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

Abstract

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.