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.
|Number of pages||15|
|Journal||Frontiers in Artificial Intelligence|
|Early online date||28 Feb 2023|
|Publication status||Published - 28 Feb 2023|
Copyright © 2023 Morales Pantoja, Smirnova, Muotri, Wahlin, Kahn, Boyd, Gracias, Harris, Cohen-Karni, Caffo, Szalay, Han, Zack, Etienne-Cummings, Akwaboah, Romero, Alam El Din, Plotkin, Paulhamus, Johnson, Gilbert, Curley, Cappiello, Schwamborn, Hill, Roach, Tornero, Krall, Parri, Sillé, Levchenko, Jabbour, Kagan, Berlinicke, Huang, Maertens, Herrmann, Tsaioun, Dastgheyb, Habela, Vogelstein and Hartung. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
The workshop was financially supported by the Doerenkamp-Zbinden Foundation through the Transatlantic Thinktank for Toxicology (t4). The workshop was cohosted the Johns Hopkins Whiting School of Engineering and Frontiers. Preliminary work was financed by a Johns Hopkins Discovery Grant [TH, (PI), BC, DG, and LS] and T32ES007141-38 grant.
- Artificial Intelligence
- microphysiological systems
- artificial intelligence
- biological computing
- Organoid Intelligence