Scientists Just Taught Lab-Grown Brain Cells to Play Video Games

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Credit: Grok AI

Credit: Grok AI

Researchers say they have successfully trained living human brain cells to play the classic video game Doom, marking a major step forward in “biological computing.”

Australian biotech company Cortical Labs revealed the development in a recent demonstration, showing lab-grown neurons interacting with the 1993 first-person shooter.

The breakthrough builds on the company’s 2022 work, when clusters of human brain cells grown in a petri dish learned to play the simpler game Pong. Those early experiments involved “mini-brains” composed of roughly 800,000 to one million living human neurons, which demonstrated the ability to adapt and learn basic tasks in real time.

Now, researchers say they have advanced the technology by teaching neurons to navigate the far more complex world of Doom, a three-dimensional game that requires movement, targeting, and exploration.https://

To achieve this, engineers converted the game’s digital signals into patterns of electrical stimulation that the neurons could interpret.

“So we showed that biological neurons could play Pong,” Cortical Labs chief scientific officer Brett Kagan said in a video announcement. “This was a massive milestone because it demonstrated adaptive, real-time, goal-directed learning.”

Doom was much more complex,” he added. “It’s 3D, it has enemies, it requires exploration, and it’s challenging.”

In the system, called the CL1 biological computer, the neurons respond to electrical signals from the computer, and their activity is translated back into actions within the game.

“If the neurons fire in a specific pattern, the Doom guy shoots,” explained Cortical Labs chief technology officer David Hogan. “If they fire in another pattern, he moves right.”

The platform allows developers to interact with the living neurons online, and researchers say the neurons are still learning. At this stage, they play the game roughly like a beginner who has never used a computer before.

Scientists believe the technology could eventually power new kinds of artificial intelligence systems or control advanced robotics.

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