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Human Brain Cells Learn to Play Doom on Biological Computer
Cortical Labs' CL1 system uses 200,000 living human neurons to adaptively play Doom, showcasing synthetic biological intelligence and real-time learning in a commercial neural computing platform.
- In a YouTube announcement, Cortical Labs said it demonstrated living human neurons playing Freedoom on the CL1 system and posted the demo and open-source code to YouTube and GitHub.
- Building on neuron‑Pong demonstrations years ago with clusters of 800,000 to one million cells, Cortical Labs chose Doom as a practical, symbolic benchmark, completing it far faster than the 18 months for Pong.
- Using a mapped stimulus‑response loop, the CL1 maps on‑screen elements to electrical stimuli that provoke neuronal spikes in 200,000 human neurons on a 59-electrode array managed by biOS, with Sean Cole training the neurons via Cortical Cloud and Python API in less than a week.
- While performance trails humans, the system learns faster than traditional silicon-based machine learning and can improve with newer algorithms, experts say.
- Under current lab conditions, the CL1 ships as a desktop or 30‑unit rack module, with each module costing around 1,000 dollars and consuming 850 to 1,000 watts per rack.
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The computer created by Cortical Labs demonstrates the almost infinite capabilities of this type of neural networks
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Scientists Train Lab-Grown Human Brain Cells To Play Doom
ZeroHedge - On a long enough timeline, the survival rate for everyone drops to zero
·United States
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Scientists Taught a Clump of Human Brain Cells to Play Doom
The internet has a long, rich history of getting the classic video game Doom to play on a wide variety of things, from pregnancy tests to a classic TI-84+ calculator that itself was powered by potatoes. It’s an engineering challenge where people get to demonstrate they can accomplish incredibly stupid things as long as they […]
·New York, United States
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Coverage Details
Total News Sources59
Leaning Left7Leaning Right9Center8Last UpdatedBias Distribution38% Right
Bias Distribution
- 38% of the sources lean Right
38% Right
L 29%
C 33%
R 38%
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