Event-Based Trajectory Prediction Using Spiking Neural Networks - CNRS - Centre national de la recherche scientifique Accéder directement au contenu
Article Dans Une Revue Frontiers in Computational Neuroscience Année : 2021

Event-Based Trajectory Prediction Using Spiking Neural Networks

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.
Fichier principal
Vignette du fichier
Debat et al. - 2021 - Event-Based Trajectory Prediction Using Spiking Neural Networks (1).pdf (3.59 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-03253195 , version 1 (11-11-2021)

Identifiants

Citer

Guillaume Debat, Tushar Chauhan, Benoit Cottereau, Timothée Masquelier, Michel Paindavoine, et al.. Event-Based Trajectory Prediction Using Spiking Neural Networks. Frontiers in Computational Neuroscience, 2021, 15, ⟨10.3389/fncom.2021.658764⟩. ⟨hal-03253195⟩
61 Consultations
41 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More