Closed-Loop Physical Reservoir Computing with Optogenetic Control in Biological Neural Networks
This study introduces physical reservoir computing (PRC) using in vitro biological neural networks for efficient signal processing and computation. The system employs optogenetic control and First-Order Reduced and Controlled Error (FORCE) learning to enable a virtual car to avoid obstacles. Online neuronal activity is captured with a CMOS camera based on calcium imaging and optical stimulation is performed via a digital micromirror device (DMD). Neuronal action potentials, induced by light, guide the movement of the car. The adaptive learning system demonstrates effective error minimization and robust obstacle avoidance, advancing neuro-engineering applications and intelligent biohybrid systems.
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)
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
- Published
- 2024-11-02
- DOI
- https://doi.org/10.1109/acp/ipoc63121.2024.10809918
- Citations
- 2
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.69
Funders
- Beijing University of Posts and Telecommunications
- State Key Laboratory of Information Photonics and Optical Communications
- Fundamental Research Funds for the Central Universities