Machine Learning‐Driven Bioelectronics for Closed‐Loop Control of Cells. (21st December 2020)

• Record Type: Journal Article
• Authors: Selberg, John; Jafari, Mohammad; Mathews, Juanita; Jia, Manping; Pansodtee, Pattawong; Dechiraju, Harika; Wu, Chunxiao; Cordero, Sergio; Flora, Alexander; Yonas, Nebyu; Jannetty, Sophia; Diberardinis, Miranda; Teodorescu, Mircea; Levin, Michael; Gomez, Marcella; Rolandi, Marco
• Is Part Of: Advanced intelligent systems. Volume 2:Number 12(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-21
• Subjects: Artificial intelligence -- Periodicals; Robotics -- Periodicals; Control theory -- Periodicals; 006.3
• DOI: 10.1002/aisy.202070122
• Languages: English
• ISSNs: 2640-4567

Abstract

Abstract : Bioelectronics In article number 2000140, Michael Levin, Marcella Gomez, Marco Rolandi, and co‐workers present bioelectronics coupled with machine learning for intelligent control of cellular function. The authors set and maintain the membrane voltage of human pluripotent stem cells with H + ‐conducting bioelectronic devices. The H + ‐devices are actuated in a closed feedback loop and respond to required adjustments to the cell membrane voltage as measured with fluorescent reporters.