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Training Deep Spiking Neural Networks Using Backpropagation
Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a...
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| Publicado no: | Front Neurosci |
|---|---|
| Main Authors: | , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Frontiers Media S.A.
2016
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5099523/ https://ncbi.nlm.nih.gov/pubmed/27877107 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/fnins.2016.00508 |
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