Self-organization of an inhomogeneous memristive hardware for sequence learning

Autor: Melika Payvand, Filippo Moro, Kumiko Nomura, Thomas Dalgaty, Elisa Vianello, Yoshifumi Nishi, Giacomo Indiveri
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Nature Communications, Vol 13, Iss 1, Pp 1-12 (2022)
Druh dokumentu: article
ISSN: 2041-1723
DOI: 10.1038/s41467-022-33476-6
Popis: One gap between the neuro-inspired computing and its applications lies in the intrinsic variability of the devices. Here, Payvand et al. suggest a technologically plausible co-design of the hardware architecture which takes into account and exploits the physics behind memristors.
Databáze: Directory of Open Access Journals