An organic artificial synapse for low-energy neuromorphic computing (2017)
- Authors:
- Autor USP: FARIA, GREGORIO COUTO - IFSC
- Unidade: IFSC
- Subjects: CÉREBRO; SINAPSE; REDES NEURAIS; DISPOSITIVOS ELETRÔNICOS
- Language: Inglês
- Imprenta:
- Publisher: Materials Research Society - MRS
- Publisher place: Warrendale
- Date published: 2017
- Source:
- Título: Abstracts
- Conference titles: Materials Research Society Fall Meeting and Exhibit
-
ABNT
BURGT, Yoeri van de et al. An organic artificial synapse for low-energy neuromorphic computing. 2017, Anais.. Warrendale: Materials Research Society - MRS, 2017. Disponível em: https://mrsfall.zerista.com/event/member/429071. Acesso em: 27 dez. 2025. -
APA
Burgt, Y. van de, Lubberman, E., Fuller, E., Keene, S., Faria, G. C., Marinella, M., et al. (2017). An organic artificial synapse for low-energy neuromorphic computing. In Abstracts. Warrendale: Materials Research Society - MRS. Recuperado de https://mrsfall.zerista.com/event/member/429071 -
NLM
Burgt Y van de, Lubberman E, Fuller E, Keene S, Faria GC, Marinella M, Agarwal S, Talin A, Salleo A. An organic artificial synapse for low-energy neuromorphic computing [Internet]. Abstracts. 2017 ;[citado 2025 dez. 27 ] Available from: https://mrsfall.zerista.com/event/member/429071 -
Vancouver
Burgt Y van de, Lubberman E, Fuller E, Keene S, Faria GC, Marinella M, Agarwal S, Talin A, Salleo A. An organic artificial synapse for low-energy neuromorphic computing [Internet]. Abstracts. 2017 ;[citado 2025 dez. 27 ] Available from: https://mrsfall.zerista.com/event/member/429071 - Sistemas de computação baseados no cérebro humano já são realidade: computação neuromórfica permitirá às máquinas aprenderem de maneira análoga ao cérebro humano [Depoimento]
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