A Brain-Inspired In-Memory Computing System for Neuronal Communication via Memristive Circuits

Abstract

Brain-inspired approaches can efficiently analyze the activities of biological neural networks and solve computationally hard problems with energy efficiencies unattainable with von Neumann architectures, indicating a significant improvement in the understanding of neuronal communications and functionalities. Here, we present a brain-inspired multimodal signal processing system with organic memristor arrays that can potentially integrate the signal sensory, storage, and computation. To facilitate the multimodal signal processing system design, we used four components. First, we present a multimodal signal sensory module mainly responsible for multimodal (iconic, echoic, olfactory, muscular, and gustatory) signal collection, fusion, and storage. Second, a high-density cross-point memristive synapse array is constructed after fabrication of the albumin protein memristor to realize the dense connectivity between layers of computing, data storage, and communication. Third, considering the structure and function of the brain region, we demonstrate a general learning module for hierarchy learning, which can recognize and imagine multimodal information. Finally, the necessary peripheral circuit module (consisting of winnerless competition function circuit, analog-to-digital converter, digital-to-analog converter, pulse modulator, etc.) is designed. Notably, our system can capture massive amounts of data every second and perform in situ processing of multimodal signals. This study is expected to help achieve the deep integration of nano materials into neuromorphic computing systems and energy-efficient integrated circuits.