A bidirectional thermal sensory leaky integrate-and-fire (LIF) neuron model based on bipolar NbO volatile threshold devices with ultra-low operating current.

Journal: Nanoscale
PMID: 37874690

Abstract

Brain-like artificial intelligence (AI) will become the main form and important platform in future computing. It will play an important and unique role in simulating brain functions, efficiently implementing AI algorithms, and improving computing power. Developing artificial neurons that can send facilitation/depression signals to artificial synapses, sense, and process temperature information is of great significance for achieving more efficient and compact brain-like computing systems. Herein, we have constructed a NbO bipolar volatile threshold memristor, which could be operated by 1 μA ultra-low current and up to ∼10 switching ratios. By using a leaky integrate-and-fire (LIF) artificial neuron model, a bipolar LIF artificial neuron is constructed, which can realize the conventional threshold-driven firing, all-or-nothing spiking, refractory periods, and intensity-modulated frequency response bidirectionally at the positive/negative voltage stimulation, which will give the artificial synapse facilitation/depression signals. Furthermore, this bipolar LIF neuron can also explore different temperatures to output different signals, which could be constructed as a more compact thermal sensory neuron to avoid external harm to artificial robots. This study is of great significance for improving the computational efficiency of the system more effectively, achieving high integration density and low energy consumption artificial neural networks to meet the needs of brain-like neural computing.

Authors

  • Jianhui Zhao
    National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
  • Liang Tong
  • Jiangzhen Niu
    Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China. xiaobing_yan@126.com.
  • Ziliang Fang
    Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China. xiaobing_yan@126.com.
  • Yifei Pei
    National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
  • Zhenyu Zhou
    Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China. xiaobing_yan@126.com.
  • Yong Sun
    School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
  • Zhongrong Wang
    School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China.
  • Hong Wang
    Department of Cardiology, Liuzhou Workers' Hospital, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, China.
  • Jianzhong Lou
    Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China. xiaobing_yan@126.com.
  • Xiaobing Yan
    Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, Key Laboratory of Optoelectronic Information Materials of Hebei Province, Hebei University, Baoding, Hebei, 071002, China.

Keywords

External Resources

Popular Topics
Recent Journals