Simulation of networks of spiking neurons: A review of tools and strategies.
Romain Brette
(1)
,
Michael Rudolph
(2, 3)
,
Ted Carnevale
(4)
,
Michael Hines
(4)
,
David Beeman
(5)
,
James M Bower
(6)
,
Markus Diesmann
(7, 8)
,
Abigail Morrison
(8)
,
Philip H Goodman
(9)
,
Frederick C Harris
(9)
,
Milind Zirpe
(9)
,
Thomas Natschläger
(10)
,
Dejan Pecevski
(11)
,
Bard Ermentrout
(12)
,
Mikael Djurfeldt
(13)
,
Anders Lansner
(13)
,
Olivier Rochel
(14)
,
Thierry Viéville
(15)
,
Eilif Muller
(16)
,
Andrew P. Davison
(2, 3)
,
Sami El Boustani
(2, 3)
,
Alain Destexhe
(2, 3)
1
DI-ENS -
Département d'informatique - ENS-PSL
2 UNIC - Unité de neurosciences intégratives et computationnelles
3 INAF - Institut de Neurobiologie Alfred Fessard
4 Yale University [New Haven]
5 University of Colorado [Boulder]
6 University of Texas Health Science Center
7 BCF - Bernstein Center Freiburg
8 RIKEN CBS - RIKEN Center for Brain Science [Wako]
9 Brain Laboratory [Reno]
10 SCCH - Software Competence Center Hagenberg
11 TU Graz - Graz University of Technology [Graz]
12 Department of Computer Science - University of Pittsburgh
13 KTH TMH - Department of Speech, Music and Hearing [KTH Stockholm]
14 School of Computing [Leeds]
15 ODYSSEE - Computer and biological vision
16 Kirchhoff Institut für Physik
2 UNIC - Unité de neurosciences intégratives et computationnelles
3 INAF - Institut de Neurobiologie Alfred Fessard
4 Yale University [New Haven]
5 University of Colorado [Boulder]
6 University of Texas Health Science Center
7 BCF - Bernstein Center Freiburg
8 RIKEN CBS - RIKEN Center for Brain Science [Wako]
9 Brain Laboratory [Reno]
10 SCCH - Software Competence Center Hagenberg
11 TU Graz - Graz University of Technology [Graz]
12 Department of Computer Science - University of Pittsburgh
13 KTH TMH - Department of Speech, Music and Hearing [KTH Stockholm]
14 School of Computing [Leeds]
15 ODYSSEE - Computer and biological vision
16 Kirchhoff Institut für Physik
Romain Brette
- Fonction : Auteur
- PersonId : 1191949
- IdHAL : romain-brette
- ORCID : 0000-0003-0110-1623
- IdRef : 089322835
Michael Rudolph
- Fonction : Auteur
- PersonId : 180205
- IdHAL : michael-rudolph
- ORCID : 0000-0002-8540-7808
- IdRef : 184986117
Markus Diesmann
- Fonction : Auteur
- PersonId : 758821
- ORCID : 0000-0002-2308-5727
Olivier Rochel
- Fonction : Auteur
- PersonId : 15643
- IdHAL : olivier-rochel
- ORCID : 0000-0003-1716-3650
- IdRef : 077493230
Thierry Viéville
- Fonction : Auteur
- PersonId : 1252718
- IdHAL : thierry-vieville
- ORCID : 0000-0003-1031-3572
Andrew P. Davison
- Fonction : Auteur
- PersonId : 741237
- IdHAL : andrew-davison
- ORCID : 0000-0002-4793-7541
Résumé
We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.