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 (TiOx )-based interface-type dynamic memristor and an niobium oxide (NbOx )-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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
