A new approach to computing is taking shape as Cortical Labs experiments with data centers powered partly by lab-grown human neurons. The company has opened its first facility in Melbourne and is planning a larger site in Singapore, aiming to test whether biological systems can complement traditional silicon-based computing.
Instead of relying only on conventional servers, these facilities use the company’s CL1 systems, which combine living human neurons with electronic components. The goal is not to replace silicon chips but to explore how biological systems can support specific computing tasks.
The concept builds on how neurons naturally process information. Unlike traditional chips that follow fixed instructions, neurons form connections that change over time, allowing them to learn and adapt based on feedback.
In earlier research published in the Neuron journal, Cortical Labs demonstrated that lab-grown neurons could learn to play a simplified version of the game Pong. More recent experiments have shown similar systems interacting with simplified versions of Doom, using feedback loops to guide behavior.
Each CL1 system contains around 200,000 human neurons grown from stem cells on a silicon chip. These neurons are connected through a microelectrode array, which allows electrical signals to be sent and recorded in real time. A supporting life system maintains nutrients, temperature, and stability, while software translates biological activity into usable digital outputs.
This approach is often described as Reservoir Computing, where a dynamic system processes inputs into complex patterns that software can interpret.
Interest in this technology is growing alongside the rise of artificial intelligence, which demands large amounts of computing power and energy. Biological systems offer a different model. The human brain operates on about 20 watts of power while handling complex tasks such as learning and pattern recognition.
However, the technology is still at an early stage. Current systems are small, limited, and far from competing with traditional processors used in large-scale AI workloads. Facilities today operate at a small scale compared to hyperscale data centers run by companies like Amazon, Microsoft, and Google.
Experts also highlight challenges around scalability, reliability, and control. Biological systems require strict environmental conditions and can behave unpredictably. Ethical considerations are also emerging as the technology develops.
As Steve Furber noted, “Despite huge progress in neuroscience over the last century, the fundamental principles of information processing and storage in the brain are far from understood… We are still some way from identifying an optimal approach that is as general-purpose in this domain as is the general-purpose programmable processor in the conventional computing domain.”
While still experimental, the effort reflects a broader shift in computing research. As traditional chip design faces limits, alternatives such as biological systems and hybrid models are being explored.
For now, these systems remain early-stage and constrained. But they offer a glimpse into a future where living systems could play a role in how machines process information.
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