@vytalow: Introducing Parasma. Previously, we trained human brain cells to play Doom. Now, we trained them to predict words in a …

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Summary

Parasma publicly launched, revealing that it trains human brain cells to perform next-token prediction, achieving 90% accuracy in word prediction tasks as an energy-efficient alternative to GPUs.

Introducing Parasma. Previously, we trained human brain cells to play Doom. Now, we trained them to predict words in a sentence. We are unlocking human brain cells for compute. Learn more here: https://parasma.com/news/human-brain-cells-do-next-token-prediction… @ParasmaAI
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Introducing Parasma. Previously, we trained human brain cells to play Doom. Now, we trained them to predict words in a sentence. We are unlocking human brain cells for compute. Learn more here: https://parasma.com/news/human-brain-cells-do-next-token-prediction… @ParasmaAI


Human brain cells do next-token prediction | Parasma

Source: https://parasma.com/news/human-brain-cells-do-next-token-prediction News13 Aug 2026

Parasma launches to train brain cells for compute

SAN FRANCISCO — August 13, 2026Parasmatoday announced its public launch. The company is developing the algorithms and infrastructure for compute powered by human brain cells as an energy efficient alternative to GPUs.

The work behind Parasma first entered public view earlier this year. In a project unveiled in late February, founder Sean Cole collaborated with Cortical Labs to train a culture of human neurons to playDoom.

Token prediction

The architecture converts context words into electrical stimulation delivered to the cells. Their responses are measured and decoded directly into tokens, then the system feeds back whether each prediction was correct.

Parasma tested whether human brain cells can solve precisely defined token prediction tasks. The cells correctly predicted90% of next-token decisionswhile taking in the full sentence context. Randomized control runs scored significantly worse, with all results consistent with chance.

Parasma also ran a harder, non-linear token prediction experiment where no single context token alone can determine the answer. In one isolated run, the cells achieved78.1% accuracy, exceeding the75% ceilingthat a silicon linear decoder could achieve.

“Crossing this boundary that a linear silicon decoder cannot represent is exactly the kind of result we built Parasma to pursue,” saidSean Cole, founder of Parasma. “It is an early step toward understanding where living neurons can add computational capabilities, particularly in places like language modelling.”

These results suggest that custom architectures built for biological computing can support language-based token prediction.

What this could enable

In the near term, Parasma is building toward repeatable benchmarks across cultures, devices, and time to scale biology with advances in AI. The company will also investigate richer contextual prediction and adaptive-control tasks.

Over the longer term, programmable biological compute would replace silicon in systems that benefit from continual learning or extreme energy and sample efficiency, giving rise to new model paradigms. Parasma’s goal is to develop the algorithms that make biological computing useful as an alternative to GPUs.

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