Optogenetically enhanced physical reservoir computing with in vitro neural networks for obstacle avoidance
The results highlight the potential of optogenetically controlled biological neural networks in neuro-robotic systems, showcasing their capability to achieve accurate and efficient obstacle avoidance through physical reservoir computing.
Authors
- Xiaojuan Sun (ORCID: https://orcid.org/0000-0002-3705-4083)
- Longze Sha (ORCID: https://orcid.org/0000-0001-7372-6644)
- Jie Li (ORCID: https://orcid.org/0000-0002-7075-4145)
- Yarong Lin
- Yueheng Lan (ORCID: https://orcid.org/0000-0002-2950-8022)
- Lili Gui (ORCID: https://orcid.org/0000-0002-3710-6330)
- Zeying Lu (ORCID: https://orcid.org/0000-0001-9245-2204)
- Yin Deng
- Kun Xu (ORCID: https://orcid.org/0000-0002-3863-344X)
- Qi Xu (ORCID: https://orcid.org/0000-0003-2660-8092)
Institutions
- Beijing University of Posts and Telecommunications (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- State Key Laboratory of Information Photonics and Optical Communications
Publication Details
- Journal
- Journal of Biomedical Optics
- Published
- 2025-10-22
- DOI
- https://doi.org/10.1117/1.jbo.30.10.105004
- Citations
- 1
- Primary Topic
- Photoreceptor and optogenetics research
- Type
- article
- Field-Weighted Citation Impact
- 0.91