Emerging multimodal memristors for neuromorphic perception applications

Abstract

The integration of sensory information from different modalities, such as touch and vision, is essential for organisms to perform behavioral functions such as decision making, learning, and memory. Artificial implementation of human multi-sensory perception using electronic supports is of great significance for achieving efficient human-machine interaction. Thanks to their structural and functional similarity with biological synapses, memristors are emerging as promising nanodevices for developing artificial neuromorphic perception. Memristive devices can sense multidimensional signals including light, pressure, and sound. Their in-sensor computing architecture represents an ideal platform for efficient multimodal perception. We review recent progress in multimodal memristive technology and its application to neuromorphic perception of complex stimuli carrying visual, olfactory, auditory, and tactile information. We describe and clarify the principles underlying memristors and their mechanism of operation. Finally, we discuss the challenges and prospects associated with this rapidly progressing field of research.