神经形态工程学
MNIST数据库
材料科学
计算机科学
联轴节(管道)
人工神经网络
神经科学
纳米技术
人工智能
生物
冶金
作者
Yuanxia Chen,J. Xia,Youzhi Qu,Hongjie Zhang,Tingting Mei,Xinyi Zhu,Guoheng Xu,Dongyang Li,Li Wang,Quanying Liu,Kai Xiao
标识
DOI:10.1002/adma.202419013
摘要
Neuromorphic devices are designed to replicate the energy-efficient information processing advantages found in biological neural networks by emulating the working mechanisms of neurons and synapses. However, most existing neuromorphic devices focus primarily on functionally mimicking biological synapses, with insufficient emphasis on ion transport mechanisms. This limitation makes it challenging to achieve the complexity and connectivity inherent in biological systems, such as ephaptic coupling. Here, an ionic biomimetic synaptic device based on a flexible ion-gel nanofiber network is proposed, which transmits information and enables ephaptic coupling through capacitance formation by ion transport with an extremely low energy consumption of just 6 femtojoules. The hysteretic ion transport behavior endows the device with synaptic-like memory effects, significantly enhancing the performance of the reservoir computing system for classifying the MNIST handwritten digit dataset and demonstrating high efficiency in edge learning. More importantly, the devices in an array establish communication connections, exhibiting global oscillatory behaviors similar to ephaptic coupling in biological neural networks. This connectivity enables the array to perform working memory tasks, paving the way for developing brain-like systems characterized by high complexity and vast connectivity.
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