Neuromorphic Computing
Neuromorphic computing aims to build energy-efficient computer systems inspired by the brain's architecture, focusing on spiking neural networks (SNNs) that process information via brief electrical pulses. Current research emphasizes improving SNN training methods, particularly addressing challenges in deep network architectures and exploring novel algorithms like surrogate gradients and online learning rules to enhance accuracy and reduce power consumption. This field holds significant promise for advancing artificial intelligence, particularly in resource-constrained applications like robotics and edge computing, by offering substantial improvements in energy efficiency and processing speed compared to traditional computing.
Papers
Neuromorphic Vision-based Motion Segmentation with Graph Transformer Neural Network
Yusra Alkendi, Rana Azzam, Sajid Javed, Lakmal Seneviratne, Yahya Zweiri
Towards free-response paradigm: a theory on decision-making in spiking neural networks
Zhichao Zhu, Yang Qi, Wenlian Lu, Zhigang Wang, Lu Cao, Jianfeng Feng
Oxygen vacancies modulated VO2 for neurons and Spiking Neural Network construction
Liang Li, Ting Zhou, Tong Liu, Zhiwei Liu, Yaping Li, Shuo Wu, Shanguang Zhao, Jinglin Zhu, Meiling Liu, Zhihan Lin, Bowen Sun, Jianjun Li, Fangwen Sun, Chongwen Zou