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Event-Based Trajectory Prediction Using Spiking Neural Networks
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-ba...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8180888/ https://www.ncbi.nlm.nih.gov/pubmed/34108870 http://dx.doi.org/10.3389/fncom.2021.658764 |
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author | Debat, Guillaume Chauhan, Tushar Cottereau, Benoit R. Masquelier, Timothée Paindavoine, Michel Baures, Robin |
author_facet | Debat, Guillaume Chauhan, Tushar Cottereau, Benoit R. Masquelier, Timothée Paindavoine, Michel Baures, Robin |
author_sort | Debat, Guillaume |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8180888 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81808882021-06-08 Event-Based Trajectory Prediction Using Spiking Neural Networks Debat, Guillaume Chauhan, Tushar Cottereau, Benoit R. Masquelier, Timothée Paindavoine, Michel Baures, Robin Front Comput Neurosci Neuroscience 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. Frontiers Media S.A. 2021-05-24 /pmc/articles/PMC8180888/ /pubmed/34108870 http://dx.doi.org/10.3389/fncom.2021.658764 Text en Copyright © 2021 Debat, Chauhan, Cottereau, Masquelier, Paindavoine and Baures. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Debat, Guillaume Chauhan, Tushar Cottereau, Benoit R. Masquelier, Timothée Paindavoine, Michel Baures, Robin Event-Based Trajectory Prediction Using Spiking Neural Networks |
title | Event-Based Trajectory Prediction Using Spiking Neural Networks |
title_full | Event-Based Trajectory Prediction Using Spiking Neural Networks |
title_fullStr | Event-Based Trajectory Prediction Using Spiking Neural Networks |
title_full_unstemmed | Event-Based Trajectory Prediction Using Spiking Neural Networks |
title_short | Event-Based Trajectory Prediction Using Spiking Neural Networks |
title_sort | event-based trajectory prediction using spiking neural networks |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8180888/ https://www.ncbi.nlm.nih.gov/pubmed/34108870 http://dx.doi.org/10.3389/fncom.2021.658764 |
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