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(46):10786-95 [PubMed]
2 . Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM, Diesmann M, Morrison A, et al. (2007) Simulation of networks of spiking neurons: A review of tools and strategies. J Comp Neurosci 23:349-98 [PubMed]
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 A ; Rochel O ; Vieville T ; Muller E ; El Boustani, Sami [elboustani at]; Rudolph M ;

of the NEURON benchmark models for Brette et al (2006) 
Simulation of networks of spiking neurons: 
a review of tools and strategies.

Autolaunching requires version 5.9.30 or higher.
To manually run from the mosinit.hoc file under Linux or Mac OS X,
in the directory containing the mosinit.hoc file run
    nrnivmodl coba cuba cubadv
    nrngui mosinit.hoc
Alternatively, each model can be run from within its subdirectory 
(after linking the model specific mod file). In that case, to view 
the intrinsic cell properties, use
    nrngui intrinsic.hoc
and to run the network simulation use
    nrngui init.hoc
The latter will produce an out.dat file which can be viewed with
    nrngui ../common/spkplt.hoc
Performance information is appended to the perf.dat file.

The network simulations are ready to run, without modification, on 
individual single processor machines, or on parallel cluster machines 
under MPI.


All four benchmark models have the same network structure, so the 
code that sets this up has been factored into a directory called 
common.  The "instrumentation code," which displays simulation 
results and gathers and reports performance information, is also 
shared across the models; it too has been placed in the common 

The models differ with respect to the properties of their constituent 
cells, and how these cells are affected by synaptic inputs.  Cell-
specific code has been factored into directores called coba, cobahh, 
cuba, and cubadv.

Comments have been inserted into the source code at selected points to 
aid those who wish to pursue the implementational strategy more closely.  
Syntax and usage of hoc and NMODL are detailed in the Programmer's 
Reference (
and chapters 5, 6, and 9-13 of The NEURON Book (Carnevale and Hines, 
Cambridge: Cambridge University Press, 2006).


20110805 Ted Carnevale corrected the synaptic time constants for the 
NEURON implemetation of the coba model (see coba/cobacell.hoc).

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