Neumann system is a conventional computing architecture with divided processor and memory unit which executes computational tasks sequentially which has served as a pillar of contemporary computing since 1945. However, the frequent data shuffling between the separated processor and memory unit induce massive power consumption and latency which is so-called von Neumann bottleneck. [7,8] The human brain is capable of concurrently executing several complicated tasks with enormous parallelism with extremely low power consumption, outstanding fault tolerance, and remarkable durability owing to its extensive connection, functional organizational hierarchy, advanced learning rules, and neuronal plasticity. Inspiring from it, Mead first pioneered the notion of "neuromorphic computing" in the late 1980s and early 1990s. [9,10] Accordingly, to eliminate inherent constraint of the von Neumann systems, substantial effort has been focused on investigating neuromorphic computing systems. [11][12][13] Biological neuromorphic systems consisting 10 11 neurons interconnected with each other via 10 15 synapses [14,15] is capable to respond to environment and history at different levels from simple molecular (nucleic acids could displays adaptive behaviors including self-repair and replication, under stimuli from the local environment), the elementary information-processing blocks in biological systems (neurons could exhibit more than 20 different dynamic behaviors triggered by historically and environmentally electrochemical stimulation), to whole functional systems with more hierarchical complexity (extremely low or high relative humidity (RH), has a substantial impact on the accuracy of human visual system). [16][17][18] Recently, inspired by human sensory processing and perceptual learning, neuromorphic sensing and computing systems incorporating sensors and machine learning algorithms have been demonstrated to perceive, process, and integrate diverse sensory information where the adaptation and learning can be obtained through dynamically updating the weights of neural network according to the different training algorithms. [19,20] However, on contrary to the distributed processing in biological hierarchical architectures which is more adaptable and cognitive for the optimum analysis of complicated information, the modern computing systems adopting centralized processing are still based on static elements with zeroth-order complexity (e.g., transistor). The essential step for developing neuromorphic systems is to The essential step for developing neuromorphic systems is to construct more biorealistic elementary devices with rich spatiotemporal dynamics to exhibit highly separable responses in dynamic environmental circumstances. Unlike transistor-based devices and circuits with zeroth-order complexity, memristors intrinsically express some simple biomimetic functions. However, with only two-terminal structure, precise control of operation principles to ensure large dynamic space, improved linearity and symmetry, multimodal oper...
