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

      review-article
      1 , 1 , 2 , 3 , 4 , 5 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 25 , 1 , 1 , 1 , 26 , 26 , 27 , 28 , 28 , 29 , 30 , 31 , 32 , 33 , 1 , 34 , 35 , 1 , 36 , 37 , 38 , 21 , 6 , 1 , 1 , 1 , 39 , 39 , 12 , 1 , 40 , * ,
      Frontiers in Artificial Intelligence
      Frontiers Media S.A.
      microphysiological systems, brain, electrophysiology, cognition, artificial intelligence, biological computing, Organoid 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.

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          Most cited references57

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          Fully integrated silicon probes for high-density recording of neural activity

          Sensory, motor and cognitive operations involve the coordinated action of large neuronal populations across multiple brain regions in both superficial and deep structures. Existing extracellular probes record neural activity with excellent spatial and temporal (sub-millisecond) resolution, but from only a few dozen neurons per shank. Optical Ca2+ imaging offers more coverage but lacks the temporal resolution needed to distinguish individual spikes reliably and does not measure local field potentials. Until now, no technology compatible with use in unrestrained animals has combined high spatiotemporal resolution with large volume coverage. Here we design, fabricate and test a new silicon probe known as Neuropixels to meet this need. Each probe has 384 recording channels that can programmably address 960 complementary metal–oxide–semiconductor (CMOS) processing-compatible low-impedance TiN sites that tile a single 10-mm long, 70 × 20-μm cross-section shank. The 6 × 9-mm probe base is fabricated with the shank on a single chip. Voltage signals are filtered, amplified, multiplexed and digitized on the base, allowing the direct transmission of noise-free digital data from the probe. The combination of dense recording sites and high channel count yielded well-isolated spiking activity from hundreds of neurons per probe implanted in mice and rats. Using two probes, more than 700 well-isolated single neurons were recorded simultaneously from five brain structures in an awake mouse. The fully integrated functionality and small size of Neuropixels probes allowed large populations of neurons from several brain structures to be recorded in freely moving animals. This combination of high-performance electrode technology and scalable chip fabrication methods opens a path towards recording of brain-wide neural activity during behaviour.
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            Generation of cerebral organoids from human pluripotent stem cells.

            Human brain development exhibits several unique aspects, such as increased complexity and expansion of neuronal output, that have proven difficult to study in model organisms. As a result, in vitro approaches to model human brain development and disease are an intense area of research. Here we describe a recently established protocol for generating 3D brain tissue, so-called cerebral organoids, which closely mimics the endogenous developmental program. This method can easily be implemented in a standard tissue culture room and can give rise to developing cerebral cortex, ventral telencephalon, choroid plexus and retinal identities, among others, within 1-2 months. This straightforward protocol can be applied to developmental studies, as well as to the study of a variety of human brain diseases. Furthermore, as organoids can be maintained for more than 1 year in long-term culture, they also have the potential to model later events such as neuronal maturation and survival.
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              Memristive devices for computing.

              Memristive devices are electrical resistance switches that can retain a state of internal resistance based on the history of applied voltage and current. These devices can store and process information, and offer several key performance characteristics that exceed conventional integrated circuit technology. An important class of memristive devices are two-terminal resistance switches based on ionic motion, which are built from a simple conductor/insulator/conductor thin-film stack. These devices were originally conceived in the late 1960s and recent progress has led to fast, low-energy, high-endurance devices that can be scaled down to less than 10 nm and stacked in three dimensions. However, the underlying device mechanisms remain unclear, which is a significant barrier to their widespread application. Here, we review recent progress in the development and understanding of memristive devices. We also examine the performance requirements for computing with memristive devices and detail how the outstanding challenges could be met.
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                Author and article information

                Contributors
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                Journal
                Front Artif Intell
                Front Artif Intell
                Front. Artif. Intell.
                Frontiers in Artificial Intelligence
                Frontiers Media S.A.
                2624-8212
                28 February 2023
                2023
                : 6
                : 1116870
                Affiliations
                [1] 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
                [2] 2Department of Pediatrics and Cellular and Molecular Medicine, School of Medicine, University of California, San Diego , San Diego, CA, United States
                [3] 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
                [4] 4Viterbi Family Department of Ophthalmology & the Shiley Eye Institute, UC San Diego , La Jolla, CA, United States
                [5] 5Berman Institute of Bioethics, Johns Hopkins University , Baltimore, MD, United States
                [6] 6Department of Chemical and Biomolecular Engineering, Johns Hopkins University , Baltimore, MD, United States
                [7] 7Department of Chemistry, Johns Hopkins University , Baltimore, MD, United States
                [8] 8Department of Materials Science and Engineering, Johns Hopkins University , Baltimore, MD, United States
                [9] 9Laboratory for Computational Sensing and Robotics (LCSR), Johns Hopkins University , Baltimore, MD, United States
                [10] 10Center for Microphysiological Systems (MPS), Johns Hopkins University School of Medicine , Baltimore, MD, United States
                [11] 11Oncology and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine , Baltimore, MD, United States
                [12] 12Department of Biomedical Engineering, Johns Hopkins University , Baltimore, MD, United States
                [13] 13Janelia Research Campus, Howard Hughes Medical Institute , Ashburn, VA, United States
                [14] 14Departments of Biomedical Engineering, Carnegie Mellon University , Pittsburgh, PA, United States
                [15] 15Department of Materials Science and Engineering, Carnegie Mellon University , Pittsburgh, PA, United States
                [16] 16Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University , Baltimore, MD, United States
                [17] 17Department of Computer Science, Whiting School of Engineering, Johns Hopkins University , Baltimore, MD, United States
                [18] 18Department of Physics and Astronomy, Krieger School of Arts and Sciences, Johns Hopkins University , Baltimore, MD, United States
                [19] 19Mark Foundation Center for Advanced Genomics and Imaging, Johns Hopkins University , Baltimore, MD, United States
                [20] 20Department of Statistics and Economics, University of Washington , Seattle, WA, United States
                [21] 21Department of Ophthalmology, Johns Hopkins University School of Medicine , Baltimore, MD, United States
                [22] 22Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine , Baltimore, MD, United States
                [23] 23Department of Genetic Medicine, Johns Hopkins University School of Medicine , Baltimore, MD, United States
                [24] 24Department of Neuroscience, Johns Hopkins University School of Medicine , Baltimore, MD, United States
                [25] 25Department of Electrical and Computer Engineering, Johns Hopkins University , Baltimore, MD, United States
                [26] 26Department of Research and Exploratory Development, Johns Hopkins University Applied Physics Laboratory , Laurel, MD, United States
                [27] 27Philosophy Program, School of Humanities, University of Tasmania , Hobart, TAS, Australia
                [28] 28AxoSim Inc. , New Orleans, LA, United States
                [29] 29Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg , Esch-sur-Alzette, Luxembourg
                [30] 30School of Biosciences, College of Health and Life Sciences, Aston University , Birmingham, United Kingdom
                [31] 31Department of Chemistry, School of Science, Loughborough University, Loughborough , Leicestershire, United Kingdom
                [32] 32Department of Biomedical Sciences, Institute of Neuroscience, University of Barcelona , Barcelona, Spain
                [33] 33Clinic Hospital August Pi i Sunyer Biomedical Research Institute (IDIBAPS) , Barcelona, Spain
                [34] 34Department of Molecular and Comparative Pathobiology, Johns Hopkins University , Baltimore, MD, United States
                [35] 35Aston Pharmacy School, College of Health and Life Sciences, Aston University , Birmingham, United Kingdom
                [36] 36Department of Biomedical Engineering, Yale Systems Biology Institute, Yale University , New Haven, CT, United States
                [37] 37Department of Bioscience and Biotechnology, University of Maryland Global Campus , Rockville, MD, United States
                [38] 38Cortical Labs , Melbourne, VIC, Australia
                [39] 39Department of Neurology, Johns Hopkins School of Medicine , Baltimore, MD, United States
                [40] 40Center for Alternatives to Animal Testing (CAAT)-Europe, University of Konstanz , Konstanz, Germany
                Author notes

                Edited by: Huixiao Hong, United States Food and Drug Administration, United States

                Reviewed by: Hui Liu, University of Bremen, Germany; Jonathan Andrew Miller, Onto Innovation, United States

                *Correspondence: Thomas Hartung thartung@ 123456jhsph.edu

                This article was submitted to Organoid Intelligence, a section of the journal Frontiers in Artificial Intelligence

                Article
                10.3389/frai.2023.1116870
                10013972
                36925616
                60f04fac-d129-4c04-b6d4-c4a67a6e94d8
                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.

                History
                : 05 December 2022
                : 08 February 2023
                Page count
                Figures: 3, Tables: 0, Equations: 0, References: 61, Pages: 15, Words: 12372
                Funding
                The workshop was financially supported by the Doerenkamp-Zbinden Foundation through the Transatlantic Thinktank for Toxicology (t 4). 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.
                Categories
                Artificial Intelligence
                Review

                microphysiological systems,brain,electrophysiology,cognition,artificial intelligence,biological computing,organoid intelligence

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