In parallel with recent developments in machine learning such as GPT-4, a group of scientists has recently proposed the use of neural tissue itself, carefully grown to recreate animal brain structures, as a computational substrate. After all, if AI is inspired by neurological systems, what better way to make computers than a real neurological system? Gathering developments from the fields of computer science, electrical engineering, neurobiology, electrophysiology and pharmacology, the authors propose a new research initiative they call organoid intelligence.
OI is a collective effort to advance the use of brain organoids, spherical masses of brain tissue grown from stem cells for computation, drug research, and as a model for studying at a small scale how a complete brain can function. In other words, organoids offer an opportunity to better understand the brain, and OI aims to use that knowledge to develop neurobiological computational systems that learn from less data and with less energy than silicon hardware.
The development of organoids was made possible by two bioengineering breakthroughs: induced pluripotent stem cells and 3D cell culture techniques.
Taking the existing field of neuromorphic computing, where the structure of neurons and the connections between them are studied and mimicked in silicon architectures, OI extends the engineering analogy with the opportunity to directly program desired behaviors into the activating task of animal brain cell cultures.
Organoids typically measure 500 micrometers in diameter, roughly the thickness of the fingernail. As organoids develop, the researchers say, their constituent neurons begin to interconnect in networks and patterns of activity that mimic the structures of different brain regions. The development of the organoid field was made possible by two breakthroughs in bioengineering: induced pluripotent stem cells (IPSCs) and 3D cell culture techniques. IPSCs are stem cells capable of developing into any cell found in an animal’s body that are created by transforming an adult cell into a stem cell. These induced stem cells are then biochemically coaxed into the specific neurons and glia needed to construct a given organoid. More recently developed 3D scaffolding methods allow biologists to grow iPSC-derived neural tissues both vertically and horizontally, allowing the organoids to develop the interneuronal networks seen in an animal’s brain. Scientists have studied 2D cultures for decades, but monolayer tissues are unable to grow into brain networks the way organoids do.
The networks make organoids a powerful model for understanding and potentially exploiting the dynamics of brain activity. Jens Schwamborn, professor of developmental and cell biology at the University of Luxembourg, is using organoids to study the development of neurological disorders such as Parkinson’s disease. We have summarized the key features of the pathology. We can see the loss of dopaminergic neurons, we see the appearance of disease-relevant protein aggregates, said Schwamborn, whose lab has developed an organoid model of Parkinson’s. These platforms allow researchers to study, on a small scale, Parkinson’s development in a cellular network context that monolayer cultures cannot. That’s the main benefit, Schwarnborn says. We can see features of the disease that we know occur in patients but have so far been unable to recapitulate in the laboratory. Now, finally, we can do it.
We are not teaching cells how to do this. [Organoids] they end up with the organization of structures in the brain. I think that’s the power: Computational power comes from that organization.
Alysson Muotri, University of California, San Diego
Just as organoids themselves are the product of advances in bioengineering, their utility as models for neurological function is the product of many other innovations in biochemistry, electrophysiology, and microfluidics. Researchers can now guide organoid development more reliably and precisely than they could even half a decade ago, and can use that specificity to create organoids that mimic the network structure and cellular composition of specific cortical and subcortical structures. Alysson Muotri, a professor of pediatrics and molecular medicine at the University of California, San Diego, believes these structures may give them the information-processing capabilities of brain tissue. In 3D, you see all this extra organization that you don’t see in 2D. This is genetically coded. We are not teaching cells how to do this. They end up with the organization of structures in the brain. I think that’s the power: Computational power comes from that organization.
Having consistent and sustainable organoids also allows scientists to make meaningful measurements of the activity of the neurons within them. Multi-electrode arrays (MEAs) are panels of tiny electrodes that can measure and stimulate the electrical activity of neurons near the surface of an organoid. Flexible MEAs that can wrap around an organoid mass are able to record from the entire surface, rather than just from the bottom layer of neurons in contact with the petri dish. By analyzing those recordings, scientists can deduce how all those neurons talk to each other. Through a series of signal processing techniques called causal modeling, researchers can produce maps of connections between neurons that make up networks of functional organoid structure. These network maps can then be used to track how information is processed by the developing mass of neural tissue.
By conditioning populations of neurons within organoids to respond in a consistent and predictable way to certain electrical inputs, scientists speculate they can transform organoid systems into organic processing units that can harness the apparent information-processing capabilities of neural tissue to create flexible and powerful computer systems.
Cortical Labs, a Melbourne-based biotech startup, is launching Dishbrain, the first trainable neurobiological computing platform. The company aims to provide programmable single-layer 2D neural cultures that are already proven to reliably learn digital input/output patterns such as playing the classic video game pongto end users as a cloud service. Brett Kagan, the company’s chief science officer, says the company plans to have the service up and running by the end of the year. We should have, by the end of this year, a beta system so that people can either through the cloud or by collaborating with us for internal use, access and be able to run very simple environments, he said.
While similar organoid-on-chip computer systems are not yet available, the OI team is optimistic about their rate of progress. Professor Muotri thinks we could see organoid computing systems developed within the decade. We could see a prototype in the next two to three years, he said. For things to become more reproducible, with all the necessary tools, it will take five or 10 years.
The group’s research was recently published in the journal Frontiers of neuroscience.
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