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1. TAVRNN: Temporal Attention-enhanced Variational Graph RNN Captures Neural Dynamics and Behavior

Author

Khajehnejad, Moein, Habibollahi, Forough, Khajehnejad, Ahmad, Kagan, Brett J., and Razi, Adeel

Subjects
Quantitative Biology - Neurons and Cognition, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract

We introduce Temporal Attention-enhanced Variational Graph Recurrent Neural Network (TAVRNN), a novel framework for analyzing the evolving dynamics of neuronal connectivity networks in response to external stimuli and behavioral feedback. TAVRNN captures temporal changes in network structure by modeling sequential snapshots of neuronal activity, enabling the identification of key connectivity patterns. Leveraging temporal attention mechanisms and variational graph techniques, TAVRNN uncovers how connectivity shifts align with behavior over time. We validate TAVRNN on two datasets: in vivo calcium imaging data from freely behaving rats and novel in vitro electrophysiological data from the DishBrain system, where biological neurons control a simulated environment during the game of pong. We show that TAVRNN outperforms previous baseline models in classification, clustering tasks and computational efficiency while accurately linking connectivity changes to performance variations. Crucially, TAVRNN reveals that high game performance in the DishBrain system correlates with the alignment of sensory and motor subregion channels, a relationship not evident in earlier models. This framework represents the first application of dynamic graph representation of electrophysiological (neuronal) data from DishBrain system, providing insights into the reorganization of neuronal networks during learning. TAVRNN's ability to differentiate between neuronal states associated with successful and unsuccessful learning outcomes, offers significant implications for real-time monitoring and manipulation of biological neuronal systems., Comment: 31 pages, 6 figures, 4 supplemental figures, 4 tables, 8 supplemental tables

Published
2024

2. Biological Neurons Compete with Deep Reinforcement Learning in Sample Efficiency in a Simulated Gameworld

Author

Khajehnejad, Moein, Habibollahi, Forough, Paul, Aswin, Razi, Adeel, and Kagan, Brett J.

Subjects
Quantitative Biology - Neurons and Cognition, Computer Science - Artificial Intelligence
Abstract

How do biological systems and machine learning algorithms compare in the number of samples required to show significant improvements in completing a task? We compared the learning efficiency of in vitro biological neural networks to the state-of-the-art deep reinforcement learning (RL) algorithms in a simplified simulation of the game `Pong'. Using DishBrain, a system that embodies in vitro neural networks with in silico computation using a high-density multi-electrode array, we contrasted the learning rate and the performance of these biological systems against time-matched learning from three state-of-the-art deep RL algorithms (i.e., DQN, A2C, and PPO) in the same game environment. This allowed a meaningful comparison between biological neural systems and deep RL. We find that when samples are limited to a real-world time course, even these very simple biological cultures outperformed deep RL algorithms across various game performance characteristics, implying a higher sample efficiency. Ultimately, even when tested across multiple types of information input to assess the impact of higher dimensional data input, biological neurons showcased faster learning than all deep reinforcement learning agents., Comment: 13 Pages, 6 Figures - 38 Supplementary Pages, 6 Supplementary Figures, 4 Supplementary Tables

Published
2024

3. Active Inference and Intentional Behaviour

Author

Friston, Karl J., Salvatori, Tommaso, Isomura, Takuya, Tschantz, Alexander, Kiefer, Alex, Verbelen, Tim, Koudahl, Magnus, Paul, Aswin, Parr, Thomas, Razi, Adeel, Kagan, Brett, Buckley, Christopher L., and Ramstead, Maxwell J. D.

Subjects
Quantitative Biology - Neurons and Cognition, Computer Science - Machine Learning
Abstract

Recent advances in theoretical biology suggest that basal cognition and sentient behaviour are emergent properties of in vitro cell cultures and neuronal networks, respectively. Such neuronal networks spontaneously learn structured behaviours in the absence of reward or reinforcement. In this paper, we characterise this kind of self-organisation through the lens of the free energy principle, i.e., as self-evidencing. We do this by first discussing the definitions of reactive and sentient behaviour in the setting of active inference, which describes the behaviour of agents that model the consequences of their actions. We then introduce a formal account of intentional behaviour, that describes agents as driven by a preferred endpoint or goal in latent state-spaces. We then investigate these forms of (reactive, sentient, and intentional) behaviour using simulations. First, we simulate the aforementioned in vitro experiments, in which neuronal cultures spontaneously learn to play Pong, by implementing nested, free energy minimising processes. The simulations are then used to deconstruct the ensuing predictive behaviour, leading to the distinction between merely reactive, sentient, and intentional behaviour, with the latter formalised in terms of inductive planning. This distinction is further studied using simple machine learning benchmarks (navigation in a grid world and the Tower of Hanoi problem), that show how quickly and efficiently adaptive behaviour emerges under an inductive form of active inference., Comment: 33 pages, 9 figures

Published
2023

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

Author

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

Subjects
Information and Computing Sciences, Engineering, Biomedical Engineering, Neurosciences, Neurological, microphysiological systems, brain, electrophysiology, cognition, artificial intelligence, biological computing, Organoid Intelligence, Control engineering, mechatronics and robotics, Artificial intelligence, Machine learning
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.

Published
2023

9. Scientific communication and the semantics of sentience

Author

Kagan, Brett J., primary, Razi, Adeel, additional, Bhat, Anjali, additional, Kitchen, Andy C., additional, Tran, Nhi T., additional, Habibollahi, Forough, additional, Khajehnejad, Moein, additional, Parker, Bradyn J., additional, Rollo, Ben, additional, and Friston, Karl J., additional

Published
2023
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10. First Organoid Intelligence (OI) workshop to form an OI community.

Author

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

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.

Published
2023

11. The Baltimore declaration toward the exploration of organoid intelligence

Author

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

Published
2023

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

Author

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

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.

Published
2023

13. The Baltimore declaration toward the exploration of organoid intelligence

Author

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

Published
2023
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14. Organoid intelligence (OI): the new frontier in biocomputing and intelligence-in-a-dish

Author

Smirnova, Lena, primary, Caffo, Brian S., additional, Gracias, David H., additional, Huang, Qi, additional, Morales Pantoja, Itzy E., additional, Tang, Bohao, additional, Zack, Donald J., additional, Berlinicke, Cynthia A., additional, Boyd, J. Lomax, additional, Harris, Timothy D., additional, Johnson, Erik C., additional, Kagan, Brett J., additional, Kahn, Jeffrey, additional, Muotri, Alysson R., additional, Paulhamus, Barton L., additional, Schwamborn, Jens C., additional, Plotkin, Jesse, additional, Szalay, Alexander S., additional, Vogelstein, Joshua T., additional, Worley, Paul F., additional, and Hartung, Thomas, additional

Published
2023
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15. First Organoid Intelligence (OI) workshop to form an OI community

Author

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

Published
2023
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18. Transcript of: ActInf GuestStream #031 ~ Brett Kagan & Adeel Razi, 'Free Energy Principle & Active Inference in Synthetic Biological Intelligence'

Author

Kagan, Brett, Razi, Adeel, and Friedman, Daniel

Abstract

Discussion, with authors, of the 2022 paper "In vitro neurons learn and exhibit sentience when embodied in a simulated game-world" by Brett J. Kagan, Andy C. Kitchen, Nhi T. Tran, Forough Habibollahi, Moein Khajehnejad, Bradyn J. Parker, Anjali Bhat, Ben Rollo, Adeel Razi, & Karl J. Friston (2022). https://www.sciencedirect.com/science/article/pii/S0896627322008066 https://doi.org/10.1016/j.neuron.2022.09.001 ActInf GuestStream Session 031.1, December 7, 2022 https://www.youtube.com/watch?v=57-aRIjUrC4

Published
2022
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26. Investigating the therapeutic efficacy of cellular therapy for neonatal ischemic injury

Author

Kagan, Brett J. and Kagan, Brett J.

Abstract

Improvements in medical science have drastically increased survival for infants suffering from neonatal arterial ischemic stroke (NAIS). However, the failure to reverse the associated injuries has resulted in increased numbers of patients suffering from chronic neurodevelopmental disability. Potentially, stem cell-based therapy offers the greatest potential as a viable therapeutic tool not only for neuro-protection but importantly also for neural repair and restabilising lost connections. However, there is still vital investigations to be performed in the optimisation of cellular therapy as a medically effective tool, along with gaining a more comprehensive understanding of the mechanisms of actions and difference utility between different cell types. This research firstly aimed to develop a technically simple, high-throughput rodent model of NAIS affecting the cortex to be able to effectively test cellular therapy. Refined behavioural and cognitive testing procedures were also required to test the functional recovery offered by this therapy and this work aimed to develop and assess the validity of these methods. Ultimately, this work aimed to test the therapeutic efficacy human pluripotent cells differentiated to a cortical progenitor cells as a method of directly replacing damaged tissue. Moreover, this therapy was directly compared to the use of human umbilical cord blood cells (hUCBCs) as a therapy, with the aim of determining the relative value each of these therapies as potential treatments following NAIS. A technically simple, high-throughput rodent model of NAIS was developed. Utilising this model, two separate studies were conducted testing the therapeutic efficacy of pluripotent cells differentiated to cortical progenitor cells vs multipotent UBCs as a treatment of neonatal ischemic stroke. Holistically, the data indicated that grafts of cortical progenitors but not hUCBCs improved behavioural outcomes compared to ischemia alone. Moreover, a comparable lev

Published
2018

29. Human Neural Organoid Microphysiological Systems Show the Building Blocks Necessary for Basic Learning and Memory.

Author

El Din DA, Moenkemoeller L, Loeffler A, Habibollahi F, Schenkman J, Mitra A, van der Molen T, Ding L, Laird J, Schenke M, Johnson EC, Kagan BJ, Hartung T, and Smirnova L

Abstract

Brain Microphysiological Systems including neural organoids derived from human induced pluripotent stem cells offer a unique lens to study the intricate workings of the human brain. This paper investigates the foundational elements of learning and memory in neural organoids, also known as Organoid Intelligence by quantifying immediate early gene expression, synaptic plasticity, neuronal network dynamics, and criticality to demonstrate the utility of these organoids in basic science research. Neural organoids showed synapse formation, glutamatergic and GABAergic receptor expression, immediate early gene expression basally and evoked, functional connectivity, criticality, and synaptic plasticity in response to theta-burst stimulation. In addition, pharmacological interventions on GABAergic and glutamatergic receptors, and input specific theta-burst stimulation further shed light on the capacity of neural organoids to mirror synaptic modulation and short-term potentiation, demonstrating their potential as tools for studying neurophysiological and neurological processes and informing therapeutic strategies for diseases., Competing Interests: Declaration of interests T.H. is named inventor on a patent by Johns Hopkins University on the production of organoids, which is licensed to Axo-Sim, New Orleans, LA, USA. T.H. and L.S. are consultants for AxoSim, New Orleans, and T.H. is also a consultant for AstraZeneca and American Type Culture Collection (ATCC) on advanced cell culture methods. B.J.K. is a named inventor on patents by CCLabs Pty Ltd trading as Cortical Labs on the use of biological neural systems for intelligent purposes. B.J.K., F.H, and A.L are employees of Cortical Labs. B.J.K. and A.L. are shareholders of Cortical Labs. J.L is a data science consultant for Vindhya Data Science specializing in bioinformatics analysis. The rest of the authors declare no conflict of interest.

Published
2024
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30. Integrating levels of bone growth control: From stem cells to body proportions.

Author

Kagan BJ and Rosello-Diez A

Subjects
Animals, Humans, Stem Cells physiology, Bone Development, Cell Differentiation, Osteogenesis, Stem Cells cytology
Abstract

The study of the mechanisms controlling organ size during development and regeneration is critical to understanding how complex life arises from cooperating single cells. Long bones are powerful models in this regard, as their size depends on a scaffold made from another tissue (cartilage, composed of chondrocytes), and both tissues interact during the growth period. Investigating long bone growth offers a valuable window into the processes that integrate internal and external cues to yield finely controlled size of organs. Within the cellular and molecular pathways that control bone growth, the regulation of stem-cell renewal, along with amplification and differentiation of their progeny, are key to understanding normal and perturbed long-bone development. The phenomenon of "catch-up" growth-where cellular hyperproliferation occurs following injury to restore a normal growth trajectory-reveals key aspects of this regulation, such as the fact that bone growth is target-seeking. The control mechanisms that lead to this behavior are either bottom-up or top-down, and the interaction between these modes is likely critical to achieve a highly nuanced, yet flexible, degree of control. The role of cartilage-intrinsic mechanisms has been well studied, establishing a very solid groundwork for this field. However, addressing the unanswered questions of bone growth arguably requires new hypotheses and approaches. Future research could for example address to what extent extrinsic signals and cells, as well as communication with other tissues, modulate intra-limb and inter-organ growth coordination. This article is categorized under: Adult Stem Cells, Tissue Renewal, and Regeneration > Tissue Stem Cells and Niches Establishment of Spatial and Temporal Patterns > Regulation of Size, Proportion, and Timing Vertebrate Organogenesis > Musculoskeletal and Vascular., (© 2020 Wiley Periodicals LLC.)

Published
2021
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