近日，福州大学的陈惠鹏及其研究团队取得一项新进展。经过不懈努力，他们提出面向混合物理节点储层计算的双光电输出发光突触储层系统。相关研究成果已于2024年8月1日在国际知名学术期刊《光：科学与应用》上发表。

因此，本研究首次介绍了一种用于储层计算的双光电输出人工发光突触（LES）装置，并提出了一种采用混合物理节点的储层系统。该系统利用包含不同物理量的混合物理节点储层，有效地将输入信号转换为两个特征值输出：一是具有非线性光效应的光输出，二是具有记忆特性的电输出。

与以往基于忆阻器的储层系统仅在一个物理维度上追求丰富的储层状态不同，本研究提出的混合物理节点储层系统能够在单个输入中同时获得两个物理维度的储层状态，而无需增加器件的数量和类型。

在MNIST数据集识别任务中，人工发光突触库系统的识别率达到了97.22%。通过光电双储层的非线性映射，实现了多通道图像的识别，识别精度高达99.25%。本研究提出的混合物理节点储层计算方法，为光电混合神经网络的发展以及材料-算法的协同设计提供了广阔的前景。

据悉，基于忆阻器的物理储层计算在高效处理复杂时空数据方面具有巨大潜力，这对于推进人工智能至关重要。然而，由于传统忆阻器储层计算的单一物理节点映射特性，在一定程度上不可避免地导致特征值的高重复性，极大地限制了基于忆阻器的储层计算在复杂任务中的效率和性能。

附：英文原文

Title: Towards mixed physical node reservoir computing: light-emitting synaptic reservoir system with dual photoelectric output

Author: Lian, Minrui, Gao, Changsong, Lin, Zhenyuan, Shan, Liuting, Chen, Cong, Zou, Yi, Cheng, Enping, Liu, Changfei, Guo, Tailiang, Chen, Wei, Chen, Huipeng

Issue&Volume: 2024-08-01

Abstract: Memristor-based physical reservoir computing holds significant potential for efficiently processing complex spatiotemporal data, which is crucial for advancing artificial intelligence. However, owing to the single physical node mapping characteristic of traditional memristor reservoir computing, it inevitably induces high repeatability of eigenvalues to a certain extent and significantly limits the efficiency and performance of memristor-based reservoir computing for complex tasks. Hence, this work firstly reports an artificial light-emitting synaptic (LES) device with dual photoelectric output for reservoir computing, and a reservoir system with mixed physical nodes is proposed. The system effectively transforms the input signal into two eigenvalue outputs using a mixed physical node reservoir comprising distinct physical quantities, namely optical output with nonlinear optical effects and electrical output with memory characteristics. Unlike previously reported memristor-based reservoir systems, which pursue rich reservoir states in one physical dimension, our mixed physical node reservoir system can obtain reservoir states in two physical dimensions with one input without increasing the number and types of devices. The recognition rate of the artificial light-emitting synaptic reservoir system can achieve 97.22% in MNIST recognition. Furthermore, the recognition task of multichannel images can be realized through the nonlinear mapping of the photoelectric dual reservoir, resulting in a recognition accuracy of 99.25%. The mixed physical node reservoir computing proposed in this work is promising for implementing the development of photoelectric mixed neural networks and material-algorithm collaborative design.

DOI: 10.1038/s41377-024-01516-z

Source: https://www.nature.com/articles/s41377-024-01516-z