evt_MNIST: A spike based version of ...
Type de document :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Titre :
evt_MNIST: A spike based version of traditional MNIST an event-based MNIST
Auteur(s) :
Fatahi, Mazdak [Auteur]
Razi University of Kermanshah
Shahsavari, Mahyar [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Ahmadi, Mahmood [Auteur]
Razi University of Kermanshah
Ahmadi, Arash [Auteur]
Razi University of Kermanshah
Devienne, Philippe [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Razi University of Kermanshah
Shahsavari, Mahyar [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Ahmadi, Mahmood [Auteur]
Razi University of Kermanshah
Ahmadi, Arash [Auteur]
Razi University of Kermanshah
Devienne, Philippe [Auteur]

Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Titre de la manifestation scientifique :
1st International Conference on New Research Achievements in Electrical and Computer Engineering
Ville :
Teheran
Pays :
Iran
Date de début de la manifestation scientifique :
2016-05-26
Mot(s)-clé(s) en anglais :
Neuromorphic
spike train
Spiking Neural Networks
AER
Poisson distribution
spike train
Spiking Neural Networks
AER
Poisson distribution
Discipline(s) HAL :
Informatique [cs]/Informatique et langage [cs.CL]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Intelligence artificielle [cs.AI]
Résumé en anglais : [en]
Benchmarks and datasets have important role in evaluation of machine learning algorithms and neural network implementations. Traditional dataset for images such as MNIST is applied to evaluate efficiency of different ...
Lire la suite >Benchmarks and datasets have important role in evaluation of machine learning algorithms and neural network implementations. Traditional dataset for images such as MNIST is applied to evaluate efficiency of different training algorithms in neural networks. This demand is different in Spiking Neural Networks (SNN) as they require spiking inputs. It is widely believed, in the biological cortex the timing of spikes is irregular. Poisson distributions provide adequate descriptions of the irregularity in generating appropriate spikes. Here, we introduce a spike-based version of MNSIT (handwritten digits dataset), using Poisson distribution and show the Poissonian property of the generated streams. We introduce a new version of evt_MNIST which can be used for neural network evaluation.Lire moins >
Lire la suite >Benchmarks and datasets have important role in evaluation of machine learning algorithms and neural network implementations. Traditional dataset for images such as MNIST is applied to evaluate efficiency of different training algorithms in neural networks. This demand is different in Spiking Neural Networks (SNN) as they require spiking inputs. It is widely believed, in the biological cortex the timing of spikes is irregular. Poisson distributions provide adequate descriptions of the irregularity in generating appropriate spikes. Here, we introduce a spike-based version of MNSIT (handwritten digits dataset), using Poisson distribution and show the Poissonian property of the generated streams. We introduce a new version of evt_MNIST which can be used for neural network evaluation.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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- evtMNIST_MazdakFatahi.pdf
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