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 ;
// Main file for network of cells with coba (COnductance BAsed) synapses.

{load_file("nrngui.hoc")}  // GUI and runtime libraries
{load_file("cobacell.hoc")}  // defines CobaCell class

// Procedures that set up network architecture and performance reporting.

// Called by create_cells() in common/net.hoc
obfunc newcell() {
	return new CobaCell()

// Create the cells, then connect them.
create_net()  // in common/net.hoc
// Randomized spike trains driving excitatory synapses.
create_stim(run_random_low_start_, AMPA_GMAX)  // in common/netstim.hoc

// A few last items for performance reports, e.g. set up spike time recording, and,
// if in "demo" mode, create graph for raster plots, and panel with Stop button.
finish_setup()  // in common/init.hoc

// Parallel run to tstop.
prun()  // in common/perfrun.hoc

// Only the "master" cpu does this.
if ( == 0) {print "RunTime: ", runtime}

// Up to this point, all CPUs have executed the same code,
// except for taking different branches depending on their value of,
// which ranges from 0 to pc.nhost-1.

// Gather performance statistics from each CPU.

// Only the master ( == 0) returns from pc.runworker().
// All other CPUs ("workers") now wait for messages.

// Send requests to the workers and handle the results they send back.
collect_results()  // in common/init.hoc

// Send all workers a QUIT message; those NEURON processes exit.
// The master waits until all worker output has been transferred to it.

// Only the master executes code beyond this point; all others have exited.

// Times of all spikes, and consolidated performance report.
output_results()  // in common/perfrun.hoc