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Neuromorphic Hardware Learns to Learn
Hyperparameters and learning algorithms for neuromorphic hardware are usually chosen by hand to suit a particular task. In contrast, networks of neurons in the brain were optimized through extensive evolutionary and developmental processes to work well on a range of computing and learning tasks. Occ...
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| Publicado no: | Front Neurosci |
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| Main Authors: | , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Frontiers Media S.A.
2019
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6536858/ https://ncbi.nlm.nih.gov/pubmed/31178681 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/fnins.2019.00483 |
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