Advancements in neuromorphic computing for bio-inspired artificial vision: A review
Sharmarke A. Gabayre, Mindula Illeperuma, De Silva, Xiyu Shi, Sergey Savel’ev
Abstract
Neuromorphic computing is revolutionising artificial vision by emulating the human brain’s remarkable efficiency, adaptability, and spatio-temporal processing. This review synthesises recent advances in neuromorphic vision, with a special focus on wave-based dynamics; particularly the role of cortical travelling waves and neural oscillations in coordinating activity across the visual cortex. We examine how these biological mechanisms inspire cutting-edge computational models, including Physics-Informed Neural Networks, reservoir computing, and spiking neural networks, each enabling real-time, energy-efficient visual processing. The review also highlights breakthroughs in hardware, from memristive devices and photonic circuits to optoelectronic polymers, which support in-sensor and event-based processing while dramatically reducing power consumption. By integrating insights from computational neuroscience, materials science, and machine intelligence, we identify persistent challenges; such as scalable training, robust hardware integration, and biologically plausible modelling and outline actionable directions for future research. Our synthesis provides a comprehensive roadmap for the next generation of neuromorphic vision systems, paving the way toward artificial perception that rivals the efficiency and adaptability of biological vision.