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Arthur T Knackerbracket [soylentnews.org] writes:

THIS HAS BEEN PRODUCED BY SOFTWARE UNDER DEVELOPMENT - THE CONTENT WILL NEED EXTENSIVE EDITING

https://www.newscientist.com/article/2517389-human-brain-cells-on-a-chip-learned-to-play-doom-in-a-week/?utm_campaign=rss%7cnsns&utm_source=nsns&utm_medium=rss&utm_content=technology162ec25850db93c4cf36e1bb0331b252 [newscientist.com]

A clump of human brain cells [soylentnews.org] can play the classic computer game Doom. While its performance is not up to par with humans, experts say it brings biological computers a step closer to useful real-world applications, like controlling robot arms.

In 2021, the Australian company Cortical Labs [corticallabs.com] used its neuron-powered computer chips to play [soylentnews.org]Pong [soylentnews.org]. The chips consisted of clumps of more than 800,000 living brain cells grown on top of microelectrode arrays that can both send and receive electrical signals. Researchers had to carefully train the chips to control the paddles on either side of the screen.

Now, Cortical Labs has developed an interface that makes it easier to program these chips using the popular programming language Python. An independent developer, Sean Cole, then used Python to teach the chips to play [soylentnews.org]Doom [soylentnews.org], which he did in around a week.

"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," says Brett Kagan [google.com] of Cortical Labs. "It’s this accessibility and this flexibility that makes it truly exciting."

The neuronal computer chip, which used about a quarter as many neurons as the Pong demonstration, played Doom better than a randomly firing player, but far below the performance of the best human players. However, it learnt much faster than traditional, silicon-based machine learning systems and should be able to improve its performance with newer learning algorithms, says Kagan.

However, it's not useful to compare the chips with human brains, he says. "Yes, it's alive, and yes, it’s biological, but really what it is being used as is a material that can process information in very special ways that we can’t recreate in silicon."

"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," says Andrew Adamatzky [uwe.ac.uk] at the University of the West of England in Bristol, UK.

Steve Furber [google.com] at the University of Manchester, UK, agrees that Doom is a significant level up from playing Pong, but he says there is still a lot we don't understand about how these neurons are playing the game, such as how the neurons know what is expected of them or how they can "see" the screen with no eyes.

Even so, the jump in capability is exciting, says Yoshikatsu Hayashi [reading.ac.uk] at the University of Reading, UK, and brings us significantly 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 [soylentnews.org]. "[Playing Doom] is like a simpler version of controlling a whole arm," says Hayashi.

"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," says Adamatzky. "That's much closer to the kinds of challenges future biological or hybrid computers will need to handle."

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