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First Organoid Intelligence (OI) workshop to form an OI community

Frontiers in Artificial Intelligence · 42 authors, 40 centres

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The first Organoid Intelligence (OI) workshop established a multidisciplinary community to explore using brain organoids for biocomputing. The workshop outlined a roadmap for OI, including ethical frameworks, technological challenges, and potential applications in computing and neurological research. The key finding is that OI could revolutionize computing by leveraging the brain's efficiency, but requires careful ethical consideration and standardized protocols.

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**Background:** The human brain is an extremely efficient computational system, processing vast amounts of information while consuming only 20 watts of power. Recent advances in stem cell technology have led to the development of three-dimensional (3D) brain organoids that recapitulate human brain functionality better than traditional 2D cultures. Organoid Intelligence (OI) aims to harness the innate biological capabilities of brain organoids for biocomputing and synthetic intelligence by interfacing them with computer technology. The first Organoid Intelligence workshop was held at Johns Hopkins University on February 22–24, 2022, to form an OI community and lay the groundwork for OI as a new scientific discipline.

**Methods:** The workshop brought together members of the global scientific community to present and discuss OI across four tracks: ethics, brain organoids, electrophysiology, and data analysis. The workshop included presentations on the current state of organoid technology, ethical considerations, electrophysiological interfacing, and data analysis methods. Workgroup sessions on the second day addressed challenges and strategies, with summaries reported on the third day. The workshop also introduced the Baltimore Declaration toward OI.

**Key Results:** The workshop highlighted several key developments and challenges. In the ethics track, a pilot project (SATORI) conducted 60 interviews with patients and outpatients, showing broad support for organoid research but conditional responses for brain organoids, emphasizing the need for boundaries and thorough consent processes. In the brain organoids track, Alysson Muotri presented cortical organoids that exhibit neuro-oscillations mimicking prenatal to postnatal human brain EEGs. Karl Wahlin discussed retinal organoids as sensory models, and Lena Smirnova presented the BrainSphere model with reproducible size and cellular composition, including myelination. In the electrophysiology track, David Gracias introduced shell MEAs for 3D recording, John Rogers presented 3D mesoscale structures for bioelectronic interfaces, Tim Harris discussed Neuropixels probes with 384 channels across 960 sites, and Tzahi Cohen-Karni introduced Organ-on-e-Chip for 3D recording. In the data analysis track, Brian Caffo emphasized repeatability and validity, Fang Han discussed machine learning for separating meaningful outputs from inputs, and Alex Szalay presented the SciServer for storing petabytes of data. The workshop identified challenges including the need for consistent terminology, defining intelligence in organoids, improving organoid fitness and longevity, and creating standardized benchmarks. The participants agreed on the Baltimore Declaration toward OI.

**Clinical Implications:** OI has the potential to revolutionize computing, neurological research, and drug development. It could be used to study conditions such as Asperger's, impaired neurodevelopment, and dementia, and to develop better treatments. The workshop emphasized the importance of embedded ethics, with ethicists included in research teams. The development of OI could lead to new ways of brain/machine interactions, better clinical outcomes, and improved therapeutics. The establishment of standards and reproducible protocols is critical for the clinical translation of OI technologies.

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neuroscience

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ORIGINAL PAPER

PMC10013972

First Organoid Intelligence (OI) workshop to form an OI community

Frontiers in Artificial Intelligence

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ABSTRACT

The brain is arguably the most powerful computation system known. It is extremely efficient in processing large amounts of information and can discern signals from noise, adapt, and filter faulty information all while running on only 20 watts of power. The human brain's processing efficiency, progressive learning, and plasticity are unmatched by any computer system. Recent advances in stem cell technology have elevated the field of cell culture to higher levels of complexity, such as the development of three-dimensional (3D) brain organoids that recapitulate human brain functionality better than traditional monolayer cell systems. Organoid Intelligence (OI) aims to harness the innate biological capabilities of brain organoids for biocomputing and synthetic intelligence by interfacing them with computer technology. With the latest strides in stem cell technology, bioengineering, and machine learning, we can explore the ability of brain organoids to compute, and store given information (input), execute a task (output), and study how this affects the structural and functional connections in the organoids themselves. Furthermore, understanding how learning generates and changes patterns of connectivity in organoids can shed light on the early stages of cognition in the human brain. Investigating and understanding these concepts is an enormous, multidisciplinary endeavor that necessitates the engagement of both the scientific community and the public. Thus, on Feb 22–24 of 2022, the Johns Hopkins University held the first Organoid Intelligence Workshop to form an OI Community and to lay out the groundwork for the establishment of OI as a new scientific discipline. The potential of OI to revolutionize computing, neurological research, and drug development was discussed, along with a vision and roadmap for its development over the coming decade.

CITATION EXPORT

Itzy E. Morales Pantoja, Lena Smirnova, Alysson R. Muotri et al.. First Organoid Intelligence (OI) workshop to form an OI community. Frontiers in Artificial Intelligence. (2023). https://doi.org/10.3389/frai.2023.1116870

AUTHORS

42 · 40 CENTRES

Itzy E. Morales Pantoja, Lena Smirnova, Alysson R. Muotri, Karl J. Wahlin, Jeffrey Kahn, J. Lomax Boyd, David H. Gracias, Timothy D. Harris, Tzahi Cohen-Karni, Brian S. Caffo, Alexander S. Szalay, Fang Han, Donald J. Zack, Ralph Etienne-Cummings, Akwasi Akwaboah, July Carolina Romero, Dowlette-Mary Alam El Din, Jesse D. Plotkin, Barton L. Paulhamus, Erik C. Johnson, Frederic Gilbert, J. Lowry Curley, Ben Cappiello, Jens C. Schwamborn, Eric J. Hill, Paul Roach, Daniel Tornero, Caroline Krall, Rheinallt Parri, Fenna Sillé, Andre Levchenko, Rabih E. Jabbour, Brett J. Kagan, Cynthia A. Berlinicke, Qi Huang, Alexandra Maertens, Kathrin Herrmann, Katya Tsaioun, Raha Dastgheyb, Christa Whelan Habela, Joshua T. Vogelstein, Thomas Hartung

1Center for Alternatives to Animal Testing (CAAT), Department of Environmental Health and Engineering, Bloomberg School of Public Health and Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
2Department of Pediatrics and Cellular and Molecular Medicine, School of Medicine, University of California, San Diego, San Diego, CA, United States
3Center for Academic Research and Training in Anthropogeny (CARTA), Archealization Center (ArchC), Kavli Institute for Brain and Mind, University of California, San Diego, San Diego, CA, United States
4Viterbi Family Department of Ophthalmology & the Shiley Eye Institute, UC San Diego, La Jolla, CA, United States
5Berman Institute of Bioethics, Johns Hopkins University, Baltimore, MD, United States
6Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States
7Department of Chemistry, Johns Hopkins University, Baltimore, MD, United States
8Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, United States
9Laboratory for Computational Sensing and Robotics (LCSR), Johns Hopkins University, Baltimore, MD, United States
10Center for Microphysiological Systems (MPS), Johns Hopkins University School of Medicine, Baltimore, MD, United States
11Oncology and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
12Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
13Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, United States
14Departments of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
15Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
16Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
17Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
18Department of Physics and Astronomy, Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD, United States
19Mark Foundation Center for Advanced Genomics and Imaging, Johns Hopkins University, Baltimore, MD, United States
20Department of Statistics and Economics, University of Washington, Seattle, WA, United States
21Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
22Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, MD, United States
23Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
24Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, United States
25Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States
26Department of Research and Exploratory Development, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States
27Philosophy Program, School of Humanities, University of Tasmania, Hobart, TAS, Australia
28AxoSim Inc., New Orleans, LA, United States
29Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
30School of Biosciences, College of Health and Life Sciences, Aston University, Birmingham, United Kingdom
31Department of Chemistry, School of Science, Loughborough University, Loughborough, Leicestershire, United Kingdom
32Department of Biomedical Sciences, Institute of Neuroscience, University of Barcelona, Barcelona, Spain
33Clinic Hospital August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain
34Department of Molecular and Comparative Pathobiology, Johns Hopkins University, Baltimore, MD, United States
35Aston Pharmacy School, College of Health and Life Sciences, Aston University, Birmingham, United Kingdom
36Department of Biomedical Engineering, Yale Systems Biology Institute, Yale University, New Haven, CT, United States
37Department of Bioscience and Biotechnology, University of Maryland Global Campus, Rockville, MD, United States
38Cortical Labs, Melbourne, VIC, Australia
39Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, United States
40Center for Alternatives to Animal Testing (CAAT)-Europe, University of Konstanz, Konstanz, Germany