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National Geographic
6 days ago
- Science
- National Geographic
Scientists want to build 'living' computers—powered by live brain cells
The potential for these kinds of machines to reshape computer processing, increase energy efficiency, and revolutionize medical testing has scientists excited. But when do we consider these cells to be conscious? Scientists are experimenting with ways to integrate brain cells into computer processors. The technology could help conserve energy. FinalSpark's Multi-Electrode Arrays (MEAs) incorporate four human brain organoids, each interfacing with eight electrodes. Photograph Courtesy FinalSpark In 2022, a group of Australian researchers ran a rudimentary simulation of the arcade game, Pong. None of them were controlling the virtual paddle, and yet, after a few misses, the paddle moved up and down the screen on its own to meet the returning ball and hit it back. The 2D game was wired to a cluster of lab-grown human and mouse brain cells growing on a petri dish. Through a multi-electrode array, the researchers taught the 'mini-brain' where the ball was and rewarded it with electrical stimulation when there was a hit. In about five minutes, the cells learned the drill and played short rallies without human intervention. 'The recent success of LLMs [Large Language Models] has risen from trying to model processes that happen in the brain,' says Brett Kagan, the chief scientific officer at Cortical Labs, a startup spun out of the Pong research. 'I often like to say, 'any sufficiently advanced machine is indistinguishable from biology', so what if we used biology in our attempts to harness intelligence?' The Pong experiment proved neurons can learn and respond to feedback in real time, even in a petri dish, says Lena Smirnova, assistant professor at the Johns Hopkins Bloomberg School of Public Health. A year later in 2023, Smirnova, alongside other researchers, laid out the vision for 'organoid intelligence,' an emerging scientific field that leverages the strengths of living human and animal brain cell cultures — learning from fewer examples, adapting in real-time, and efficiently using energy—as a new type of biological computer. Using brain cells as the processing hub of a computer has far-reaching implications. It could significantly reduce the amount of energy needed to power artificial intelligence and revolutionize medicine. The technology is already creating a lucrative new industry that scientists are leveraging for major breakthroughs. But with this booming sector come complicated questions about when consciousness begins and the ethical implications of using living tissue that can feel pain. A forebrain organoid as seen under a scanning electron microscope. This clump of brain matter, developed from Human iPSC-derived Neural Stem Cells, is at the heart of new research on "living computers." Micrograph Courtesy FinalSpark The devices we use today, from computers to phones, run on chips, where billions of little components called transistors are neatly etched in silicon and arranged into logic gates. Each chip can take up to a couple of bits as input and then shuttle forward a single-bit output. Combining numerous such gates makes it possible to execute complex operations, such as those used in modern AI chatbots. However, units of brain organoids, known as bioprocessors, function in tandem with a traditional silicon chip. Inside each organoid, endless neurons grow in three dimensions, forming connections through synapses. Because there's no fixed wiring to limit them, the network constantly self-organizes and evolves as it learns. Neurons can simultaneously transfer information by electrical pulses and chemical signals, as opposed to the rigid, step-by-step logic of a normal computer. 'It's more like an ever-adapting web than a tidy circuit board,' adds Smirnova. Not only is the human brain naturally adaptive, but it's also incredibly energy efficient. Training a Generative AI model like OpenAI's GPT-3, for example, is estimated to consume just under 1,300 megawatt-hours (MWh) of electricity, as much power as used by 130 U.S. homes. The brain needs a fraction of that and requires no more energy than a common lightbulb to perform a comparable task. Data from the Johns Hopkins research suggests biocomputing could cut down AI energy consumption by "1 million to 10 billion times.' 'The development of large organoids for power-efficient neural networks could help with running complex deep learning models without significantly impacting climate change,' Ben Ward-Cherrier, a computational neuroscience researcher at the University of Bristol, told National Geographic. How bioprocessers are already being used It's no longer an experimental pipedream either: a cottage industry of startups has raced to commercially build what some colloquially call a 'living computer.' Swiss-based FinalSpark's Neuroplatform, for example, lets anyone remotely run experiments on a cluster of organoids for $1,000 per month. Its facility incubates thousands of processing units, where each organoid is connected to eight electrodes plugged into a conventional computer. Using FinalSpark's software, researchers can code programs to electrically stimulate the neurons, monitor their response, and expose them to the feel-good neurotransmitters dopamine and serotonin to train them to perform computing tasks. In addition to renting out its biological computers over the cloud, Cortical Labs also began selling its bioprocessing units earlier this year for $35,000 each. The units look like devices out of a sci-fi movie: a large glass and metal container houses all the support systems—from waste filtration to temperature control—needed to keep human brain cells alive for up to six months. Over the last couple of years, researchers have taken advantage of these privately-run biological computers to test breakthroughs. The University of Bristol's Ward-Cherrier, for instance, integrates organoids into robots as their 'brain,' so they can learn on the go. His team used Neuroplatform's organoids to develop a system that reads Braille characters at 83 percent accuracy. Each letter's spatial information is encoded into specific electrical pulses, which the neurons can identify. Soon, Ward-Cherrier's team plans to use organoids to teach robots to execute motor commands based on specific events and situations, such as feeling an object and following its contour with a robotic arm. The skill could one day help a robot understand what it's interacting with. For now, living brain-cell computers are far from replacing your laptop's processor. For one, the brain cells deployed in computer circuits are in their infancy and remain immature—fetal-like in both biological structure and behavior. They lack the structured architecture of a mature human brain, which prevents them from performing advanced cognitive feats. In their current state, organoids can be taught in simpler ways, such as learning rudimentary tasks when stimulated and exhibiting rudimentary memory functions. Plus, no two organoids behave the same way, and keeping them alive for extended periods remains a challenge. Smirnova agrees that cellular computers aren't close to the level of reliability or scale needed for mainstream computing tasks. Yet being immature allows these networks to be flexible, which is ideal for research. A safer and more humane way to test drugs For the foreseeable future, Smirnova says, she and her research team will continue using organoids to better understand and treat neurological conditions. While organoids may not be advanced enough to compute complex information, they're becoming a more feasible and humane way to test drugs. Researchers may soon be able to grow an organoid from a patient's stem cells and test how a particular drug affects their specific neurons or screen a library of chemicals to check for neurotoxic effects—all without involving animals. Kyle Wedgwood, a professor at the University of Exeter's Living Systems Institute is doing just that. He's leveraging FinalSpark's Neuroplatform to figure out ways to restore the brain's memory after it's disrupted by diseases such as Alzheimer's. 'This work will lay down foundations for smart, implantable biotechnology to help mitigate neurodegenerative conditions,' adds Wedgwood. When do organoids become organs? As these lab-grown 'mini-brains' become more complex, scientists are also probing questions about when they enter the realm of consciousness and the ethics of activating their pain receptors. Smirnova isn't waiting for an organoid to show even a hint of consciousness and has begun work to place frameworks—similar to those enforced in animal research—in place, with review boards and protocols to prevent suffering. In practice, this could mean setting limits on an organoid's age, what kind of experiments can be conducted on them, how cells are sourced and produced, and in case they come from a human, using them more responsibly and with donor consent. 'The bottom line is that we're proceeding with a lot of care and thoughtfulness, well before anything like a 'sentient' brain tissue could ever become a reality,' adds Smirnova.
Business Times
05-07-2025
- Science
- Business Times
Brain cells on silicon chips: The rise of ‘biological computers'
[SINGAPORE] The brain is full of unsolved mysteries. One startup thinks that it can crack these puzzles – and even open up new possibilities in computing – by fusing brain cells with silicon. In March, Australian startup Cortical Labs unveiled what it says is the first commercial 'biological computer'. Called the CL1, the device integrates lab-grown brain cells – derived from human stem cells – with hard silicon. The CL1 can be used for drug discovery, disease modelling and research into neuroscience and information systems, Cortical Labs' chief scientific officer, Dr Brett Kagan, told me over a Zoom call. The CL1 fuses lab-grown brain cells with hard silicon. PHOTO: CORTICAL LABS With its combination of brain cells and silicon, the CL1 is meant to be able to adapt and learn faster than purely silicon-based artificial intelligence (AI). Dr Kagan said: 'Cortical Labs was started with the question in mind: 'What if we use the most powerful information processor that we currently know of?' And that, ultimately, is brain cells. Whether it's flies, cats or us, we all can do amazing things – with very little power, very little data – using brain cells.' Indeed, I was surprised to learn that the brain uses just 20 watts of power, equivalent to what a light bulb would require. BT in your inbox Start and end each day with the latest news stories and analyses delivered straight to your inbox. Sign Up Sign Up In contrast, training an AI model, such as the GPT-3 large language model, guzzles as much as 1,300 megawatt hours of electricity – enough to power about 130 homes in the US for a year. The CL1's launch follows a widely publicised paper published in the journal Neuron in 2022, which gave an account of the Cortical Labs team's efforts to train brain cells in a dish to play the arcade game Pong. 'That paper was a proof of concept to see if you could get brain cells in a dish… to process information and do something in a goal-orientated way – in this case, control a paddle to 'hit' a ball,' said Dr Kagan. 'While we weren't that surprised to see that cells could learn and respond… the speed at which they learnt and responded was surprising. We expected it would take much longer to see some meaningful learning, but in fact, it was within minutes.' Untangling a paradox Dr Kagan is excited, not just about the current applications of the CL1, but what breakthroughs it could lead to. In the near future, applications could include personalised medicine – through which scientists could grow a person's cells in a lab and test drugs on them while measuring the cells' response, enabling the treatment to be tailored to that individual. Dr Kagan believes the use of biological computers for personalised medicine could be just a few years away with proper investment, given that the technical barriers are not high. In the long term, a biological computer could be used in fields such as robotics, cybersecurity or even systems with 'generalised intelligence', or in the human-like ability to solve general problems, surpassing current AI systems. 'I think the most exciting applications may be ones that I'm not even going to be able to tell you today,' the chief scientific officer said. Of course, there are a host of ethical issues that come with such work. For instance, some would be concerned if a biological computer would be able to feel pain. 'No, the system can't feel pain – it doesn't have pain receptors (and) it's not set up to feel pain,' Dr Kagan said, adding that the company has been working with bioethicists on how such technology should move forward. He noted that scientists have grown brain cells in labs for decades, but previously had no means to test the information-processing ability of these cells. What devices like the CL1 do is introduce new possibilities. Dr Kagan references a famous quote by the American scientist Emerson Pugh: 'If the brain was so simple that we could understand it, we would be so simple that we couldn't.' Pointing to Cortical Labs' work, he added: 'What we're building here might be a way to overcome that paradox.' The rise of biological computers is just one of many weird and wonderful phenomena out there. This column routes signals away from the motherboard of regular news and into peripheral curiosities, whether in finance, economics, science, psychology, or even beyond.
Yahoo
03-07-2025
- Science
- Yahoo
You Can Now Rent a Flesh Computer Grown in a British Lab
The world's first commercial hybrid of silicon circuitry and human brain cells will soon be available for rent. Marketed for its vast potential in medical research, the biological machine, grown inside a British laboratory, builds on the Pong-playing prototype, DishBrain. Each CL1 computer is formed of 800,000 neurons grown across a silicon chip, and their life-support system. While it can't yet match the mind-blowing capabilities of today's most powerful computers, the system has one very significant advantage: it only consumes a fraction of the energy of comparable technologies. AI centers now consume countries' worth of energy, whereas a rack of CL1 machines only uses 1,000 watts and is naturally capable of adapting and learning in real time. "The neuron is self-programming, infinitely flexible, and the result of four billion years of evolution. What digital AI models spend tremendous resources trying to emulate, we begin with," Australian biotech startup Cortical Labs claims on its website. They teamed up with UK company to further develop DishBrain, an experimental platform designed to explore the "wetware" concept. Related: When neuroscientist Brett Kagan and colleagues pitted their creation against equivalent levels of machine learning algorithms, the cell culture systems outperformed them. Users can send code directly into the synthetically supported system of neurons, which is capable of responding to electrical signals almost instantly. These signals act as bits of information that can be read and acted on by the cells. But perhaps the greatest potential for this biological and synthetic hybrid is as an experimental tool for learning more about our own brains and their abilities, from neuroscience to creativity. "Epileptic cells can't learn to play games very well, but if you apply antiepileptics to the cell culture, they can suddenly learn better as well as a range of other previously inaccessible metrics," Kagan told Shannon Cuthrell at IEEE's Spectrum, pointing out the system's ethical drug testing capacity. The computing neurons are grown from skin and blood samples provided by adult human donors. While there are still many limitations – for one, the neurons only survive for six months at a time – the energy-saving potential of this technology alone suggests such systems are worth developing further. Especially given the dire state of our own life support system. The first CL1 units will reportedly ship soon for US$35,000 each, or remote access can apparently be rented for $300 per week. This Strange 'Bubble Wrap' Can Produce Drinking Water in The Desert Disturbing Signs of AI Threatening People Spark Concern Scientists Figured Out How to Extract Gold From Old Phones And Laptops
Yahoo
09-03-2025
- Science
- Yahoo
The world's first 'body in a box' biological computer costs $35,000 and looks both cool as hell plus creepy as heck
When you buy through links on our articles, Future and its syndication partners may earn a commission. Here's one for you: when is a 'body in a box' not as macabre as it sounds? Simple—when it's a tech startup. Wait! Put the turn-of-the-millennium trench coat and sunglasses combo down! Let me explain. The CL1 is described as "the world's first code deployable biological computer" according to the splashy website, incorporating human brain cells in order to send and receive electrical signals (via The Independent). These cells hang out on the surface of the computer's silicon chip, and the machine's Biological Intelligence Operating System (or biOS for short—cute), allows users to wrangle the neurons for a variety of computing tasks. Organic hardware like this for research purposes isn't new—for just one example, FinalSpark's Neuroplatform began offering rentable 'minibrains' last year. The neurons central to the CL1 are lab-grown, cultivated inside a nutrient rich solution and then kept alive thanks to a tightly temperature controlled environment working alongside an internal life support system. Under favourable conditions, the cells can survive for up to six months. Hence, the project's chief scientific officer Brett Kagan pitching it "like a body in a box." Should you be so inclined to pick up your own surprisingly fleshy, short-lived computer, you can do so from June…for $35,000. Now, I know what you're thinking—not because you're actually living life in a Matrix-style pod, but purely because I'm asking the same question: Why? First, a smidge more background on this brain box, which is the latest project from Cortical Labs, and was unveiled this week at Mobile World Congress in Barcelona. We've covered this Melbourne-based company before, with highlights including that time their team coaxed brain cells in a petri dish to learn Pong faster than AI. That lattermost experiment is the CL1's great grandparent, with continued scientific interest fostered by the hope that 'wetware' like lab-grown brain cells could give robotics and AI a serious leg-up. Whereas traditional AI can play something like the theatre kid favourite of 'yes, and' but totally lacks any true understanding of context, the lab-grown neurons could potentially learn and adapt. Furthermore, the lab-grown cells are apparently much more energy efficient compared to the power demands of AI using more traditional, non-biological computers. Turns out the old noggin cells are still showing that new-fangled silicon a trick or two. Who would have thought? However, there's no avoiding the question of ethics: what are these brain cells experiencing, and is it anything like sentience—or suffering? Perhaps my questions verge on the hyperbolic, but my own osseous brain box can do nothing but wonder. Best gaming PC: The top pre-built gaming laptop: Great devices for mobile gaming.
Yahoo
09-03-2025
- Science
- Yahoo
Weird New Computer Runs AI on Captive Human Brain Cells
Australian startup Cortical Labs has launched what it's calling the "world's first code deployable biological computer." The shoe box-sized device, dubbed CL1, is a notable departure from a conventional computer, and uses human brain cells to run fluid neural networks. In 2022, Cortical Labs made a big splash after teaching human brain cells in a petri dish how to play the video game "Pong." The CL1, however, is a fundamentally different approach, as New Atlas reports. It makes use of hundreds of thousands of tiny neurons, roughly the size of an ant brain each, which are cultivated inside a "nutrient rich solution" and spread out across a silicon chip, according to the company's website. Through a combination of "hard silicon and soft tissue," the company claims that owners can "deploy code directly to the real neurons" to "solve today's most difficult challenges." "A simple way to describe it would be like a body in a box, but it has filtration for waves, it has where the media is stored, it has pumps to keep everything circulating, gas mixing, and of course temperature control," Cortical Labs chief science officer Brett Kagan told New Atlas late last year. Whether it will actually prove useful remains to be seen, but Kagan is excited for scientists to get their hands on the tech. "There's so many different options," he told Australian broadcaster ABC News, suggesting it could be used for "disease modelling, or drug testing." "The large majority of drugs for neurological and psychiatric diseases that enter clinical trial testing fail, because there's so much more nuance when it comes to the brain — but you can actually see that nuance when you test with these tools," Kagan told New Atlas. "Our hope is that we're able to replace significant areas of animal testing with this." For now, the company is selling the device as a way to train "biological AI," meaning neural networks that rely on actual neurons. In other words, the neurons can be "taught" via the silicon chip. "The only thing that has 'generalized intelligence'... are biological brains," Kagan told ABC. "What humans, mice, cats and birds can do [that AI can't] is infer from very small amounts of data and then make complex decisions." But the CL1 isn't about to disrupt the entire AI field overnight. "We're not here to try and replace the things that the current AI methods do well," Kagan added. Nonetheless, the approach could have some key advantages. For instance, the neurons only use a few watts of power, compared to infamously power-hungry AI chips that require orders of magnitude more than that. Apart from selling the CL1, Cortical Labs is also looking to sell compute via the cloud, using its own assembled racks of the unusual computers. In short, while it sounds like an exciting new take on conventional computers, Cortical Labs still has a lot to prove, especially when it comes to teaching neurons not unlike an AI. "I know where it's coming from, because it is clear that these human neuronal networks learn remarkably fast," University of Queensland biologist and stem cell research specialist Ernst Wolvetang told ABC. "At this stage I would like to reserve my judgement, because, learning Pong is one thing, but making complex decisions is another," he added. More on Cortical Labs: Researchers Teach Human Brain Cells in a Dish to Play "Pong"
