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
• Other Authors: School of Materials Science and Engineering
• Format: Article
• Language: English
• Published: 2021
• Subjects: Engineering::Materials; Engineering::Electrical and electronic engineering; Optogenetics; Signal Noise Ratio
• Online Access: https://hdl.handle.net/10356/152915
• Institution: 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...