Event-Based Trajectory Prediction Using Spiking Neural Networks

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TitreEvent-Based Trajectory Prediction Using Spiking Neural Networks
Type de publicationJournal Article
Year of Publication2021
AuteursDebat G, Chauhan T, Cottereau BR, Masquelier T, Paindavoine M, Baures R
JournalFRONTIERS IN COMPUTATIONAL NEUROSCIENCE
Volume15
Pagination658764
Date PublishedMAY 24
Type of ArticleArticle
Mots-clésball trajectory prediction, motion selectivity, SNN, spiking camera, STDP, Unsupervised learning
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.

DOI10.3389/fncom.2021.658764