Event-Based Trajectory Prediction Using Spiking Neural Networks
Affiliation auteurs | !!!! Error affiliation !!!! |
Titre | Event-Based Trajectory Prediction Using Spiking Neural Networks |
Type de publication | Journal Article |
Year of Publication | 2021 |
Auteurs | Debat G, Chauhan T, Cottereau BR, Masquelier T, Paindavoine M, Baures R |
Journal | FRONTIERS IN COMPUTATIONAL NEUROSCIENCE |
Volume | 15 |
Pagination | 658764 |
Date Published | MAY 24 |
Type of Article | Article |
Mots-clés | ball 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. |
DOI | 10.3389/fncom.2021.658764 |