Event-Based Trajectory Prediction Using Spiking Neural Networks


Journal Article


Debat, G., Chauhan, T., Cottereau, B., Masquelier, T., Paindavoine, M., Baures, R.




Event-Based Trajectory Prediction Using Spiking Neural Networks

Journal / Livre / Conférence

Frontiers in Computational Neurosciences


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.



Liens relatifs

  • https://doi.org/10.3389/fncom.2021.658764

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