A Memristors‐Based Dendritic Neuron for High‐Efficiency Spatial‐Temporal Information Processing

Abstract

Abstract Diverse microscopic ionic dynamics help mediate the ability of a biological neural network to handle complex tasks with low energy consumption. Thus, rich internal ionic dynamics in memristors based on transition metal oxide are expected to provide a unique and useful platform for implementing energy‐efficient neuromorphic computing. To this end, a titanium oxide (TiO x )‐based interface‐type dynamic memristor and an niobium oxide (NbO x )‐based Mott memristor are integrated as an artificial dendrite and spike‐firing soma, respectively, to construct a dendritic neuron unit for realizing high‐efficiency spatial‐temporal information processing. Further, a dendritic neural network is hardware‐implemented for spatial‐temporal information processing to highlight the computational advantages achieved by incorporating dendritic functions in the network. Human motion recognition is demonstrated using the Nanyang Technological University‐Red Green Blue (NTU‐RGB) dataset as a benchmark spatial‐temporal task; it shows a nearly 20% improvement in accuracy for the memristors‐based hardware incorporating dendrites and a 1000× advantage in power efficiency compared to that of the graphics processing unit (GPU). The dendritic neuron developed in this study can be considered a critical building block for implementing more bio‐plausible neural networks that can manage complex spatial‐temporal tasks with high efficiency.