An integrated toolbox for creating neuromorphic edge applications
spiking neural networks (SNNs) and neuromorphic models are believed to be more efficient in general and have more biological realism than the activation functions typically used in deep neural networks, transformer models and generative AI. SNNs have local learning rules, are able to learn on small...
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| Autori principali: | , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
IOP Publishing
2025-01-01
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| Serie: | Neuromorphic Computing and Engineering |
| Soggetti: | |
| Accesso online: | https://doi.org/10.1088/2634-4386/adad0f |
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