中心博士后马伯文的工作——Stochastic photonic spiking neuron for Bayesian inference with unsupervised learning（面向无监督贝叶斯推理的随机光子尖峰神经元）相关成果近期被Optics Letters期刊接收发表，该工作得到了国家重点研发计划（2019YFB2203700）、国家自然科学基金（T2225023）的部分资助。作者提出了一种基于噪声注入的随机光子尖峰神经元实现方案，模拟了生物神经元的泊松发放特性，表征了尖峰触发概率的信息编码机制。进一步，证明了随机光子尖峰神经元网络的贝叶斯推理能力，通过预测熵指标能够规避输入不确定性导致的错误决策。因此，随机光子尖峰神经元有望为光子神经形态领域提供高效的贝叶斯推理与不确定性评估能力。

摘要: Stochasticity is an inherent feature of biological neural activities. We propose a noise-injection scheme to implement a GHz-rate stochastic photonic spiking neuron (S-PSN). The firing-probability encoding is experimentally demonstrated and exploited for Bayesian inference with unsupervised learning. In a breast diagnosis task, the stochastic photonic spiking neural network (S-PSNN) can not only achieve a classification accuracy of 96.6%, but can also evaluate the diagnosis uncertainty with prediction entropies. As a result, the misdiagnosis rate is reduced by 80% compared to that of a conventional deterministic photonic spiking neural network (D-PSNN) for the same task. The GHz-rate S-PSN endows the neuromorphic photonics with high-speed Bayesian inference for reliable information processing in error-critical scenarios.

• 论文DOI：10.1364/OL.484268 论文链接