论文类型：期刊论文
第一作者：王宽
通讯作者：何毓辉
合写作者：缪向水,童浩,Scheicher,H.,Ralph,王伦,张大友,诸葛福伟,林琪,高滨,胡庆
发表刊物：The Royal Society of Chemistry
所属单位：华中科技大学、清华大学、Uppsala University
学科门类：工学
一级学科：电子科学与技术
文献类型：J
卷号：8
期号：2
页面范围：619-629
DOI码：10.1039/DOMH01759K
发表时间：4417-09-01
摘要：Biological neurons exhibit dynamic excitation behavior in the form of stochastic firing, rather than stiffly giving out spikes upon reaching a fixed threshold voltage, which empowers the brain to perform probabilistic inference in the face of uncertainty. However, owing to the complexity of the stochastic firing process in biological neurons, the challenge of fabricating and applying stochastic neurons with bio-realistic dynamics to probabilistic scenarios remains to be fully addressed. In this work, a novel CuS/GeSe conductive-bridge threshold switching memristor is fabricated and singled out to realize electronic stochastic neurons, which is ascribed to the similarity between the stochastic switching behavior observed in the device and that of biological ion channels. The corresponding electric circuit of a stochastic neuron is then constructed and the probabilistic firing capacity of the neuron is utilized to implement Bayesian inference in a spiking neural network (SNN). The application prospects are demonstrated on the example of a tumor diagnosis task, where common fatal diagnostic errors of a conventional artificial neural network are successfully circumvented. Moreover, in comparison to deterministic neuron-based SNNs, the stochastic neurons enable SNNs to deliver an estimate of the uncertainty in their predictions, and the fidelity of the judgement is drastically improved by 81.2%.
发布期刊链接：https://pubs.rsc.org/en/content/articlehtml/2021/mh/d0mh01759k