Bionic Dual-Loop Emotional Learning Circuit and Its Application in Radiation Early Warning Monitoring

被引:9
|
作者
Zhou, Hanpu [1 ]
Fei, Zhuoying [1 ]
Hong, Qinghui [1 ]
Sun, Jingru [1 ]
Du, Sichun [1 ]
Li, Tao [1 ]
Zhang, Jiliang [1 ]
机构
[1] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Peoples R China
基金
中国国家自然科学基金;
关键词
Biological circuit; early warning; emotional learning; memristor; MEMRISTIVE CIRCUIT; BRAIN; CONTROLLER; MEMORY; IMPLEMENTATION; MODEL; FEAR;
D O I
10.1109/TCDS.2022.3200470
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Emotional learning is a very important learning mechanism for the human body, and it is also an important part of artificial intelligence. This article proposes a bionic circuit, which simulates the dual loop of emotional learning in the human brain. The circuit uses the characteristics of the memristor to simulate the fast loop that quickly generates initial emotions to predict danger and the detour loop which generates a more refined emotion by further processing of the input information. The circuit includes: 1) thalamus module; 2) sensory cortex module; and 3) amygdala module. The first module sends the strongest input stimulus directly to the amygdala module for rapid processing. The second module processes the input stimuli and sends them to the amygdala module for emotion generation and judgment. The third module is responsible for generating output signals representing the corresponding emotions. The simulation results in PSPICE show that the output signal generated by the circuit can represent the characteristics of the dual loop of emotional learning, and more realistically simulate the biological process. By configuring the parameters, this circuit can be more flexibly applied to the radiation intensity early warning system to protect the safety of humans when working in a radiation environment.
引用
收藏
页码:1196 / 1208
页数:13
相关论文
共 21 条
  • [21] Dual-indicators machine learning assisted processing high-quality laser-induced fluorine-doped graphene and its application on droplet velocity monitoring sensor
    Xie, Bin
    Guo, Yuanhui
    Chen, Yun
    Luo, Xiangyuan
    Zhang, Hao
    Long, Junyu
    Wen, Guanhai
    Hou, Maoxiang
    Liu, Huilong
    Ma, Li
    Chen, Xin
    CARBON, 2024, 226