Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks

Shallow feed-forward networks are incapable of addressing complex tasks such as natural language processing that require learning of temporal signals. To address these requirements, we need deep neuromorphic architectures with recurrent connections such as deep recurrent neural networks. However, th...

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Detalles Bibliográficos
Autores principales: John, Rohit Abraham, Acharya, Jyotibdha, Zhu, Chao, Surendran, Abhijith, Bose, Sumon Kumar, Chaturvedi, Apoorva, Tiwari, Nidhi, Gao, Yang, He, Yongmin, Zhang, Keke K., Xu, Manzhang, Leong, Wei Lin, Liu, Zheng, Basu, Arindam, Mathews, Nripan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7316775/
https://www.ncbi.nlm.nih.gov/pubmed/32587241
http://dx.doi.org/10.1038/s41467-020-16985-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7316775/
https://www.ncbi.nlm.nih.gov/pubmed/32587241
http://dx.doi.org/10.1038/s41467-020-16985-0

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