Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Brain organoid reservoir computing for artificial intelligence

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

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

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.

References

  1. Tang, J. et al. Bridging biological and artificial neural networks with emerging neuromorphic devices: fundamentals, progress, and challenges. Adv. Mater. 31, e1902761 (2019).

    Article  Google Scholar 

  2. Sejnowski, T. J. & Rosenberg, C. R. Parallel networks that learn to pronounce English text. Complex Syst. 1, 145–168 (1987).

    Google Scholar 

  3. Samarasinghe, S. Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition (Auerbach Publications, 2016).

  4. Gokmen, T. & Vlasov, Y. Acceleration of deep neural network training with resistive cross-point devices: design considerations. Front. Neurosci. 10, 333 (2016).

    Article  Google Scholar 

  5. Mehonic, A. & Kenyon, A. J. Brain-inspired computing needs a master plan. Nature 604, 255–260 (2022).

    Article  Google Scholar 

  6. Xia, Q. & Yang, J. J. Memristive crossbar arrays for brain-inspired computing. Nat. Mater. 18, 309–323 (2019).

    Article  Google Scholar 

  7. Wang, Z. R. et al. Resistive switching materials for information processing. Nat. Rev. Mater. 5, 173–195 (2020).

    Article  Google Scholar 

  8. Tanaka, G. et al. Recent advances in physical reservoir computing: a review. Neural Netw. 115, 100–123 (2019).

    Article  Google Scholar 

  9. Zidan, M. A., Strachan, J. P. & Lu, W. D. The future of electronics based on memristive systems. Nat. Electron. 1, 22–29 (2018).

    Article  Google Scholar 

  10. Grollier, J. et al. Neuromorphic spintronics. Nat. Electron. 3, 360–370 (2020).

    Article  Google Scholar 

  11. Marković, D., Mizrahi, A., Querlioz, D. & Grollier, J. Physics for neuromorphic computing. Nat. Rev. Phys. 2, 499–510 (2020).

    Article  Google Scholar 

  12. Goswami, S. et al. Decision trees within a molecular memristor. Nature 597, 51–56 (2021).

    Article  Google Scholar 

  13. Purves, D. et al. Neurosciences (De Boeck Supérieur, 2019).

  14. Parisi, G. I., Kemker, R., Part, J. L., Kanan, C. & Wermter, S. Continual lifelong learning with neural networks: a review. Neural Netw. 113, 54–71 (2019).

    Article  Google Scholar 

  15. Krogh, A. What are artificial neural networks? Nat. Biotechnol. 26, 195–197 (2008).

    Article  Google Scholar 

  16. Schmidhuber, J. Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015).

    Article  Google Scholar 

  17. Milano, G. et al. In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks. Nat. Mater. 21, 195–202 (2022).

    Article  Google Scholar 

  18. Sillin, H. O. et al. A theoretical and experimental study of neuromorphic atomic switch networks for reservoir computing. Nanotechnology 24, 384004 (2013).

    Article  Google Scholar 

  19. Strukov, D. B., Snider, G. S., Stewart, D. R. & Williams, R. S. The missing memristor found. Nature 453, 80–83 (2008).

    Article  Google Scholar 

  20. Torrejon, J. et al. Neuromorphic computing with nanoscale spintronic oscillators. Nature 547, 428–431 (2017).

    Article  Google Scholar 

  21. Zhang, W. Q. et al. Neuro-inspired computing chips. Nat. Electron. 3, 371–382 (2020).

    Article  Google Scholar 

  22. Wang, Z. et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat. Mater. 16, 101–108 (2017).

    Article  Google Scholar 

  23. Yang, J. J., Strukov, D. B. & Stewart, D. R. Memristive devices for computing. Nat. Nanotechnol. 8, 13–24 (2013).

    Article  Google Scholar 

  24. Yao, P. et al. Face classification using electronic synapses. Nat. Commun. 8, 15199 (2017).

    Article  Google Scholar 

  25. Li, C. et al. Analogue signal and image processing with large memristor crossbars. Nat. Electron. 1, 52–59 (2018).

    Article  Google Scholar 

  26. Moon, J. et al. Temporal data classification and forecasting using a memristor-based reservoir computing system. Nat. Electron. 2, 480–487 (2019).

    Article  Google Scholar 

  27. Zhong, Y. et al. Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing. Nat. Commun. 12, 408 (2021).

    Article  Google Scholar 

  28. Trujillo, C. A. et al. Complex oscillatory waves emerging from cortical organoids model early human brain network development. Cell Stem Cell 25, 558–569 (2019).

    Article  Google Scholar 

  29. Lancaster, M. A. et al. Cerebral organoids model human brain development and microcephaly. Nature 501, 373–379 (2013).

    Article  Google Scholar 

  30. Chiaradia, I. & Lancaster, M. A. Brain organoids for the study of human neurobiology at the interface of in vitro and in vivo. Nat. Neurosci. 23, 1496–1508 (2020).

    Article  Google Scholar 

  31. Qian, X. et al. Brain-region-specific organoids using mini-bioreactors for modeling ZIKV exposure. Cell 165, 1238–1254 (2016).

    Article  Google Scholar 

  32. Lukoševičius, M. & Jaeger, H. Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3, 127–149 (2009).

    Article  Google Scholar 

  33. Giandomenico, S. L. et al. Cerebral organoids at the air-liquid interface generate diverse nerve tracts with functional output. Nat. Neurosci. 22, 669–679 (2019).

    Article  Google Scholar 

  34. Sharf, T. et al. Functional neuronal circuitry and oscillatory dynamics in human brain organoids. Nat. Commun. 13, 4403 (2022).

    Article  Google Scholar 

  35. Canossa, M. et al. Neurotrophin release by neurotrophins: implications for activity-dependent neuronal plasticity. Proc. Natl Acad. Sci. USA 94, 13279–13286 (1997).

    Article  Google Scholar 

  36. Smirnova, L. et al. Organoid intelligence (OI): the new frontier in biocomputing and intelligence-in-a-dish. Front. Sci. 1, 1017235 (2023).

  37. Magliaro, C. & Ahluwalia, A. To brain or not to brain organoids. Front. Sci. 1, 1148873 (2023).

    Article  Google Scholar 

  38. Hofer, M. & Lutolf, M. P. Engineering organoids. Nat. Rev. Mater. 6, 402–420 (2021).

    Article  Google Scholar 

  39. Huang, Q. et al. Shell microelectrode arrays (MEAs) for brain organoids. Sci. Adv. 8, eabq5031 (2022).

    Article  MathSciNet  Google Scholar 

  40. Park, Y. et al. Three-dimensional, multifunctional neural interfaces for cortical spheroids and engineered assembloids. Sci. Adv. 7, eabf9153 (2021).

    Article  Google Scholar 

  41. Li, T. L. et al. Stretchable mesh microelectronics for the biointegration and stimulation of human neural organoids. Biomaterials 290, 121825 (2022).

    Article  Google Scholar 

  42. Weltman, A., Yoo, J. & Meng, E. Flexible, penetrating brain probes enabled by advances in polymer microfabrication. Micromachines 7, 180 (2016).

    Article  Google Scholar 

  43. Lin, S. et al. A flexible, robust, and gel-free electroencephalogram electrode for noninvasive brain-computer interfaces. Nano Lett. 19, 6853–6861 (2019).

    Article  Google Scholar 

  44. Kagan, B. J. et al. In vitro neurons learn and exhibit sentience when embodied in a simulated game-world. Neuron 110, 3952–3969.e3958 (2022).

    Article  Google Scholar 

  45. Bakkum, D. J., Chao, Z. C. & Potter, S. M. Spatio-temporal electrical stimuli shape behavior of an embodied cortical network in a goal-directed learning task. J. Neural Eng. 5, 310 (2008).

    Article  Google Scholar 

  46. Chao, Z. C., Bakkum, D. J. & Potter, S. M. Shaping embodied neural networks for adaptive goal-directed behavior. PLoS Comput. Biol. 4, e1000042 (2008).

    Article  MathSciNet  Google Scholar 

  47. Ao, Z. et al. Understanding immune-driven brain aging by human brain organoid microphysiological analysis platform. Adv. Sci. 9, e2200475 (2022).

    Article  Google Scholar 

Download references

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

Authors
  1. Hongwei Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zheng Ao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chunhui Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhuhao Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hongcheng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jason Tchieu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mingxia Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ken Mackie
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Feng Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Ethics declarations

Competing interests

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.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41928-023-01069-w

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing