Artificial intelligence theory could be the key to how collective cell intelligence works

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This study used the concept of reinforcement learning to explain the navigation of chemotactic cells toward sparsely distributed targets, showing how decentralized information processing through environmental interaction can lead to highly intelligent behavior. Simulations showed that groups of simple agents could navigate mazes more robustly than a more intelligent single agent. This demonstrates that decentralized teams of simple agents can efficiently process information as a group, with potential applications in medicine, artificial intelligence, and robotics.

2025.10.17

Press Release

Artificial intelligence theory could be the key to how collective cell intelligence works

Researchers from #UTokyo_IIS have found that single cells in collective chemotaxis act like agents in distributed reinforcement learning, utilizing the environment as an “external memory” and exhibiting highly intelligent behavior.

Related Researchers

KOBAYASHI Tetsuya J Professor

Quantitative Biology

Department of Informatics and Electronics
Center for Research on Engineering in Medicine and Biology (CREMeB)
Center for Dependable Socio-information Platform（DSiP）

Mathematical Informatics : Graduate School of Information Science and Technology
Electrical Engineering and Information Systems : Graduate School of Engineering