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Microelectrode arrays cultured with in vitro neural networks for motion control tasks: encoding and decoding progress
Sihan Hua1,2, Yaoyao Liu1,2, Jinping Luo3,4
1State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190, China.
Microsystems & Nanoengineering
|November 27, 2025
Summary
Bio-integrated systems using microelectrode arrays (MEAs) and in vitro neural networks show promise for motion control. Artificial intelligence enhances real-time control, with algorithm choice depending on task complexity.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Artificial Intelligence
Background:
- Microelectrode arrays (MEAs) with in vitro neural networks are crucial for bio-integrated systems.
- These systems exhibit inherent plasticity and learning behaviors, making them suitable for advanced applications.
Purpose of the Study:
- To review recent advances in motion control using MEA-based bio-integrated systems.
- To focus on encoding-decoding techniques and compare classical and AI-driven approaches.
Main Methods:
- Examination of classical decoding algorithms (e.g., firing-rate mapping).
- Evaluation of artificial intelligence (AI) methods for enhanced motion control accuracy and adaptability.
- Comparative analysis of algorithm complexity versus task requirements.
Main Results:
- AI methods significantly improve real-time, closed-loop motion control.
- Simpler algorithms are effective for basic tasks, while complex models excel in dynamic environments.
- Bio-integrated systems offer flexibility and low energy consumption.
Conclusions:
- MEA-based bio-integrated systems are promising for diverse motion control applications.
- Algorithm selection is critical for optimizing performance based on task complexity.
- Future research can drive cross-disciplinary advancements in neuroscience and AI.
