Neuron-Powered Chips Learn Doom, Advance Biological Computing

Human brain cells grown on computer chips have successfully learned to play the classic first-person shooter game, Doom. This achievement brings biological computers closer to practical applications, such as controlling robotic arms.

Credit: Cortical Labs

Australian organisation Cortical Labs previously used neuron-powered chips to play Pong in 2021. Those chips consisted of clumps of more than 800,000 living brain cells grown on top of microelectrode arrays capable of both sending and receiving electrical signals. Researchers painstakingly trained the chips to control the game's paddles.

Cortical Labs has since developed an interface simplifying the programming of these chips using Python, a popular language. Independent developer Sean Cole then used Python to teach the chips to play Doom, accomplishing this in approximately one week.

Brett Kagan of Cortical Labs stated, "Unlike the Pong work that we did a few years ago, which represented years of painstaking scientific effort, this demonstration has been done in a matter of days by someone who previously had relatively little expertise working directly with biology." He added, "It’s this accessibility and this flexibility that makes it truly exciting."

The neuronal computer chip used about a quarter of the neurons compared to the Pong demonstration. While it played Doom better than a randomly firing player, its performance remained far below that of top human players.

The chip learned significantly faster than traditional, silicon-based machine learning systems and is expected to improve with newer learning algorithms, according to Kagan. He also noted that comparing these chips to human brains is not useful, explaining that the biological material processes information in ways silicon cannot replicate.

Andrew Adamatzky, from the University of the West of England in Bristol, UK, commented, "Doom is vastly more complex than earlier demonstrations, and successfully interacting with it highlights real advances in how living neural systems can be controlled and trained."

Steve Furber, from the University of Manchester, UK, agreed that Doom represents a significant step up from Pong. However, he highlighted that much remains unknown about how these neurons play the game, including how they understand expectations or "see" the screen without eyes.

Yoshikatsu Hayashi, from the University of Reading, UK, called the jump in capability exciting, bringing biological computers closer to useful real-world applications, such as controlling a robotic arm with biological computers, a task which Hayashi and his colleagues are attempting with a similar computer made from jelly-like hydrogel. Hayashi and his colleagues are attempting a similar task with a computer made from a jelly-like hydrogel. He stated, "[Playing Doom] is like a simpler version of controlling a whole arm."

Adamatzky further emphasised, "What’s exciting here is not just that a biological system can play Doom, but that it can cope with complexity, uncertainty, and real-time decision-making." He added that this is "much closer to the kinds of challenges future biological or hybrid computers will need to handle."

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• Human brain cells on a chip successfully learned to play Doom.

• Cortical Labs developed an interface, allowing independent developer Sean Cole to program the chips using Python in approximately one week.

• The chip's performance surpassed random players and learned faster than silicon-based systems, despite not matching human players.

Source: NEW SCIENTIST