Literature DB >> 17629781

Simulation of networks of spiking neurons: a review of tools and strategies.

Romain Brette1, Michelle Rudolph, Ted Carnevale, Michael Hines, David Beeman, James M Bower, Markus Diesmann, Abigail Morrison, Philip H Goodman, Frederick C Harris, Milind Zirpe, Thomas Natschläger, Dejan Pecevski, Bard Ermentrout, Mikael Djurfeldt, Anders Lansner, Olivier Rochel, Thierry Vieville, Eilif Muller, Andrew P Davison, Sami El Boustani, Alain Destexhe.   

Abstract

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.

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Year:  2007        PMID: 17629781      PMCID: PMC2638500          DOI: 10.1007/s10827-007-0038-6

Source DB:  PubMed          Journal:  J Comput Neurosci        ISSN: 0929-5313            Impact factor:   1.621


  73 in total

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Authors:  M L Hines; N T Carnevale
Journal:  Neural Comput       Date:  2000-05       Impact factor: 2.026

2.  Efficient and accurate time-stepping schemes for integrate-and-fire neuronal networks.

Authors:  M J Shelley; L Tao
Journal:  J Comput Neurosci       Date:  2001 Sep-Oct       Impact factor: 1.621

3.  Spike-timing-dependent synaptic modification induced by natural spike trains.

Authors:  Robert C Froemke; Yang Dan
Journal:  Nature       Date:  2002-03-28       Impact factor: 49.962

4.  SpikeNET: an event-driven simulation package for modelling large networks of spiking neurons.

Authors:  Arnaud Delorme; Simon J Thorpe
Journal:  Network       Date:  2003-11       Impact factor: 1.273

Review 5.  Rallpacks: a set of benchmarks for neuronal simulators.

Authors:  U S Bhalla; D H Bilitch; J M Bower
Journal:  Trends Neurosci       Date:  1992-11       Impact factor: 13.837

6.  Exact simulation of integrate-and-fire models with exponential currents.

Authors:  Romain Brette
Journal:  Neural Comput       Date:  2007-10       Impact factor: 2.026

7.  An active membrane model of the cerebellar Purkinje cell. I. Simulation of current clamps in slice.

Authors:  E De Schutter; J M Bower
Journal:  J Neurophysiol       Date:  1994-01       Impact factor: 2.714

Review 8.  A model of cortical associative memory based on a horizontal network of connected columns.

Authors:  E Fransén; A Lansner
Journal:  Network       Date:  1998-05       Impact factor: 1.273

Review 9.  On numerical simulations of integrate-and-fire neural networks.

Authors:  D Hansel; G Mato; C Meunier; L Neltner
Journal:  Neural Comput       Date:  1998-02-15       Impact factor: 2.026

10.  Propagating waves in visual cortex: a large-scale model of turtle visual cortex.

Authors:  Zoran Nenadic; Bijoy K Ghosh; Philip Ulinski
Journal:  J Comput Neurosci       Date:  2003 Mar-Apr       Impact factor: 1.621

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  160 in total

1.  Asymptotic model of electrical stimulation of nerve fibers.

Authors:  Jonathan P Cranford; Brian J Kim; Wanda Krassowska Neu
Journal:  Med Biol Eng Comput       Date:  2012-02-21       Impact factor: 2.602

2.  Code generation: a strategy for neural network simulators.

Authors:  Dan F M Goodman
Journal:  Neuroinformatics       Date:  2010-10

3.  iqr: a tool for the construction of multi-level simulations of brain and behaviour.

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Journal:  Neuroinformatics       Date:  2010-06

4.  Spiking neural network simulation: memory-optimal synaptic event scheduling.

Authors:  Robert D Stewart; Kevin N Gurney
Journal:  J Comput Neurosci       Date:  2010-11-03       Impact factor: 1.621

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6.  Automated Sholl analysis of digitized neuronal morphology at multiple scales.

Authors:  Melinda K Kutzing; Christopher G Langhammer; Vincent Luo; Hersh Lakdawala; Bonnie L Firestein
Journal:  J Vis Exp       Date:  2010-11-14       Impact factor: 1.355

7.  Voltage-stepping schemes for the simulation of spiking neural networks.

Authors:  G Zheng; A Tonnelier; D Martinez
Journal:  J Comput Neurosci       Date:  2008-11-26       Impact factor: 1.621

8.  KInNeSS: a modular framework for computational neuroscience.

Authors:  Massimiliano Versace; Heather Ames; Jasmin Léveillé; Bret Fortenberry; Anatoli Gorchetchnikov
Journal:  Neuroinformatics       Date:  2008-08-10

Review 9.  Computational modeling of epilepsy for an experimental neurologist.

Authors:  Abbey B Holt; Theoden I Netoff
Journal:  Exp Neurol       Date:  2012-05-14       Impact factor: 5.330

10.  Population persistence in river networks.

Authors:  Jonathan Sarhad; Robert Carlson; Kurt E Anderson
Journal:  J Math Biol       Date:  2013-07-12       Impact factor: 2.259

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