PERSPECTIVE article
Front. Comput. Neurosci.
From generative AI to the brain: five takeaways
Provisionally accepted
- Physics, Goethe University Frankfurt, Frankfurt, Germany
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The big strides seen in generative AI are not based on somewhat obscure algorithms, but due to clearly defined generative principles. The resulting concrete implementations have proven themselves in large numbers of applications. We suggest that it is imperative to thoroughly investigate which of these generative principles may be operative also in the brain, and hence relevant for cognitive neuroscience. In addition, ML research led to a range of interesting characterizations of neural information processing systems. We discuss five examples, the shortcomings of world modelling, the generation of thought processes, attention, neural scaling laws, and quantization, that illustrate how much neuroscience could potentially learn from ML research.
Keywords: Generative AI, Cognitive neuroscience, Attention, predictive coding, Chain of Thought, quantization
Received: 04 Oct 2025; Accepted: 10 Nov 2025.
Copyright: © 2025 Gros. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Claudius Gros, gros07@itp.uni-frankfurt.de
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