Merging machine learning and bioelectronics for closed-loop control of biological systems and homeostasis

The regulation of most physiological processes relies on a state of equilibrium called homeostasis, which is achieved through a biological control loop involving sensors and actuators. However, disease and aging can disrupt these control loops, leading to impaired or slower homeostatic mechanisms. Bioelectronic devices offer the opportunity to interface artificial technology with biological systems, enabling the measurement and control of specific processes using sensors and actuators. To effectively interact with complex biological dynamics and adapt to changing environmental conditions, these interfacing devices must be capable of real-time sensing and response. In this context, we propose that machine learning can significantly enhance the capabilities of bioelectronics by facilitating real-time processing of sensor and actuator data. By utilizing machine-learning-driven bioelectronics, we can maintain and regulate biological system responses more effectively compared with traditional approaches. This advancement holds promising implications for bioelectronic medicine and precision medicine, particularly in repairing impaired homeostatic mechanisms.