The human brain is a master of computation. It’s no wonder that from brain-inspired algorithms to neuromorphic chips which are a “brain in your pocket,” scientists are borrowing the brain’s playbook to give machines a boost.
Yet the results, in both software and hardware, only capture a fraction of the computational intricacies embedded in neurons. But perhaps the major roadblock in building brain-like computers is that we still don’t fully understand how the brain works. For example, how does its architecture, which is defined by pre-established layers, regions, and ever-changing neural circuits, make sense of our chaotic world with high efficiency and low energy usage?
The Future of AI, by keynote Matthew Griffin
So why not sidestep this conundrum and use neural tissue directly as a biocomputer?
These pea-sized nuggets roughly resemble the early foetal human brain in their gene expression, wide variety of brain cells, and organisation. Their neural circuits spark with spontaneous activity, ripple with brain waves, and can even detect light and control muscle movement.
In essence, brain organoids are highly-developed processors that emulate the brain to a limited degree. Theoretically, different types of mini-brains could be hooked up to digital sensors and output devices – not unlike brain-machine interfaces, but as a circuit outside the body. In the long term, they may connect to each other in a super biocomputer trained using biofeedback and machine learning methods to enable “intelligence in a dish.”
Sound a bit creepy? I agree. Scientists have long debated where to draw the line; that is, when the mini-brain becomes too similar to a human one, with the hypothetical nightmare scenario of the nuggets developing consciousness.
The team is well aware. As part of organoid intelligence, they highlight the need for “embedded ethics,” with a consortium of scientists, bioethicists, and the public weighing in throughout development. But to senior author Dr. Thomas Hartung, the time for launching organoid intelligence research is now.
“Biological computing could be faster, more efficient, and more powerful than silicon-based computing and AI, and only require a fraction of the energy,” the team wrote.
Using brain tissue as computational hardware may seem bizarre, but there’ve been previous pioneers. In 2022, the Australian company Cortical Labs taught hundreds of thousands of isolated neurons in a dish to play Pong inside a virtual environment. The neurons connected with silicon chips powered by deep learning algorithms into a “synthetic biological intelligence platform” that captured basic neurobiological signs of learning.
Here, the team took the idea a step further. If isolated neurons could already support a rudimentary form of biocomputing, what about 3D mini-brains?
Since their debut a decade ago, mini-brains have become darlings for examining neurodevelopmental disorders such as autism and testing new drug treatments. Often grown from a patient’s skin cells – transformed into induced pluripotent stem cells (iPSCs) – the organoids are especially powerful for mimicking a person’s genetic makeup, including their neural wiring. More recently, human organoids partially restored damaged vision in rats after integrating with their host neurons.
In other words, mini-brains are already building blocks for a plug-and-play biocomputing system that readily connects with biological brains. So why not leverage them as processors for a computer?
“The question is: can we learn from and harness the computing capacity of these organoids?” the team asked.
Last year, a group of biocomputing experts united in the first organoid intelligence workshop in an effort to form a community tackling the use and implications of mini-brains as biocomputers. The overarching theme, consolidated into “the Baltimore declaration,” was collaboration. A mini-brain system needs several components: devices to detect input, the processor, and a readable output.
In the new paper, Hartung envisions four trajectories to accelerate organoid intelligence.
The first focuses on the critical component: the mini-brain. Although densely packed with brain cells that support learning and memory, organoids are still difficult to culture on a large scale. An early key aim, explained the authors, is scaling up.
Microfluidic systems, which act as “nurseries,” also need to improve. These high-tech bubble baths provide nutrients and oxygen to keep burgeoning mini-brains alive and healthy while removing toxic waste, giving them time to mature. The same system can also pump neurotransmitters—molecules that bridge communication between neurons—into specific regions to modify their growth and behavior.
Scientists can then monitor growth trajectories using a variety of electrodes. Although most are currently tailored for 2D systems, the team and others are levelling up with 3D interfaces specifically designed for organoids, inspired by EEG caps with multiple electrodes placed in a spherical shape.
Then comes the decoding of signals. The second trajectory is all about deciphering the whens and wheres of neural activity inside the mini-brains. When zapped with certain electrical patterns—for example, those that encourage the neurons to play Pong—do they output the expected results?
It’s another hard task; learning changes neural circuits on multiple levels. So what to measure? The team suggests digging into multiple levels, including altered gene expression in neurons and how they connect into neural networks.
Here is where AI and collaboration can make a splash. Biological neural networks are noisy, so multiple trials are needed before “learning” becomes apparent – in turn generating a deluge of data. To the team, machine learning is the perfect tool to extract how different inputs, processed by the mini-brain, transform into outputs. Similar to large-scale neuroscience projects such as the BRAIN Initiative, scientists can share their organoid intelligence research in a community workspace for global collaborations.
Trajectory three is further in the future. With efficient and long-lasting mini-brains and measuring tools in hand, it’s possible to test more complex inputs and see how the stimulation feeds back into the biological processor. For example, does it make its computation more efficient? Different types of organoids—say, those that resemble the cortex and the retina—can be interconnected to build more complex forms of organoid intelligence. These could help “empirically test, explore, and further develop neurocomputational theories of intelligence,” the authors wrote.
The fourth trajectory is the one that underlines the entire project – the ethics of using mini-brains for biocomputing. As brain organoids increasingly resemble the brain – so much so that they can integrate and partially restore a rodent’s injured visual system – scientists are asking if they may gain a sort of awareness.
To be clear, there is no evidence that mini-brains are conscious. But “these concerns will mount during the development of organoid intelligence, as the organoids become structurally more complex, receive inputs, generate outputs, and – at least theoretically – process information about their environment and build a primitive memory,” the authors said. However, the goal of organoid intelligence isn’t to recreate human consciousness – rather, it’s to mimic the brain’s computational functions.
The mini-brain processor is hardly the only ethical concern. Another is cell donation. Because mini-brains retain their donor’s genetic makeup, there’s a chance of selection bias and limitation on neurodiversity.
“What does the organoid exhibit about the cell donor?” the authors asked. Will researchers have an obligation to inform the donor if they discover neurological disorders during their research?
To navigate the “truly uncharted territory,” the team proposes an embedded ethics approach. At each step, bioethicists will collaborate with research teams to map out potential issues iteratively while gathering public opinions. The strategy is similar to other controversial topics, such as genetic editing in humans. But, a mini-brain-powered computer is decades away.
“It will take decades before we achieve the goal of something comparable to any type of computer,” said Hartung. But it’s time to start – launching the program, consolidating multiple technologies across fields, and engaging in ethical discussions.
“Ultimately, we aim toward a revolution in biological computing that could overcome many of the limitations of silicon-based computing and AI and have significant implications worldwide,” the team said.
Matthew Griffin, described as “The Adviser behind the Advisers” and a “Young Kurzweil,” is the founder and CEO of the World Futures Forum and the 311 Institute, a global Futures and Deep Futures consultancy working between the dates of 2020 to 2070, and is an award winning futurist, and author of “Codex of the Future” series.
Regularly featured in the global media, including AP, BBC, Bloomberg, CNBC, Discovery, RT, Viacom, and WIRED, Matthew’s ability to identify, track, and explain the impacts of hundreds of revolutionary emerging technologies on global culture, industry and society, is unparalleled. Recognised for the past six years as one of the world’s foremost futurists, innovation and strategy experts Matthew is an international speaker who helps governments, investors, multi-nationals and regulators around the world envision, build and lead an inclusive, sustainable future.
A rare talent Matthew’s recent work includes mentoring Lunar XPrize teams, re-envisioning global education and training with the G20, and helping the world’s largest organisations envision and ideate the future of their products and services, industries, and countries.
Matthew's clients include three Prime Ministers and several governments, including the G7, Accenture, Aon, Bain & Co, BCG, Credit Suisse, Dell EMC, Dentons, Deloitte, E&Y, GEMS, Huawei, JPMorgan Chase, KPMG, Lego, McKinsey, PWC, Qualcomm, SAP, Samsung, Sopra Steria, T-Mobile, and many more.
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