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The Neuron Circuit with Property of Sensitization or Habituation Based on Threshold Switch Devices

机译:基于阈值开关装置的具有敏化或习惯性的神经元电路

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Brain-inspired computing is an efficient method to implement artificial neural network, which is the foundation of artificial intelligence. In the brain-inspired computing, neuron circuits play an important role in information processing and computing. But in recent researches, most efforts have been paid to improve the calculating performance with the cost of adding more functions. Nonetheless, the biological characteristics of neuron have attracted less attention. This paper aims to emulate two important biological characteristics (habituation and sensitization) of neuron employed CMOS circuits based on threshold switch devices. In specific, we fabricate and test an Ag/SiTe/TiN threshold switch device with more than 100 times switching ratio. Based on this device, we design a habitual neuron circuit and a sensitive neuron circuit. These two circuit can achieve nonassociative learning. It can be used in image processing and improve efficiency and accuracy in feature extraction and image recognition.
机译:脑启发式计算是实现人工神经网络的有效方法,这是人工智能的基础。在受大脑启发的计算中,神经元电路在信息处理和计算中起着重要作用。但是在最近的研究中,已经付出了很多努力来提高计算性能,但要增加更多的功能。尽管如此,神经元的生物学特性吸引了较少的关注。本文旨在模拟基于阈值开关器件的神经元采用的CMOS电路的两个重要的生物学特性(适应和敏化)。具体来说,我们制造和测试的Ag / SiTe / TiN阈值开关器件的开关率超过100倍。基于该设备,我们设计了习惯性神经元电路和敏感神经元电路。这两个电路可以实现非联想学习。它可以用于图像处理,并提高特征提取和图像识别的效率和准确性。

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