A critical survey of STDP in Spiking Neural ...
Type de document :
Communication dans un congrès avec actes
Titre :
A critical survey of STDP in Spiking Neural Networks for Pattern Recognition
Auteur(s) :
Vigneron, Alex [Auteur]
Université de Lille
Martinet, Jean [Auteur]
Université Côte d'Azur [UniCA]
Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis [I3S]
Scalable and Pervasive softwARe and Knowledge Systems [Laboratoire I3S - SPARKS]
Université de Lille
Martinet, Jean [Auteur]
Université Côte d'Azur [UniCA]
Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis [I3S]
Scalable and Pervasive softwARe and Knowledge Systems [Laboratoire I3S - SPARKS]
Titre de la manifestation scientifique :
International Joint Conference on Neural Networks (IJCNN)
Ville :
Glasgow
Pays :
Royaume-Uni
Date de début de la manifestation scientifique :
2020-07-19
Titre de l’ouvrage :
2020 International Joint Conference on Neural Networks (IJCNN)
Mot(s)-clé(s) en anglais :
Spiking Neural Networks
Machine Learning
Artificial Neural Networks
Pattern Recognition
Unsupervised Learning
STDP
Bio-inspiration
Machine Learning
Artificial Neural Networks
Pattern Recognition
Unsupervised Learning
STDP
Bio-inspiration
Discipline(s) HAL :
Informatique [cs]/Apprentissage [cs.LG]
Résumé en anglais : [en]
The bio-inspired concept of Spike-Timing-Dependent Plasticity (STDP) derived from neurobiology is increasingly used in Spiking Neural Networks (SNNs) nowadays. Mostly found in unsupervised learning, though recent work has ...
Lire la suite >The bio-inspired concept of Spike-Timing-Dependent Plasticity (STDP) derived from neurobiology is increasingly used in Spiking Neural Networks (SNNs) nowadays. Mostly found in unsupervised learning, though recent work has shown its usefulness in supervised or reinforced paradigms too, STDP is a key element to understanding SNN architectures' learning process. This review introduces a categorisation of its several variants and discusses their specificities and applications, from a pattern recognition perspective. It gathers a variety of definitions used in machine learning for pattern recognition. It provides relevant information for research communities of various backgrounds looking for an overview of this field.Lire moins >
Lire la suite >The bio-inspired concept of Spike-Timing-Dependent Plasticity (STDP) derived from neurobiology is increasingly used in Spiking Neural Networks (SNNs) nowadays. Mostly found in unsupervised learning, though recent work has shown its usefulness in supervised or reinforced paradigms too, STDP is a key element to understanding SNN architectures' learning process. This review introduces a categorisation of its several variants and discusses their specificities and applications, from a pattern recognition perspective. It gathers a variety of definitions used in machine learning for pattern recognition. It provides relevant information for research communities of various backgrounds looking for an overview of this field.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Projet ANR :
Collections :
Source :
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