TY - JOUR
T1 - First Organoid Intelligence (OI) workshop to form an OI community
AU - Morales Pantoja, Itzy E.
AU - Smirnova, Lena
AU - Muotri, Alysson R.
AU - Wahlin, Karl J.
AU - Kahn, Jeffrey
AU - Boyd, J. Lomax
AU - Gracias, David H.
AU - Harris, Timothy D.
AU - Cohen-Karni, Tzahi
AU - Caffo, Brain S.
AU - Szalay, Alexander S.
AU - Fang, Han
AU - Zack, Donald J.
AU - Etienne-Cummings, Ralph
AU - Akwaboah, Akwasi
AU - Romero, July Carolina
AU - Alam El Din, Dowlette Mary
AU - Plotkin, Jesse D.
AU - Paulhamus, Barton L.
AU - Johnson, Erik C.
AU - Gilbert, Frederic
AU - Curley, J. Lowry
AU - Cappiello, Ben
AU - Schwamborn, Jens C.
AU - Hill, Eric J.
AU - Roach, Paul
AU - Tornero, Daniel
AU - Krall, Caroline
AU - Parri, Rheinallt
AU - Sillé, Fenna
AU - Levchenko, Andre
AU - Jabbour, Rabih E.
AU - Kagan, Brett J.
AU - Berlinicke, Cynthia A.
AU - Huang, Qi
AU - Maertens, Alexandra
AU - Herrmann, Kathrin
AU - Tsaioun, Katya
AU - Dastgheyb, Raha
AU - Habela, Christa Whelan
AU - Vogelstein, Joshua T.
AU - Hartung, Thomas
N1 - Funding Information:
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.
Funding Information:
The workshop was financially supported by the Doerenkamp-Zbinden Foundation through the Transatlantic Thinktank for Toxicology (t). 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. 4 Contribution statement
Publisher Copyright:
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.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Organoid Intelligence
KW - artificial intelligence
KW - biological computing
KW - brain
KW - cognition
KW - electrophysiology
KW - microphysiological systems
UR - http://www.scopus.com/inward/record.url?scp=85150177741&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85150177741&partnerID=8YFLogxK
U2 - 10.3389/frai.2023.1116870
DO - 10.3389/frai.2023.1116870
M3 - Review article
C2 - 36925616
AN - SCOPUS:85150177741
SN - 2624-8212
VL - 6
JO - Frontiers in Artificial Intelligence
JF - Frontiers in Artificial Intelligence
M1 - 1116870
ER -