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

• Main Authors: 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
• 其他作者: School of Materials Science and Engineering
• 格式: Article
• 語言: English
• 出版: 2021
• 主題: Engineering::Materials; Engineering::Electrical and electronic engineering; Optogenetics; Signal Noise Ratio
• 在線閱讀: https://hdl.handle.net/10356/152915
• 機構: Nanyang Technological University

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...