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Microelectrode arrays cultured with in vitro neural networks for motion control tasks: encoding and decoding progress and advances

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Argitaratua izan da:Microsystems & Nanoengineering vol. 11, no. 1 (2025), p. 233-254
Egile nagusia: Hua, Sihan
Beste egile batzuk: Liu, Yaoyao, Luo, Jinping, Li, Shangchen, Jiang, Longhui, Wu, Pei, Sun, Shutong, Shang, Li, Lu, Chengji, Zhang, Kui, Liu, Juntao, Wang, Mixia, Shi, Huaizhang, Cai, Xinxia
Argitaratua:
Springer Nature B.V.
Gaiak:
Comparative analysis
Artificial intelligence
Algorithms
Brain research
Task complexity
Closed loops
Changing environments
Motion control
Microelectrodes
Neurosciences
Control systems
Neuroplasticity
Energy consumption
Signal processing
Control tasks
Neural networks
Decision making
Communications systems
Arrays
Encoding-Decoding
Information processing
Real time
Actuators
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Laburpena:Microelectrode arrays (MEAs) cultured with in vitro neural networks are gaining prominence in bio-integrated system research, owing to their inherent plasticity and emergent learning behaviors. Here, recent advances in motion control tasks utilizing MEAs-based bio-integrated systems are presented, with a focus on encoding-decoding techniques. The bio-integrated system comprises MEAs integrated with neural networks, a bidirectional communication system, and an actuator. Classical decoding algorithms, such as firing-rate mapping and central firing-rate methods, along with cutting-edge artificial intelligence (AI) approaches, have been examined. These AI methods enhance the accuracy and adaptability of real-time, closed-loop motion control. A comparative analysis indicates that simpler, lower-complexity algorithms suit basic rapid-decision tasks, whereas deeper models exhibit greater potential in more complex temporal signal processing and dynamically changing environments. The review also systematically analyzes the prospects and challenges of bio-integrated systems for motion control. Future prospects suggest that MEAs cultured with in vitro neural networks may leverage their flexibility and low energy consumption to address diverse motion control scenarios, driving cross-disciplinary research at the intersection of neuroscience and artificial intelligence.
ISSN:2096-1030
2055-7434
DOI:10.1038/s41378-025-01046-7
Baliabidea:Health & Medical Collection