| Autor: |
Hu L; Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China., Li Z; Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China., Shao J; Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China., Cheng P; Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China.; School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo 315211, China., Wang J; Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China.; School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo 315211, China., Vasilakos AV; Center for AI Research, University of Agder, Grimstad 4879, Norway., Zhang L; Healthcare Engineering Centre, School of Engineering, Temasek Polytechnic, Tampines Avenue, 529757, Singapore., Chai Y; Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong 999077, China., Ye Z; Institute of Wenzhou, Zhejiang University, Wenzhou 325006, China.; State Key Laboratory of Silicon and Advanced Semiconductor Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou 310027, China., Zhuge F; Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China.; Institute of Wenzhou, Zhejiang University, Wenzhou 325006, China.; Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200072, China. |
| Abstrakt: |
Threshold switching (TS) memristors are promising candidates for artificial neurons in neuromorphic systems. However, they often lack biological plausibility, typically functioning solely in an excitation mode. The absence of an inhibitory mode limits neurons' ability to synergistically process both excitatory and inhibitory synaptic signals. To address this limitation, we propose a novel memristive neuron capable of operating in both excitation and inhibition modes. The memristor's threshold voltage can be reversibly tuned using voltages of different polarities because of its bipolar TS behavior, enabling the device to function as an electronically reconfigurable bi-mode neuron. A variety of neuronal activities such as all-or-nothing behavior and tunable firing probability are mimicked under both excitatory and inhibitory stimuli. Furthermore, we develop a self-adaptive neuromorphic vision sensor based on bi-mode neurons, demonstrating effective object recognition in varied lighting conditions. Thus, our bi-mode neuron offers a versatile platform for constructing neuromorphic systems with rich functionality. |
