As a key building block of biological cortex, neurons are powerful information processing units and can achieve highly complex nonlinear computations even in individual cells. Hardware implementation of artificial neurons with similar capability is of great significance for the construction of intelligent, neuromorphic systems. Here, we demonstrate an artificial neuron based on NbO x volatile memristor that not only realizes traditional all-or-nothing, threshold-driven spiking and spatiotemporal integration, but also enables dynamic logic including XOR function that is not linearly separable and multiplicative gain modulation among different dendritic inputs, therefore surpassing neuronal functions described by a simple point neuron model. A monolithically integrated 4 × 4 fully memristive neural network consisting of volatile NbO x memristor based neurons and nonvolatile TaO x memristor based synapses in a single crossbar array is experimentally demonstrated, showing capability in pattern recognition through online learning using a simplified δ-rule and coincidence detection, which paves the way for bio-inspired intelligent systems.

Neuromorphic perception systems inspired by biology have tremendous potential in efficiently processing multi-sensory signals from the physical world, but a highly efficient hardware element capable of sensing and encoding multiple physical signals is still lacking. Here, we report a spike-based neuromorphic perception system consisting of calibratable artificial sensory neurons based on epitaxial VO2, where the high crystalline quality of VO2 leads to significantly improved cycle-to-cycle uniformity. A calibration resistor is introduced to optimize device-to-device consistency, and to adapt the VO2 neuron to different sensors with varied resistance level, a scaling resistor is further incorporated, demonstrating cross-sensory neuromorphic perception component that can encode illuminance, temperature, pressure and curvature signals into spikes. These components are utilized to monitor the curvatures of fingers, thereby achieving hand gesture classification. This study addresses the fundamental cycle-to-cycle and device-to-device variation issues of sensory neurons, therefore promoting the construction of neuromorphic perception systems for e-skin and neurorobotics.

Memristors proposed by Leon Chua provide a new type of memory device for novel neuromorphic computing applications. However, the approaching of distinct multi‐intermediate states for tunable switching dynamics, the controlling of conducting filaments (CFs) toward high device repeatability and reproducibility, and the ability for large‐scale preparation devices, remain full of challenges. Here, we show that vertical‐organic‐nanocrystal‐arrays (VONAs) could make a way toward the challenges. The perfect one‐dimensional structure of the VONAs could confine the CFs accurately with fine‐tune resistance states in a broad range of 103 ratios. The availability of large‐area VONAs makes the fabrication of large‐area crossbar memristor arrays facilely, and the analog switching characteristic of the memristors is to effectively imitate different kinds of synaptic plasticity, indicating their great potential in future applications.

As the era of big data dawns, conventional digital computers face increasing difficulties in performance and power efficiency due to their von Neumann architecture, leading to an urgent requirement for computing paradigms that can merge logic and memory. An efficient in‐memory logic approach based on unipolar memristors that is capable of implementing all 16 Boolean logic functions in the same cell in less than 3 logic steps using a thermochemical metallization cell, where the confinement of filament diameter and thus switching location have reduced the threshold voltages and improved the switching uniformity, is experimentally demonstrated. The high efficiency of the logic units allows for the construction of novel encryption hardware in which both the encryption and decryption processes are achieved by memristor logic while the encryption key is also generated from the intrinsic stochasticity of resistive switching. The memristive array is also used to implement the calculation of Hamming distance and 1‐bit binary full adder with high efficiency, thus paving a way for future non‐von Neumann computing architectures.