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

 Download zip file   Auto-launch 
Help downloading and running models
Accession:83319
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).
Reference:
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]
Citations  Citation Browser
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;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
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 Yale.edu]; Hines, Michael [Michael.Hines at Yale.edu]; Davison, Andrew [Andrew.Davison at iaf.cnrs-gif.fr]; Destexhe, Alain [Destexhe at iaf.cnrs-gif.fr]; Ermentrout, Bard [bard_at_pitt.edu]; 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 kth.se]; Rochel O ; Vieville T ; Muller E ; El Boustani, Sami [elboustani at unic.cnrs-gif.fr]; Rudolph M ;
/
destexhe_benchmarks
XPP
hh200x50.ode
iaf200x50.ode
                            
# 200 e and 50 I integrate & fire
# random applied current, random conductances
# 
# prevent tables from being recomputed each time params change
@ autoeval=0
table wee % 40000 0 39999 ran(1)/100
table wei % 10000 0 9999  ran(1)/100
table wie % 10000 0 9999  ran(1)/25
table wii % 2500 0 2499  ran(1)/25
# multiply by the synapses
special see=mmult(200,200,wee,se0)
special sei=mmult(200,50,wei,se0)
special sie=mmult(50,200,wie,si0)
special sii=mmult(50,50,wii,si0)
# random currents
table r_e % 200 0 199  ran(1)-.5
table r_i % 50 0 49 ran(1)-.5
# parameters
par tau_e=20,tau_i=20,ele=-65,eli=-65
par vte=-50,vti=-50
par ver=-60,vir=-70
par taue=4,taui=10
# ODEs
ve[0..199]'=(-(ve[j]-ele) + ie0+ie1*r_e([j])-gee*see([j])*(ve[j]-eex)-gie*sie([j])*(ve[j]-ein))/tau_e
vi[0..49]'=(-(vi[j]-eli) + ii0+ii1*r_i([j])-gei*sei([j])*(vi[j]-eex)-gii*sii([j])*(ve[j]-ein))/tau_i
se[0..199]'=-se[j]/taue
si[0..49]'=-si[j]/taui
# each time votlage crosses threshold, reset and set synapse to 1
global 1 ve[0..199]-vte {se[j]=1;ve[j]=ver}
global 1 vi[0..49]-vti {si[j]=1;vi[j]=vir}
# more parameters
par ie0=10,ie1=.5
par ii0=10,ii1=.5
par gee=.02,gie=.05,gii=.02,gei=.02
par eex=0,ein=-75
# initial data
init ve[0..199]=-65
init vi[0..49]=-65
# numerical stuff
@ total=200,meth=euler,nout=10,dt=.01
done