Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task

Spiking Neural Networks (SNNs), known for their potential to enable low energy consumption and computational cost, can bring significant advantages to the realm of embedded machine learning for edge applications. However, input coming from standard digital sensors must be encoded into spike trains b...

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Detalles Bibliográficos
Autores principales: Forno, Evelina, Fra, Vittorio, Pignari, Riccardo, Macii, Enrico, Urgese, Gianvito
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9811205/
https://www.ncbi.nlm.nih.gov/pubmed/36620463
http://dx.doi.org/10.3389/fnins.2022.999029

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9811205/
https://www.ncbi.nlm.nih.gov/pubmed/36620463
http://dx.doi.org/10.3389/fnins.2022.999029

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