Simulation of networks of spiking neurons:: A review of tools and strategies

被引:514
作者
Brette, Romain
Rudolph, Michelle
Carnevale, Ted
Hines, Michael
Beeman, David
Bower, James M.
Diesmann, Markus
Morrison, Abigail
Goodman, Philip H.
Harris, Frederick C., Jr.
Zirpe, Milind
Natschlaeger, Thomas
Pecevski, Dejan
Ermentrout, Bard
Djurfeldt, Mikael
Lansner, Anders
Rochel, Olivier
Vieville, Thierry
Muller, Eilif
Davison, Andrew P.
El Boustani, Sami
Destexhe, Alain
机构
[1] CNRS, UNIC, F-91190 Gif Sur Yvette, France
[2] Ecole Normale Super, F-75231 Paris, France
[3] Yale Univ, New Haven, CT USA
[4] Univ Colorado, Boulder, CO 80309 USA
[5] Univ Texas San Antonio, San Antonio, TX 78285 USA
[6] Univ Freiburg, Freiburg, Germany
[7] RIKEN, Brain Sci Inst, Wako, Saitama 35101, Japan
[8] Univ Nevada, Reno, NV 89557 USA
[9] Software Competence Ctr Hagenberg, Hagenberg, Austria
[10] Graz Univ Technol, A-8010 Graz, Austria
[11] Univ Pittsburgh, Pittsburgh, PA USA
[12] KTH, Stockholm, Sweden
[13] Univ Leeds, Leeds, W Yorkshire, England
[14] INRIA, Nice, France
[15] Kirchhoff Inst Phys, Heidelberg, Germany
基金
美国国家卫生研究院;
关键词
spiking neural networks; simulation tools; integration strategies; clock-driven; event-driven;
D O I
10.1007/s10827-007-0038-6
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页码:349 / 398
页数:50
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