Fiche publication
Date publication
mai 2021
Journal
Frontiers in computational neuroscience
Auteurs
Membres identifiés du Cancéropôle Est :
Pr PAINDAVOINE Michel
Tous les auteurs :
Debat G, Chauhan T, Cottereau BR, Masquelier T, Paindavoine M, Baures R
Lien Pubmed
Résumé
In recent years, event-based sensors have been combined with spiking neural networks (SNNs) to create a new generation of bio-inspired artificial vision systems. These systems can process spatio-temporal data in real time, and are highly energy efficient. In this study, we used a new hybrid event-based camera in conjunction with a multi-layer spiking neural network trained with a spike-timing-dependent plasticity learning rule. We showed that neurons learn from repeated and correlated spatio-temporal patterns in an unsupervised way and become selective to motion features, such as direction and speed. This motion selectivity can then be used to predict ball trajectory by adding a simple read-out layer composed of polynomial regressions, and trained in a supervised manner. Hence, we show that a SNN receiving inputs from an event-based sensor can extract relevant spatio-temporal patterns to process and predict ball trajectories.
Mots clés
SNN, STDP, ball trajectory prediction, motion selectivity, spiking camera, unsupervised learning
Référence
Front Comput Neurosci. 2021 05 24;15:658764
