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

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