Abstract
Tactile perception in artificial systems remains constrained by the von Neumann architecture, where the separation of memory and computation leads to significant latency and energy inefficiency. Neuromorphic engineering provides a biologically inspired alternative by adopting event-driven, spike-based coding, akin to neural signaling in human somatosensory systems. This review systematically examines spike-based neural coding techniques for tactile perception, focusing on three key aspects: encoding strategies, neuromorphic hardware implementations, and decoding methodologies. It compares rate coding and temporal coding in terms of biological plausibility and computational efficiency, particularly in dynamic and high-speed tactile tasks. A range of hardware platforms is evaluated, including oscillator-based encoding circuits, CMOS and memristor-based spiking neurons, and self-powered tactile sensors using triboelectric nanogenerators. On the decoding side, mechanisms such as spike-timing-dependent plasticity and spiking neural networks are analyzed for their potential to support adaptive, online learning in tactile systems. The review emphasizes co-design approaches that integrate sensing, encoding, and processing within a unified framework to achieve system-level efficiency. By bridging advances in functional materials, low-power hardware, and brain-inspired computation, this work outlines a roadmap toward artificial tactile systems with millisecond-level latency, sub-milliwatt power consumption, and high perceptual fidelity. These capabilities are essential for future applications in robotics, prosthetics, and wearable electronics.