Abstract The increasing complexity of intelligent sensing environments, driven by the growth of Internet of Things technologies, has created a strong demand for neuromorphic systems capable of real-time, low-power multisensory perception. Traditional sensory architectures, constrained by single-modal processing and centralized computing, struggle to meet the requirements of diverse and dynamic input conditions. Multisensory neuromorphic devices offer a promising solution by mimicking the distributed, event-driven processing of biological systems. Recent efforts have explored synaptic devices and material systems that respond to various input modalities, including visual, tactile, thermal, and chemical stimuli. However, challenges remain in signal conversion, encoding compatibility, and the fusion of heterogeneous inputs without loss of unisensory information. This review provides a comprehensive overview of the physical mechanisms, device behaviors, and integration strategies that underpin signal processing in neuromorphic hardware. We highlight synaptic mechanisms conducive to cross-modal interaction, analyze representative signal fusion approaches at the device level, and discuss future directions for constructing efficient, scalable, and biologically inspired multisensory neuromorphic systems.
Gui et al. (Mon,) studied this question.