0000000001123326
AUTHOR
Timothée Masquelier
26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2
International audience; No abstract available
Event-Based Trajectory Prediction Using Spiking Neural Networks
International audience; 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…