Neuromorphic Computing in Sensory Systems: A Review

Abstract

Unlike traditional sensor architectures that often generate an excess of redundant data and suffer from high power consumption, neuromorphic sensors offer a streamlined approach, providing energy-efficient data processing by leveraging the mechanisms of spiking neural networks. This work reviews the latest advancements in neuromorphic visual, auditory, gustatory, olfactory, haptic and proprioceptive sensors, drawing parallels with their biological analogs and discussing their integration with neuromorphic computing frameworks. By converging neuroscience, materials science, and microelectronics, neuromorphic sensors potentially enhance human sensory capabilities, promising profound impacts on robotics and artificial intelligence.