In a paper presented in November 2025 at the Empirical Methods in Natural Language Processing (EMNLP) conference, researchers at the Swiss Federal Institute of Technology (EPFL), the Massachusetts Institute of Technology (MIT), and Georgia Tech revisited earlier findings that showed that language models, the engines of commercial AI chatbots, show strong signal correlations with the human language network, the region of the brain responsible for processing language.
In their new results, they found that signal correlations between model and brain region change significantly over the course of the 'training' process, where models are taught to autocomplete as many as trillions of elided words (or sub-words, known as tokens) from text passages.
The correlations between the signals in the model and the signals in the language network reach their highest levels relatively early on in training. While further training continues to improve the functional performance of the models, it does not increase the correlations with the language network.
The results lend clarity to the surprising picture that has been emerging from the last decade of neuroscience research: That AI programs can show strong resemblances to large-scale brain regions—performing similar functions, and doing so using highly similar signal patterns.
Such resemblances have been exploited by neuroscientists to make much better models of cortical regions. Perhaps more importantly, the links between AI and cortex provide an interpretation of commercial AI technology as being profoundly brain-like, validating both its capabilities as well as the risks it might pose for society as the first ever synthetic braintech.
"It is something we, as a community, need to think about a lot more," said Badr AlKhamissi, doctoral student in neuroscience at EPFL and first author of the preprint, in an interview with Foom. "These models are getting better and better every day. And their similarity to the brain [or brain regions] is also getting better—probably. We're not 100% sure about it."
In a paper presented in November 2025 at the Empirical Methods in Natural Language Processing (EMNLP) conference, researchers at the Swiss Federal Institute of Technology (EPFL), the Massachusetts Institute of Technology (MIT), and Georgia Tech revisited earlier findings that showed that language models, the engines of commercial AI chatbots, show strong signal correlations with the human language network, the region of the brain responsible for processing language.
In their new results, they found that signal correlations between model and brain region change significantly over the course of the 'training' process, where models are taught to autocomplete as many as trillions of elided words (or sub-words, known as tokens) from text passages.
The correlations between the signals in the model and the signals in the language network reach their highest levels relatively early on in training. While further training continues to improve the functional performance of the models, it does not increase the correlations with the language network.
The results lend clarity to the surprising picture that has been emerging from the last decade of neuroscience research: That AI programs can show strong resemblances to large-scale brain regions—performing similar functions, and doing so using highly similar signal patterns.
Such resemblances have been exploited by neuroscientists to make much better models of cortical regions. Perhaps more importantly, the links between AI and cortex provide an interpretation of commercial AI technology as being profoundly brain-like, validating both its capabilities as well as the risks it might pose for society as the first ever synthetic braintech.
"It is something we, as a community, need to think about a lot more," said Badr AlKhamissi, doctoral student in neuroscience at EPFL and first author of the preprint, in an interview with Foom. "These models are getting better and better every day. And their similarity to the brain [or brain regions] is also getting better—probably. We're not 100% sure about it."
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