近日，俄罗斯圣彼得堡国立大学的Evgeny Sedov与Alexey Kavokin合作并取得一项新进展。经过不懈努力，他们提出将极化子晶格作为二值化神经形态网络。相关研究成果已于2025年1月16日在国际知名学术期刊《光：科学与应用》上发表。

该研究团队提出一种基于激子-极化子凝聚体晶格的新型神经形态网络架构，该架构通过非共振光泵浦实现复杂互联和能量供给。网络采用二值化框架，其中每个神经元借助成对耦合凝聚体的空间相干性执行二元运算。这种由极化子的弹道传播所产生的相干性确保了网络范围内的高效通信。

由极化子的激子成分引起的非线性排斥驱动的二值化神经元开关机制，相较于连续权重神经网络，在计算效率和可扩展性方面具有优势。该研究的网络支持并行处理，与顺序或脉冲编码二值化系统相比，提高了计算速度。

研究人员使用了包括用于图像识别的MNIST数据集和用于语音识别任务的Speech Commands数据集在内的多种数据集来评估系统性能。在两种情况下，所提系统均展现出超越现有极化子神经形态系统的潜力。在图像识别方面，其预测分类准确率高达97.5%，令人印象深刻。在语音识别方面，该系统在十类子集上的分类准确率约为68%，超过了传统基准方法——隐马尔可夫模型与高斯混合模型的组合。

附：英文原文

Title: Polariton lattices as binarized neuromorphic networks

Author: Sedov, Evgeny, Kavokin, Alexey

Issue&Volume: 2025-01-16

Abstract: We introduce a novel neuromorphic network architecture based on a lattice of exciton-polariton condensates, intricately interconnected and energized through nonresonant optical pumping. The network employs a binary framework, where each neuron, facilitated by the spatial coherence of pairwise coupled condensates, performs binary operations. This coherence, emerging from the ballistic propagation of polaritons, ensures efficient, network-wide communication. The binary neuron switching mechanism, driven by the nonlinear repulsion through the excitonic component of polaritons, offers computational efficiency and scalability advantages over continuous weight neural networks. Our network enables parallel processing, enhancing computational speed compared to sequential or pulse-coded binary systems. The system’s performance was evaluated using diverse datasets, including the MNIST dataset for image recognition and the Speech Commands dataset for voice recognition tasks. In both scenarios, the proposed system demonstrates the potential to outperform existing polaritonic neuromorphic systems. For image recognition, this is evidenced by an impressive predicted classification accuracy of up to 97.5%. In voice recognition, the system achieved a classification accuracy of about 68% for the ten-class subset, surpassing the performance of conventional benchmark, the Hidden Markov Model with Gaussian Mixture Model.

DOI: 10.1038/s41377-024-01719-4

Source: https://www.nature.com/articles/s41377-024-01719-4