A Review of Recent Advances in Intelligent Neuromorphic Computing-Assisted Machine Learning for Automatic Anomaly Detection

Intelligent neuromorphic computing has been increasingly used in recent years to improve machine learning approaches for automatically detecting anomalies. This review paper provides a thorough analysis of the progress achieved in this interdisciplinary field in recent years, namely in the areas of machine learning, computer vision, and pattern recognition. The analysis is based on the findings published in high-ranking publications. The combination of neuromorphic computing concepts and machine learning algorithms has enabled the development of innovative methods for detecting anomalies. These methods are known for their effectiveness, flexibility, and capacity to handle data in real-time or almost real-time situations. The main topics examined include the application of spiking neural networks, event-based processing, and bioinspired computing models to address the difficulties presented by intricate data patterns and changing abnormalities. This review further cements the progress made in computational neuroscience, adaptive systems, and edge computing paradigms toward increasing the efficiency and practicality of such intelligent systems. In this paper, the authors consider a full survey of the existing works on the current intelligent neuromorphic computing-driven machine learning for autonomous anomaly detection. This is done by analyzing recent literature, identifying emerging trends, and outlining potential areas for future research.