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  • 标题:All-Optical Reinforcement Learning In Solitonic X-Junctions
  • 本地全文:下载
  • 作者:M. Alonzo ; D. Moscatelli ; L. Bastiani
  • 期刊名称:Scientific Reports
  • 电子版ISSN:2045-2322
  • 出版年度:2018
  • 卷号:8
  • 期号:1
  • 页码:5716
  • DOI:10.1038/s41598-018-24084-w
  • 语种:English
  • 出版社:Springer Nature
  • 摘要:Ethology has shown that animal groups or colonies can perform complex calculation distributing simple decision-making processes to the group members. For example ant colonies can optimize the trajectories towards the food by performing both a reinforcement (or a cancellation) of the pheromone traces and a switch from one path to another with stronger pheromone. Such ant's processes can be implemented in a photonic hardware to reproduce stigmergic signal processing. We present innovative, completely integrated X-junctions realized using solitonic waveguides which can provide both ant's decision-making processes. The proposed X-junctions can switch from symmetric (50/50) to asymmetric behaviors (80/20) using optical feedbacks, vanishing unused output channels or reinforcing the used ones.
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