A biocomputer powered by lab-grown human brain cells has leveled up from Pong to Doom. While nowhere ready to handle the video game shooter’s most challenging levels, researchers at Cortical Labs in Australia believe their neuronal chip is well on its way to powering a new generation of hybrid organic technologies.
“This was a major milestone, because it demonstrated adaptive, real-time goal directed learning,” Brett Kagan, Cortical Labs Chief Scientific and Chief Operations Officer, said in a recent video announcement.
It’s taken years to cross the Doom benchmark. In 2021, Cortical Labs debuted DishBrain—an early biocomputer utilizing around 800,000 human nerve cells. These neurons were connected to a small processing chip capable of interpreting and directing electrical activity similar to a standard silicon-powered device.
Living Human Brain Cells Play DOOM on a CL1
To showcase DishBrain’s potential, engineers successfully trained their biocomputer to play Pong. The classic, 2D game is often a test case for computational neuroscientists because it requires their system to navigate a dynamic information landscape in real time.
It took Cortical Labs more than 18 months using its original hardware and software to accomplish their Pong goal. DishBrain was eventually supplanted by CL1, which the company bills as the “world’s first code deployable biological computer.”
But for a biocomputer to be actually useful, it’s going to need to do much more than move a pixelated paddle up and down on a screen. Enter Doom. For decades, major tech companies and DIY hobbyists have demonstrated ways to run the video game on all types of devices including calculators, tractors, and even ATM machines. “Can it play Doom?” is such a ubiquitous request in the tech world that it wasn’t a question of “if” Cortical Labs would try it on neuronal chips, but “when.”
The major challenge for CL1 to understand Doom is that it needed to “see” what a human player sees when playing the game on a computer. Without any optical input, this meant that engineers needed to figure out a way to convert visual information into electrical stimulation patterns that are recognizable to the neurons.
The solution wasn’t only achievable,it was completed in about a week by Sean Cole, an independent developer with little experience in biological computing. The key to this is the CL1’s new interface, which allows anyone to program it using Python.
Don’t expect the biocomputer to win any Doom tournaments, however. It plays the game better than a system that simply fires randomly at enemies, but it still loses a lot of the time. That said, Cortical Labs says it reached its current performance level faster than silicon-based machine learning systems, and will likely get better as its algorithms improve.
Beyond gunning through pixelated enemies, future generations of biocomputers may one day power robotic arms or help run complex digital programs. It’s got a long way to go, but surpassing rites of passage like playing Doom bodes well for the technology.
