Multisensory integration through high-efficiency neuromorphic hardware

Abstract

Multisensory integration allows biological organisms to merge information from various sensory modalities, enhancing perception, decision-making, and adaptability in complex environments. This process, involving specialized cortical and subcortical areas, reduces uncertainty, speeds up responses, enriches perception, and supports adaptive behaviors. Recent findings reveal that even primary sensory cortices contribute to multisensory processing, further boosting adaptability and decision-making. Inspired by these natural capabilities, researchers aim to develop artificial systems replicating biological sensory integration to address challenges in robotics, artificial intelligence, and big data. Current artificial systems, often reliant on single-modal perception, struggle in dynamic environments due to their limited adaptability. Advances in materials, device architectures, and neuromorphic technologies, such as memristor- and transistor-based neurons, are enabling the development of multimodal systems with enhanced efficiency, flexibility, and functionality. This review explores strategies to overcome single-modal limitations, focusing on synchronization, fusion, and deep interpretation of sensory data. Future directions emphasize improving integration density, novel device designs, and adaptable mechanisms. Multimodal systems hold promise to revolutionize artificial perception, narrowing the gap between biological systems and intelligent technologies.