Modular organoid networks acquire source-signal discrimination through input-driven network refinement

Brain organoids have the potential to acquire functions through plasticity within their complex cellular ensembles. However, unlocking their intrinsic capacity for functional refinement remains challenging because organoids lack structured sensory input and organized macroscopic circuit architecture. Here, we hypothesized that modular connectivity among cerebral organoids enhances their capacity for stimulus-driven circuit refinement.

To test this hypothesis, we connected cerebral organoids and assessed their time-course performance improvement in a source-signal discrimination task. Networks comprising three cerebral organoids, but neither single organoids nor two-organoid networks, significantly improved their discrimination performance after two weeks of repeated stimulation. This improvement was accompanied by the emergence of input-dependent spatiotemporal responses, differential refinement of intra- and inter-organoid functional connectivity, faster response kinetics, and directional propagation of spontaneous activity within the three-organoid networks.

Together, these results demonstrate that repeated input to an organized modular network can rewire organoids generated under identical conditions into functionally differentiated modules, thereby generating task-relevant heterogeneity that underlies consistent functional enhancement in vitro. This work was primarily supported by the Research Institute of Advanced Technology, Softbank Corp., with additional support from JSPS [20K20643, 24H02307, 25H02596, JPJSCCA 20190006], AMED [JP20gm1410001, 24wm0625318, 25wm0625323], HFSP [RGP012/2024], and the Institute for AI and Beyond. The study also received support from JST SPRING [JPMJSP2108] and the ANRI fellowship.

Institute of Industrial Science, The University of Tokyo, Tokyo, Japan Siu Yu A. Chow, Huaruo Hu, Tomoya Duenki & Yoshiho Ikeuchi Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan LIMMS, CNRS-Institute of Industrial Science, IRL 2820, The University of Tokyo, Tokyo, Japan Institute for AI and Beyond, The University of Tokyo, Tokyo, Japan The authors declare no competing interests. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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