Abstract Senses are crucial for an organism’s survival, and there have been numerous efforts to artificially replicate sensory perception to elicit desired responses to specific stimuli. Recent research is increasingly focused on developing artificial sensory nervous systems based on the unsupervised learning capabilities of artificial neural networks (ANNs) using unstructured data. However, future ANNs, which require precise sensing capabilities in increasingly complex environments, must be capable of processing a large number of signals in real time, ideally from continuous domains. This need for massive data processing is driving the evolution of hardware systems, leading to the development of devices specifically designed for artificial sensory systems (ASSs) at the hardware level. To address this challenge, sensor devices need to not only detect target substances but also enable computational functions by utilizing their inherent material properties. Research in neuromorphic sensors is advancing towards integration with next-generation processing systems based on ANNs, effectively addressing the complex scenarios we aim to identify. This review offers perspectives on human-like sensor computing to address these challenges. It examines the progress in implementing five representative senses at the device level, explores methods for integrating them into systems for ASS, and provides a comprehensive overview of potential applications. In particular, we emphasize approaches to cognitively utilize the discussed devices as artificial sensory neurons and synapses, enabling responses to specific inputs. We aim to offer perspectives for the development of artificial sensory nerve systems in the future.
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