logic gating in networks of integrate-and-fire neurons. J Neurosci 25:10786-95 [PubMed]
2 . Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM, Diesmann M, Morrison A, Goodman PH, Harris FC, Zirpe M, Natschl├Ąger T, Pecevski D, Ermentrout B, Djurfeldt M, Lansner A, Rochel O, Vieville T, Muller E, Davison AP, El Boustani S, Destexhe A (2007) Simulation of networks of spiking neurons: a review of tools and strategies. J Comput Neurosci 23:349-98 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract integrate-and-fire leaky neuron;
Gap Junctions:
Simulation Environment: NEURON; GENESIS; NEST; C or C++ program; XPP; CSIM; NCS; SPLIT; MVASpike; SciLab; Brian; PyNN; Python;
Model Concept(s): Activity Patterns; Methods;
Implementer(s): Carnevale, Ted [Ted.Carnevale at]; Hines, Michael [Michael.Hines at]; Davison, Andrew [Andrew.Davison at]; Destexhe, Alain [Destexhe at]; Ermentrout, Bard []; Brette R; Bower, James; Beeman, Dave; Diesmann M; Morrison A ; Goodman PH; Harris Jr, FC; Zirpe M ; Natschlager T ; Pecevski D ; Djurfeldt M; Lansner, Anders [ala at]; Rochel O ; Vieville T ; Muller E ; El Boustani, Sami [elboustani at]; Rudolph M ;
//Network cell templates
//   C_Cell

AMPA_INDEX = 0 //* synlist synapse indices
GABA_INDEX = 1 //*

thresh_SpikeOut = -50	// (mV)
refrac_SpikeOut = 5	// (ms)
vrefrac_SpikeOut = -60	// (mV) reset potential
grefrac_SPikeOut = 100	// (uS) clamped at reset

begintemplate CobaCell
public is_art
public init, topol, basic_shape, subsets, geom, biophys, geom_nseg, biophys_inhomo
public synlist, x, y, z, position, connect2target, spkout

public soma
public all

objref synlist, spkout

create soma

proc init() {
  synlist = new List()
  soma spkout = new SpikeOut(.5)
  x = y = z = 0 // only change via position

proc topol() { local i
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() {
  forsec all {  /*area = 20000 */ L = diam = 79.7885  }
external lambda_f
proc geom_nseg() {
//* performance killer:  soma area(.5) // make sure diam reflects 3d points
proc biophys() {
  forsec all {
    cm = 1
    insert pas
      g_pas = 5e-05
      e_pas = -60
proc biophys_inhomo(){}
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
  $o2 = new NetCon(spkout, $o1)
objref syn_
proc synapses() {
  /* E0 */   soma syn_ = new ExpSyn(0.5)  synlist.append(syn_)
//    syn_.tau = 10 // incorrect
    syn_.tau = 5
  /* I1 */   soma syn_ = new ExpSyn(0.5)  synlist.append(syn_)
//    syn_.tau = 20 // incorrect
    syn_.tau = 10
    syn_.e = -80
func is_art() { return 0 }

endtemplate CobaCell