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dc.contributor.authorNaveros Arrabal, Francisco es_ES
dc.contributor.authorGarrido Alcázar, Jesús Alberto es_ES
dc.contributor.authorCarrillo Sánchez, Richard Rafael es_ES
dc.contributor.authorRos Vidal, Eduardo es_ES
dc.contributor.authorLuque Sola, Niceto Rafael es_ES
dc.date.accessioned2017-03-09T09:30:39Z
dc.date.available2017-03-09T09:30:39Z
dc.date.issued2017
dc.identifier.citationNaveros, F.; et al. Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks. Frontiers in Neuroinformatics, 11: 7 (2017). [http://hdl.handle.net/10481/45148]es_ES
dc.identifier.issn1662-5196
dc.identifier.urihttp://hdl.handle.net/10481/45148
dc.descriptionThe Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fninf. 2017.00007/full#supplementary-materialen_EN
dc.description.abstractModeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under increasing levels of neural complexity.en_EN
dc.description.sponsorshipThis study was supported by the European Union NR (658479-Spike Control), the Spanish National Grant NEUROPACT (TIN2013-47069-P) and by the Spanish National Grant PhD scholarship (AP2012-0906). We gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan GPUs for current EDLUT development.en_EN
dc.language.isoenges_ES
dc.publisherFrontiers Mediaes_ES
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs 3.0 Licensees_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es_ES
dc.subjectEvent- and time-driven techniquesen_EN
dc.subjectCPUen_EN
dc.subjectGPUen_EN
dc.subjectLook-up tableen_EN
dc.subjectSpiking neural modelsen_EN
dc.subjectBi-fixed-step integration methodsen_EN
dc.titleEvent- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networksen_EN
dc.typeinfo:eu-repo/semantics/articleen_EN
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_EN
dc.identifier.doi10.3389/fninf.2017.00007


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