Human brain cells on chip learn to play Doom in a week

An Australian company has enabled a chip with human brain cells to play the video game Doom using a simple programming interface. Developed by Cortical Labs, the technology allows for quick training and marks progress toward practical biological computing applications. Experts highlight its potential for handling complex tasks like robotic control.

Cortical Labs, an Australian firm, has advanced its neuron-powered computer chips, allowing a clump of human brain cells to play the classic first-person shooter Doom. The chip, featuring living neurons grown on microelectrode arrays, performed better than random inputs but lagged behind skilled human players. This development builds on the company's 2021 achievement, when chips with over 800,000 brain cells were trained over years to play Pong.

The new system uses an interface compatible with the Python programming language, simplifying the process. Independent developer Sean Cole trained the chip to play Doom in about 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," said Brett Kagan of Cortical Labs. "It’s this accessibility and this flexibility that makes it truly exciting."

This latest chip employed roughly a quarter of the neurons used in the Pong setup and learned faster than traditional silicon-based machine learning models. Kagan noted that such biological systems serve as unique materials for information processing, distinct from human brains. "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."

Experts praised the leap from Pong to Doom. Andrew Adamatzky of the University of the West of England in Bristol, UK, stated, "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 of the University of Manchester, UK, called it a significant upgrade, though questions remain about how the neurons process visual inputs without eyes or understand game objectives.

Yoshikatsu Hayashi of the University of Reading, UK, who works on similar hydrogel-based computers for robotic arms, sees parallels. "[Playing Doom] is like a simpler version of controlling a whole arm," he said. Adamatzky added, "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." This suggests closer alignment with future hybrid computing needs, such as robot control.

Makala yanayohusiana

Researchers observing a detailed mouse cortex simulation on Japan's Fugaku supercomputer, with a colorful 3D brain model on screen.
Picha iliyoundwa na AI

Researchers run detailed mouse cortex simulation on Japan’s Fugaku supercomputer

Imeripotiwa na AI Picha iliyoundwa na AI Imethibitishwa ukweli

Scientists from the Allen Institute and Japan’s University of Electro-Communications have built one of the most detailed virtual models of the mouse cortex to date, simulating roughly 9 million neurons and 26 billion synapses across 86 regions on the Fugaku supercomputer.

Scientists are on the verge of simulating a human brain using the world's most powerful supercomputers, aiming to unlock secrets of brain function. Led by researchers at Germany's Jülich Research Centre, the project leverages the JUPITER supercomputer to model 20 billion neurons. This breakthrough could enable testing of theories on memory and drug effects that smaller models cannot achieve.

Imeripotiwa na AI

Researchers at Nagoya University in Japan have developed miniature brain models using stem cells to study interactions between the thalamus and cortex. Their work reveals the thalamus's key role in maturing cortical neural networks. The findings could advance research into neurological disorders like autism.

A review article by Borjan Milinkovic and Jaan Aru argues that treating the mind as software running on interchangeable hardware is a poor fit for how brains actually compute. The authors propose “biological computationalism,” a framework that ties cognition and (potentially) consciousness to computation that is hybrid, multi-scale, and shaped by energy constraints.

Imeripotiwa na AI Imethibitishwa ukweli

Researchers at the University of California, Irvine report that a machine-learning system called SIGNET can infer cause-and-effect links between genes in human brain tissue, revealing extensive rewiring of gene regulation—especially in excitatory neurons—in Alzheimer’s disease.

Researchers at Texas A&M University say they have developed a customizable “vessel-chip” that recreates the complex shapes of human blood vessels—including branches, aneurysm-like bulges and stenosis-like narrowings—so scientists can study how altered blood flow affects endothelial cells and evaluate potential treatments without relying on animal models.

Imeripotiwa na AI

Researchers have engineered a protein that detects subtle glutamate signals between neurons, unveiling a previously hidden aspect of brain communication. This tool allows real-time observation of how brain cells process incoming information, potentially advancing studies on learning, memory, and neurological disorders. The findings, published in Nature Methods, highlight a breakthrough in neuroscience.

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