AUTHOR=Debat Guillaume , Chauhan Tushar , Cottereau Benoit R. , Masquelier Timothée , Paindavoine Michel , Baures Robin TITLE=Event-Based Trajectory Prediction Using Spiking Neural Networks JOURNAL=Frontiers in Computational Neuroscience VOLUME=Volume 15 - 2021 YEAR=2021 URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2021.658764 DOI=10.3389/fncom.2021.658764 ISSN=1662-5188 ABSTRACT=

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.