“Organic Intelligence” – revolutionary biocomputers powered by human brain cells

Scientists are collaborating across multiple fields to create biocomputers that use 3D cultures of brain cells, called brain organoids, as biocomputers. They laid out their plan to achieve this goal in the scientific journal frontiers in science.

Despite the impressive track record of artificial intelligence, its computational power pales in comparison to the human brain. Now, scientists reveal a revolutionary path to move computing forward: organoid intelligence, in which lab-grown brain organoids act as biological devices.

Artificial intelligence has always been inspired by the human brain. This approach has proven to be very successful: AI is enjoying impressive feats—from diagnosing medical conditions to composing poetry. However, the original model continues to outperform the machines in many ways. This is why, for example, we can “prove our humanity” with trivial online photo quizzes. What if we went straight to the source instead of trying to make AI more like a brain?

Scientists across multiple disciplines are working to create revolutionary biocomputers in which 3D cultures of brain cells, called brain organoids, act as biological devices. They describe their roadmap for achieving this vision in the journal frontiers in science.

Magnified image of a brain organoid grown in the laboratory with fluorescent labeling of different cell types. (pink – neurons; red – oligodendrocytes; green – astrocytes; blue – all cell nuclei). Credit: Thomas Hartung, Johns Hopkins University

“We call this new interdisciplinary field ‘organic intelligence’ (OI),” said Professor Thomas Hartung of Johns Hopkins University. “A community of leading scientists has come together to develop this technology, which we believe will launch a new era of fast, powerful and efficient biocomputing.”

What are the organelles of the brain, and why do they make good computers?

Brain organoids are a type of cell culture in the laboratory. Although organoids are not “mini-brains,” they share key aspects of brain function and structure such as neurons and other brain cells that are essential for cognitive functions such as learning and memory. Also, while most cell cultures are flat, organelles have a three-dimensional structure. This results in a 1,000-fold increase in the culture’s cell density, which means neurons can form a greater number of connections.

But even if brain organoids are such good imitators of brains, why do they make such good computers? After all, aren’t computers smarter and faster than brains?

Organic Intelligence: The New Frontier in the Biocomputing Graph. Credit: Frontiers/John Hopkins University

“While silicon-based computers are definitely better with numbers, brains are better at learning,” Hartung explained. For example, AlphaGo [the AI that beat the world’s number one Go player in 2017] It was trained on data from 160,000 games. A person would have to play five hours a day for more than 175 years to experience these many games.”

Brains are not only superior learners, they are also more energy efficient. For example, the amount of energy expended training AlphaGo is more than what is required to maintain an active adult for a decade.

“Brains also have an amazing capacity to store information, estimated at 2,500 terabytes,” Hartung added. We’ve reached the physical limits of silicon computers because we can’t pack more transistors into a tiny chip. But the brain is wired in a completely different way. It has about 100 billion neurons connected via more than 1,015 connection points. It’s a huge difference in strength compared to our current technology.”

Organic Intelligence: The New Frontier in the Biocomputing Graph. Credit: Frontiers/John Hopkins University

What would biocomputers with organic intelligence look like?

According to Hartung, existing brain organelles need to increase in size for OI. They are very small, each containing about 50,000 cells. For OI, we would need to increase that number to 10 million.”

In parallel, the authors are also developing techniques for communicating with organelles: in other words, sending them information and reading what they think. The authors plan to adapt tools from various scientific disciplines, such as bioengineering and[{” attribute=””>machine learning, as well as engineer new stimulation and recording devices.

Organoid intelligence requires diverse technologies to communicate with brain organoids infographic. Credit: Frontiers/John Hopkins University

“We developed a brain-computer interface device that is a kind of an EEG cap for organoids, which we presented in an article published last August. It is a flexible shell that is densely covered with tiny electrodes that can both pick up signals from the organoid, and transmit signals to it,” said Hartung.

The authors envision that eventually, OI would integrate a wide range of stimulation and recording tools. These will orchestrate interactions across networks of interconnected organoids that implement more complex computations.

Organoid intelligence could help prevent and treat neurological conditions

OI’s promise goes beyond computing and into medicine. Thanks to a groundbreaking technique developed by Noble Laureates John Gurdon and Shinya Yamanaka, brain organoids can be produced from adult tissues. This means that scientists can develop personalized brain organoids from skin samples of patients suffering from neural disorders, such as Alzheimer’s disease. They can then run multiple tests to investigate how genetic factors, medicines, and toxins influence these conditions.

Organoid intelligence will advance medical research and innovation infographic. Credit: Frontiers/John Hopkins University

“With OI, we could study the cognitive aspects of neurological conditions as well,” Hartung said. “For example, we could compare memory formation in organoids derived from healthy people and from Alzheimer’s patients, and try to repair relative deficits. We could also use OI to test whether certain substances, such as pesticides, cause memory or learning problems.”

Taking ethical considerations into account

Creating human brain organoids that can learn, remember, and interact with their environment raises complex ethical questions. For example, could they develop consciousness, even in a rudimentary form? Could they experience pain or suffering? And what rights would people have concerning brain organoids made from their cells?

‘Embedded ethics’ will ensure responsible development of organoid intelligence infographic. Credit: Frontiers/John Hopkins University

The authors are acutely aware of these issues. “A key part of our vision is to develop OI in an ethical and socially responsible manner,” Hartung said. “For this reason, we have partnered with ethicists from the very beginning to establish an ‘embedded ethics’ approach. All ethical issues will be continuously assessed by teams made up of scientists, ethicists, and the public, as the research evolves.”

How far are we from the first organoid intelligence?

Even though OI is still in its infancy, a recently-published study by one of the article’s co-authors – Dr. Brett Kagan of the Cortical Labs – provides proof of concept. His team showed that a normal, flat brain cell culture can learn to play the video game Pong.

“Their team is already testing this with brain organoids,” Hartung added. “And I would say that replicating this experiment with organoids already fulfills the basic definition of OI. From here on, it’s just a matter of building the community, the tools, and the technologies to realize OI’s full potential,” he concluded.

Reference: “Organoid intelligence (OI): the new frontier in biocomputing and intelligence-in-a-dish” by Lena Smirnova, Brian S. Caffo, David H. Gracias, Qi Huang, Itzy E. Morales Pantoja, Bohao Tang, Donald J. Zack, Cynthia A. Berlinicke, J. Lomax Boyd, Timothy D. Harris, Erik C. Johnson, Brett J. Kagan, Jeffrey Kahn, Alysson R. Muotri, Barton L. Paulhamus, Jens C. Schwamborn, Jesse Plotkin, Alexander S. Szalay, Joshua T. Vogelstein, Paul F. Worley and Thomas Hartung, 27 February 2023, Frontiers in Science.
DOI: 10.3389/fsci.2023.1017235