Spike Timing Mechanisms in Neuromorphic Vision Sensors using Memristor-based non-volatile Memory devices

Spike-timing mechanisms in neuromorphic vision sensors represent a cutting-edge approach to mimicking biological vision systems' efficiency and adaptability. These systems utilize memristor-based non-volatile memory devices to achieve high precision and low power consumption, essential for real-time image processing and recognition tasks. This paper explores the principles and applications of spike-timing-dependent plasticity (STDP) in neuromorphic vision sensors, focusing on the integration of memristor technology. This study introduces an innovative visual-tactile perception system that integrates a scalable, biomimetic tactile sensor called NeuTouch and uses a Visual-Tactile Spiking Neural Network (VT-SNN) for rapid perception. The system demonstrates high accuracy in robotic tasks such as container classification and rotational slip detection, outperforming traditional deep learning methods. The research also contributes to the field by making visual-tactile datasets publicly available to foster further advancements. This work highlights the potential for creating intelligent, energy-efficient robotic systems. We review the latest advancements in memristor-based memory devices, their role in neuromorphic computing, and how they contribute to the development of advanced vision sensors. The potential of these technologies in revolutionizing artificial vision and their implications for future research and development are also discussed.