MAIS: an in-vitro sandbox enables adaptive neuromodulation via scalable neural interfaces

Chen, H.; Chen, F.; Chen, X.; Liu, Y.; Xu, J.; Li, J.; Bao, X.; Chen, Y.; Sun, H.; Jiang, J.; Ye, F.; Su, J.; Gen, Y.; Wang, Z.; Liu, L.; Hexige, S.; Li, X.; Ma, L.; Shuai, J.

doi:10.1101/2025.03.15.641656

bioRxiv · 10.1101/2025.03.15.641656

MAIS: an in-vitro sandbox enables adaptive neuromodulation via scalable neural interfaces

Chen, H.·Chen, F.·Chen, X.·Liu, Y.·Xu, J.·Li, J.·Bao, X.·Chen, Y.·Sun, H.·Jiang, J.·Ye, F.·Su, J.·Gen, Y.·Wang, Z.·Liu, L.·Hexige, S.·Li, X.·Ma, L.·Shuai, J.

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Abstract

Brain-machine interfaces (BMIs) predominantly rely on static digital architectures to decode biological neuronal networks, a paradigm that is incompatible with natural neural coding in the human brain1-4. Bridging this gap is a critical step in combating neuronal dysfunction, enhancing brain functionality, and refining the precision of neuroprosthetics5. The integration of brain organoids with microelectrode array (MEA), as a class of BMIs, offers a humanized in vitro platform with unique biological compatibility advantages for dynamic neuronal decoding. This study resolves the biological-electronic encoding incompatibility of brain organoid-MEA Integration through three progressive breakthroughs. First, a human-machine hybrid agent is developed as a newly proposed bioengineered platform that couples brain organoids together with high-density MEAs and computational chips, enabling closed-loop perturbation of biological neuronal networks via exogenous signals. Second, through plasticity-driven real-time tracking of neuronal activity, we establish dynamically reconfigurable stimulation nodes that self-align with the electrophysiological states of the organoids. This resolves the exogenous-endogenous encoding mismatch by implementing plasticity-driven adaptation principles that ensure biological compatibility through spatially adaptive coordination. Finally, through shared plasticity rules rather than centralized control, we construct the first scalable multi-agent interactive system (MAIS) and demonstrate its real-world applications. Through designed scenarios of pathological/normal neuronal network interaction, we validate that MAIS achieves stable cross-network coordination. MAIS embodies a self-evolving neural coding sandbox in which plasticity-driven dynamic decoding bridges the compatibility gaps between biological and digital systems, providing a scalable and foundational infrastructure for human-centered neural interfaces.

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Chen, H., Chen, F., Chen, X., Liu, Y., Xu, J., Li, J., Bao, X., Chen, Y., Sun, H., Jiang, J., Ye, F., Su, J., Gen, Y., Wang, Z., Liu, L., Hexige, S., Li, X., Ma, L., Shuai, J.. 2025-03-17. MAIS: an in-vitro sandbox enables adaptive neuromodulation via scalable neural interfaces. https://doi.org/10.1101/2025.03.15.641656

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