神经形态工程学
门控
氧化铟锡
计算机科学
人工神经网络
兴奋性突触后电位
晶体管
材料科学
记忆
纳米技术
神经科学
图层(电子)
抑制性突触后电位
电压
人工智能
电气工程
生物
数学教育
工程类
数学
作者
Yifei Liu,Guangdi Feng,Qiuxiang Zhu,Yu Xu,Shenglan Hao,Ke Qu,Bobo Tian,Chun‐Gang Duan
摘要
Information processing and memorizing in the brain take place in a neural network consisting of neurons connected with each other by synapses. Meanwhile, the neural network is immersed in a common electrochemical environment with global parameters regulating the overall functions, which is barely discussed in neuromorphic devices. In this study, organic/inorganic hybrid transistors with sodium alginate as the gate dielectric layer and indium tin oxide as the channel were successfully prepared. We have not only simulated the basic properties of synapses in a single device, but, on top of that, also simulated the global regulation of information processing in the brain due to the incorporation of global grids, achieving excitatory and inhibitory synaptic weight. Moreover, the construction of a 3 × 3 synaptic array enables image learning and memorizing functions. These results demonstrate the significant advantages of electrolyte-gated transistors in enabling complex neural network connectivity and offer a promising opportunity for future artificial synapses.
科研通智能强力驱动
Strongly Powered by AbleSci AI