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

• المؤلفون الرئيسيون: 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
• مؤلفون آخرون: School of Materials Science and Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2021
• الموضوعات: Engineering::Materials; Engineering::Electrical and electronic engineering; Optogenetics; Signal Noise Ratio
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/152915
• المؤسسة: 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...