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.

作为生物皮层的重要组成部分，神经元是强大的信息处理单元，即使在单个细胞中也可以实现高度复杂的非线性计算。具有类似功能的人工神经元的硬件实现对于构建智能神经形态系统具有重要意义。在这里，我们演示了基于NbO _x的人工神经元易失性忆阻器，不仅可以实现传统的全有或全无，阈值驱动的尖峰和时空积分，而且还可以实现包括不可线性分离的XOR功能和不同树突输入之间的乘法增益调制在内的动态逻辑，从而超越了由a所描述的神经元功能简单点神经元模型。实验证明了在单个交叉开关阵列中由基于挥发性NbO _x忆阻器的神经元和基于非易失性TaO _x忆阻器的突触组成的单片集成4×4全忆阻神经网络，显示了使用简化的δ规则和巧合通过在线学习进行模式识别的能力检测，为生物启发的智能系统铺平了道路。