Quantized state simulation of spiking neural networks

Grinblat, Guillermo L.; Ahumada, Hernán César; Kofman, Ernesto

doi:10.1177/0037549711399935

Cited by 18 publications

(10 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A system similar to this one was used by Grinblat et al 4 In that work, as VECDEVS was not available, the standard DEVS model was built by programming an ad-hoc automatic model builder, in a similar manner to what is described by Kim and Ziegler. 7 The process of programming the model builder took a couple of days and the compilation of the resulting large scale DEVS model took several minutes.…”

Section: Applications and Examplesmentioning

confidence: 99%

A vectorial DEVS extension for large scale system modeling and parallel simulation

Bergero

Kofman

2014

SIMULATION

Self Cite

View full text Add to dashboard Cite

In this article we introduce an extension to the Discrete Event System (DEVS) formalism called Vectorial DEVS (VECDEVS) that allows to represent large scale systems in a graphic block diagram way. A pure VECDEVS model basically consist in an array of identical classic DEVS models that may differ in their parameters. The interconnection of VECDEVS models with some special classic DEVS models that can handle VECDEVS events allows us to easily represent large systems of arbitrary structure. A noticeable feature of this extension is that VECDEVS models can be easily split for parallel simulation. For that purpose, we developed an algorithm that automatically splits VECDEVS models into an arbitrary number of sub-models for parallel simulation. The implementation of VECDEVS and the partitioning algorithm in a DEVS simulation tool is also described and its usage is illustrated through some application examples.

show abstract

Section: Applications and Examplesmentioning

confidence: 99%

A vectorial DEVS extension for large scale system modeling and parallel simulation

Bergero

Kofman

2014

SIMULATION

Self Cite

View full text Add to dashboard Cite

show abstract

“…The execution time of classical solvers grows quadratically with the event density, whereas that of QSS solvers grows only linearly with the number of events per time unit. This is true even for non-stiff models using non-stiff solvers for their simulation (Grinblat et al, 2012).…”

Section: Quantized State System Simulationmentioning

confidence: 99%

The Complexity Crisis - Using Modeling and Simulation for System Level Analysis and Design

Cellier¹,

Floros²,

Kofman³

2013

Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications

View full text Add to dashboard Cite

This paper discusses the current state of the art of modeling and simulation environments and proposes a set of enhancements that will be necessary for such environments to meet future demands. Modeling and simulation are categorized in accordance with the size of the models to be handled: component level modeling, device level modeling, and system level modeling. It is shown that the requirements that modeling and simulation environments need to satisfy in order to meet the demands of modelers are vastly different at these three levels.

show abstract

“…Spiking neural network (SNN) models, 20 which use spikes of neurons to encode and transfer information, are often used to explore the information process mechanisms. [21][22][23] Therefore we focus on the simulation of an SNN in this paper.…”

Section: Introductionmentioning

confidence: 99%

A novel parallel clock-driven algorithm for simulation of neuronal networks based on virtual synapse

et al. 2020

View full text Add to dashboard Cite

The traditional clock-driven algorithm is very time-consuming when performed on large-scale neuronal networks due to the huge number of synaptic currents computation and low performance of the parallel implementation of the algorithm. We find in this paper that the conductance coefficients of all the synapses coming from the same presynaptic neuron (neuron [Formula: see text] for example) does not need to be computed one by one, rather only one common conductance coefficient needs to be computed for all synapses from this neuron. We then propose an idea of virtual synapse for neuron [Formula: see text] to compute this common conductance coefficient and thereby have [Formula: see text] ([Formula: see text] is the number of neurons in the network) virtual synapses for all presynaptic neurons in the network. Since each common conductance depends on only the spiking activity of the presynaptic neuron [Formula: see text] and is irrelevant of postsynaptic neurons, the computation of the different virtual synapses can be deployed to different computer processing unit efficiently. By introducing a circular data structure for the virtual synapses, we present a novel parallel clock-driven algorithm based on graphics processors for simulation of neuronal networks. It is demonstrated by test results that the proposed algorithm reduces memory and time consumption greatly, and improves the performance of the parallelization for large-scale neuronal network simulations effectively.

show abstract

Quantized state simulation of spiking neural networks

Cited by 18 publications

References 24 publications

A vectorial DEVS extension for large scale system modeling and parallel simulation

A vectorial DEVS extension for large scale system modeling and parallel simulation

The Complexity Crisis - Using Modeling and Simulation for System Level Analysis and Design

A novel parallel clock-driven algorithm for simulation of neuronal networks based on virtual synapse

Contact Info

Product

Resources

About