A Memristors-Based Dendritic Neuron for High-Efficiency Spatial-Temporal Information Processing.
basic_science · Level V
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- Record sourced from PubMed, PMID 35735048.
- Also identified by DOI 10.1002/adma.202203684.
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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<sub>x</sub> )-based interface-type dynamic memristor and an niobium oxide (NbO<sub>x</sub> )-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.