Distributed Simulation with Cellular Automata: Architecture and Applications

Sloot, P.M.A.; Kaandorp, Jaap A.; Hoekstra, Alfons G.; Overeinder, B.J.

doi:10.1007/3-540-47849-3_13

Cited by 8 publications

(6 citation statements)

References 56 publications

(69 reference statements)

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“…The problem of simulating dynamical systems with discrete events is a well established research topic in computer science (Ferscha 1996;Sloot et al 1999;Fujimoto 2000;Zeigler et al 2000) (see also Rochel and Martinez 2003;Mayrhofer et al 2002), with appropriate data structures and algorithms already available to the computational neuroscience community. We start by describing the simple case when synaptic interactions are instantaneous, i.e., when spikes can be produced only at times of incoming spikes (no latency); then we will turn to the most general case.…”

Section: Asynchronous or Event-driven Algorithmsmentioning

confidence: 99%

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

et al. 2007

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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-andfire models, interacting with current-based or conductance-based synapses, using clock-driven or NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript 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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Section: Asynchronous or Event-driven Algorithmsmentioning

confidence: 99%

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

et al. 2007

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“…Several proposals in the literature focus on distributing the active cells between the nodes for CA featuring some cells that may become idle for a number of time steps [22,15,23]. Similarly, the possibility of moving cells from heavily loaded nodes to more unloaded ones has been described in [24,25,26].…”

Section: Previous Approachesmentioning

confidence: 99%

Laser Dynamics Modelling and Simulation: An Application of Dynamic Load Balancing of Parallel Cellular Automata

Guisado

Morales

Guerra

et al. 2010

Studies in Computational Intelligence

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“…[6,7]. Additionally, CA are intrinsic parallel systems, very suitable to be naturally and efficiently implemented in parallel computers to carry out high performance simulations [8,9].…”

Section: Introductionmentioning

confidence: 99%

Computational Simulation of Laser Dynamics as a Cooperative Phenomenon

2005

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The different kinds of behavior exhibited by the system in a laser dynamics simulation using a cellular automata model are analyzed. Three distinct types of behavior have been found: laser constant operation, laser spiking and a complex behavior showing irregular oscillations. In the last case, the power spectrum follows a power law of the type 1/f − with exponent close to = 2. In the laser spiking regime, the dependence of the decay rate of the oscillations is found to be in good agreement with the predictions of the theoretical laser rate equations and the experimental phenomenology. In our model the system components evolve under local rules which reproduce the physics of the laser system at the microscopic level, and the laser properties appear as cooperative emergent phenomena associated to these rules.

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Distributed Simulation with Cellular Automata: Architecture and Applications

Cited by 8 publications

References 56 publications

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

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

Laser Dynamics Modelling and Simulation: An Application of Dynamic Load Balancing of Parallel Cellular Automata

Computational Simulation of Laser Dynamics as a Cooperative Phenomenon

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