发表机构
Saitama University; Kanazawa University（埼玉大学; 金泽大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究光子储层计算中网络拓扑对其性能的影响，通过实验和数值模拟，引入复杂网络结构，对比混沌时间序列任务表现，发现小世界网络性能最佳，还实现光子人脑网络，表明网络拓扑影响大，小世界结构最优。
AI 中文摘要
光子储层计算作为一种快速且低成本的时间序列预测方法受到越来越多关注，它利用了光的高速、宽带宽和空间并行性。然而，大规模光子储层中内部连接结构（网络拓扑）对计算性能的影响尚未得到研究。本研究通过实验和数值模拟，利用空间光调制器进行光子储层计算，系统评估网络拓扑与储层计算性能的关系。引入小世界和无标度等复杂网络结构，进行混沌时间序列的记忆容量测量和一步预测任务以比较性能。发现小世界网络具有最大记忆容量和最佳预测性能，数值计算表明通过改变网络重连概率和储层泄漏率可优化时间序列预测性能。还实现了基于人类大脑活动连接组设计的光子人脑网络作为储层，发现网络拓扑强烈影响储层计算性能，小世界网络结构优于其他配置。
英文摘要
Photonic reservoir computing has attracted increasing attention as a fast and low-cost approach for time-series prediction. Photonic reservoir computing utilizes the high speed, broad bandwidth, and spatial parallelism of light. However, the effect of the internal connection structure (network topology) on the computing performance has not been investigated for large-scale photonic reservoirs. In this study, we experimentally and numerically demonstrate photonic reservoir computing using a spatial light modulator to systematically evaluate the relationship between the network topology and the performance of reservoir computing. We introduce complex network structures such as small-world and scale-free network topologies of the internal nodes in the reservoir. We perform the memory capacity measurement and the one-step-ahead prediction task of the chaotic time series to compare the performance. We found that the small-world network exhibits the maximum memory capacity and the best prediction performance. Our numerical calculations reveal that the performance of the time-series prediction can be optimized by changing the rewiring probability of the network and the leak rate of the reservoir. We also implement photonic human brain network as a reservoir, which is designed by the connectomes of human brain activities. We found that the network topology strongly affects the performance of reservoir computing, and the small-world network structure outperforms the other configurations.
Comments18 pages, 10 figures, 3 tables
