神经形态工程学
突触重量
实现(概率)
干扰(通信)
记忆电阻器
油藏计算
尖峰神经网络
材料科学
计算机科学
神经科学
电子工程
人工神经网络
电信
工程类
人工智能
循环神经网络
频道(广播)
生物
统计
数学
作者
Jialin Meng,Jieru Song,Yuqing Fang,Tianyu Wang,Hao Zhu,Ji Li,Qingqing Sun,David Wei Zhang,Lin Chen
出处
期刊:ACS Nano
[American Chemical Society]
日期:2024-03-13
卷期号:18 (12): 9150-9159
被引量:4
标识
DOI:10.1021/acsnano.4c00424
摘要
Realization of dendric signal processing in the human brain is of great significance for spatiotemporal neuromorphic engineering. Here, we proposed an ionic dendrite device with multichannel communication, which could realize synaptic behaviors even under an ultralow action potential of 80 mV. The device not only could simulate one-to-one information transfer of axons but also achieve a many-to-one modulation mode of dendrites. By the adjustment of two presynapses, Pavlov's dog conditioning experiment was learned successfully. Furthermore, the device also could emulate the biological synaptic competition and synaptic cooperation phenomenon through the comodulation of three presynapses, which are crucial for artificial neural network (ANN) implementation. Finally, an ANN was further constructed to realize highly efficient and anti-interference recognition of fashion patterns. By introducing the cooperative device, synaptic weight updates could be improved for higher linearity and larger dynamic regulation range in neuromorphic computing, resulting in higher recognition accuracy and efficiency. Such an artificial dendric device has great application prospects in the processing of more complex information and the construction of an ANN system with more functions.
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