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Brain organoid reservoir computing for artificial intelligence

Nature Electronics volume 6, pages 1032–1039 (2023)Cite this article

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Abstract

Brain-inspired computing hardware aims to emulate the structure and working principles of the brain and could be used to address current limitations in artificial intelligence technologies. However, brain-inspired silicon chips are still limited in their ability to fully mimic brain function as most examples are built on digital electronic principles. Here we report an artificial intelligence hardware approach that uses adaptive reservoir computation of biological neural networks in a brain organoid. In this approach—which is termed Brainoware—computation is performed by sending and receiving information from the brain organoid using a high-density multielectrode array. By applying spatiotemporal electrical stimulation, nonlinear dynamics and fading memory properties are achieved, as well as unsupervised learning from training data by reshaping the organoid functional connectivity. We illustrate the practical potential of this technique by using it for speech recognition and nonlinear equation prediction in a reservoir computing framework.

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Fig. 1: Brainoware with unsupervised learning for AI computing.
Fig. 2: Reservoir computing hardware properties.
Fig. 3: Speech recognition.
Fig. 4: Predicting a nonlinear chaotic equation.

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Data availability

Source data are provided with this paper. All other data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

F.G. wants to acknowledge support from the National Institute of Health Awards (DP2AI160242, R01DK133864 and U01DA056242). We also acknowledge Indiana University Imaging Center (NIH1S10OD024988-01).

Author information

Authors and Affiliations

  1. Department of Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN, USA

    Hongwei Cai, Zheng Ao, Chunhui Tian, Zhuhao Wu & Feng Guo

  2. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Hongcheng Liu

  3. Center for Stem Cell & Organoid Medicine (CuSTOM), Division of Pulmonary Biology, Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Jason Tchieu & Mingxia Gu

  4. University of Cincinnati School of Medicine, Cincinnati, OH, USA

    Jason Tchieu & Mingxia Gu

  5. Gill Center for Biomolecular Science, Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, USA

    Ken Mackie

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  1. Hongwei Cai
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Contributions

F.G. and H.C. conceived the study and designed the experiments. H.C., Z.A., C.T. and Z.W. performed the experiment. H.C., H.L., J.T., M.G. and K.M. analysed the data. F.G. and H.C. wrote the paper. All authors read and provided feedback on the paper.

Corresponding author

Correspondence to Feng Guo.

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The authors declare no competing interests.

Peer review

Peer review information

Nature Electronics thanks Arti Ahluwalia and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–11, Tables 1–3, Discussion and References.

Reporting Summary

Supplementary Video 1

Electrical activity of an organoid. Spike-activity heat map of cortical organoid electrical activity (day 60, culture in an high-density MEA chip). The video is in ×4. Scale bar, 100 μm.

Supplementary Video 2

Calcium activity of an organoid. Confocal calcium imaging of organoid neural activity (day 60, transfer to a glass-bottom six-well plate). The video is in ×10. Scale bar, 100 μm.

Source data

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

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Cai, H., Ao, Z., Tian, C. et al. Brain organoid reservoir computing for artificial intelligence. Nat Electron 6, 1032–1039 (2023). https://doi.org/10.1038/s41928-023-01069-w

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