Neuromorphic computing presents a brain-inspired paradigm shift in artificial intelligence, aiming to replicate the neural architecture and functionalities of the human brain. This approach directly addresses the inherent limitations of conventional Von Neumann architectures, which include a lack of general intelligence, adaptability, and energy efficiency, compounded by their reliance on massive datasets and high power consumption. By leveraging spiking neural networks (SNNs) and novel hardware like memristors, neuromorphic systems enable event-driven processing, context-aware learning, and ultra-low power consumption, fostering cognitive capabilities akin to human intelligence, such as robust pattern recognition and sensory integration. This paper provides a comprehensive overview of neuromorphic computing, examining its foundational principles, architectural components, current advancements across robotics, edge computing, and brain-machine interfaces, and discussing the significant challenges in standardization, hardware variability, and software development. Ultimately, this review highlights the immense promise of neuromorphic computing for advancing artificial general intelligence and bridging the gap between biological and artificial cognitive systems while emphasizing the critical need for responsible development to ensure its societal benefit.
Sam Garg (2025) studied this question.