Editorial: Understanding and bridging the gap between neuromorphic computing and machine learning, volume II - Deng, Lei; Tang, Huajin; Roy, Kaushik (Frontiers in Computational Neuroscience, 2024)
Two routes have been paved for pursuing intelligence: neuroscience-inspired neuromorphic computing and computer-science-oriented machine learning. Although machine learning technologies, especially the recent large models, are revolutionizing the human life, neuromorphic computing with endorsement from the powerful and efficient brain represents the future. However, current neuromorphic models usually demonstrate lower accuracy compared to "standard" machine learning models, thus limiting their applications in real-world intelligent tasks. To understand and bridge this gap, we continue to organize the Research Topic in Frontiers in Neuroscience and Frontiers in Computational Neuroscience to collect recent advances in neuromorphic computing. We finally accepted 11 submissions, and the scope of these works covers neuromorphic models and algorithms, hardware implementation, and programming frameworks.